Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. (...) It has also provided significant increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Precision agriculture is a breakthrough in digital farming technology, which facilitates the application of precise and exact amount of input level of water and fertilizer to the crop at the required time for increasing the yield. Since agriculture relies on direct rainfall than irrigation and the prediction of rainfall date is easily available from web source, the integration of rainfall prediction with precision agriculture helps to regulate the water consumption in farms. In this work, an edge computing model (...) is developed for predicting soil moisture in real time and managing the water usage in accordance with rain prediction. A soil moisture prediction hybrid algorithm has been developed that revolves around the decision-making techniques with live environmental parameters including weather parameters for the prediction of soil moisture through the impact of precipitation. Numerous algorithms with the combination of regression + clustering are estimated, and it is inferred that XGBoost + k-means outperforms other algorithmic combinations that is deployed in edge model. This model is used as an intermediary between the end IoT devices and cloud that results in the saving of computationally intensive processing performed on cloud servers. The servers located on a local edge network perform the developed algorithmic computations. Avoiding transmission over the cloud results in significant latency, response time, and computation power savings and therefore increases the efficiency of data transfer. The proposed edge computing model is implemented in Raspberry Pi as an edge, Heroku as cloud, and edge nodes as the combination of Pi with actuators and sensors. The monitored data from Pi are stored in MongoDB webserver that is controlled by Web dashboard. Finally, the developed model is implemented in cloud and edge where the edge server implementation performs better in terms of latency, bandwidth, throughput, response time, and CPU memory usage. (shrink)

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data (...) values. The decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm. Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge. (shrink)

Urban resilience is critical for ensuring the sustainability and adaptability of cities in the face of growing challenges such as climate change, population growth, and infrastructure degradation. Predictive analytics, powered by Artificial Intelligence (AI) and the Internet of Things (IoT), offers a transformative approach to enhancing urban resilience. This paper explores how AI-driven predictive analytics can optimize disaster preparedness, infrastructure maintenance, and resource allocation in smart cities. By integrating real-time data from IoT sensors with (...) advanced machine learning models, cities can proactively address vulnerabilities and improve decision-making. This study highlights key applications, challenges, and future directions for leveraging predictive analytics to build resilient urban ecosystems. (shrink)

An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed (...) design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants. (shrink)

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after (...) the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications. (shrink)

Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates (...) the potential of AI to revolutionize urban healthcare systems. Experimental results highlight improvements in diagnostic accuracy, emergency response times, and operational efficiency, paving the way for equitable and sustainable healthcare in smart cities. (shrink)

The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste (...) goals and promote circular economy practices. Experimental results demonstrate significant improvements in waste segregation accuracy, collection efficiency, and recycling rates, offering a sustainable blueprint for urban waste management. (shrink)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used (...) to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages (...) low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, (...) this study demonstrates the potential of AI to revolutionize sustainable energy management. Experimental results highlight improvements in grid reliability, cost reduction, and carbon footprint minimization, paving the way for resilient and eco-friendly urban energy systems. (shrink)

This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across (...) two points in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession. We hypothesize specific socioeconomic processes can be substantially reflected in the built environment of cities and, thus, render themselves visible at the street level. This paper describes the process of incorporating subjective visual ratings across two datasets of temporally separated street-view images, an algorithm, and a visualization tool. This work serves as a case study for utilizing AI and visualization techniques in a richer characterization of urban spaces that includes both objective metrics such as income (that operates at a broader scale) and subjective metrics such as perception of individuals (that operates at a narrower scale at specific locations). We outline an interdisciplinary methodology to test this hypothesis in streetscape data from Raleigh, NC, from 2008 to 2020. We describe the results of training algorithms that utilized image features with crowdsourced human perception ratings. We provide a comparison of the results with income data. The analysis and interpretation of this comparison provide insight into the challenges and opportunities for using AI technology in characterizing changes in urban environments. One challenge is the ability of human domain experts to interpret the output of algorithms through manipulation and to integrate these results into their workflow. This is addressed with a novel interface designed for interactive analysis and visualization. We conclude with a discussion of some of the benefits and limitations of integrating AI models in the human expert’s decision-making process in the presence of both subjective and objective metrics. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number (...) of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two disciplines, particularly the focus on causal inference in economics and prediction in machine learning. The paper discusses diverse applications of machine learning, from extracting insights from unstructured data to creating novel indicators and improving predictive accuracy, while also addressing challenges related to (...) data quality, computational efficiency, and data ownership. It emphasizes the importance of standardization, transparency, and ethical considerations in prediction tasks, recognizing that machine learning is a powerful tool but cannot replace economic theory. Ultimately, researchers remain optimistic about the transformative potential of machine learning in re-shaping research methodologies and generating new insights in economics and social sciences. (shrink)

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. (...) To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization, Ant Colony Optimization, and Round-Robin Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications. (shrink)

Ensuring public safety is a critical challenge for rapidly growing urban areas. Traditional policing and emergency response systems often struggle to keep pace with the complexity and scale of modern cities. Artificial Intelligence (AI) offers a transformative solution by enabling real-time crime prediction, optimizing emergency resource allocation, and enhancing situational awareness through IoT-enabled systems. This paper explores how AI-driven analytics, combined with data from surveillance cameras, social media, and environmental sensors, can improve public safety in smart (...) cities. By addressing challenges such as data privacy, algorithmic bias, and system scalability, this study demonstrates the potential of AI to create safer, more resilient urban environments. Experimental results highlight improvements in crime detection rates, emergency response times, and resource utilization, paving the way for smarter public safety systems. (shrink)

Coupled with the ‘smart city’, the idea of the ‘smart school’ is emerging in imaginings of the future of education. Various commercial, governmental and civil society organizations now envisage education as a highly coded, software-mediated and data-driven social institution. Such spaces are to be governed through computational processes written in computer code and tracked through big data. In an original analysis of developments from commercial, governmental and civil society sectors, the article examines two interrelated dimensions of (...) an emerging smart schools imaginary: the constant flows of digital data that smart schools depend on and the mobilization of analytics that enable student data to be used to anticipate and shape their behaviours; and the ways that young people are educated to become ‘computational operatives’ who must ‘learn to code’ in order to become ‘smart citizens’ in the governance of the smart city. These developments constitute an emerging educational space fabricated from intersecting standards, technologies, discourses and social actors, all infused with the aspirations of technical experts to govern the city at a distance through both monitoring young people as ‘data objects’ and schooling them as active ‘computational citizens’ with the responsibility to compute the future of the city. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management (...) and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)

This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics— (...) class='Hi'>machine learning, natural language processing, computer vision, data analytics and supervisory control — the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience—from industry leaders to students—the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. (shrink)

Urban green spaces are vital for mitigating climate change, enhancing biodiversity, and improving citizen well-being. However, traditional methods of designing and managing these spaces often lack the precision and scalability needed to address modern urban challenges. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban green spaces in smart cities. By integrating satellite imagery, soil sensors, and machine learning models, cities can dynamically monitor plant health, predict ecological impacts, and design green zones that maximize (...) biodiversity and sustainability. Experimental results demonstrate improvements in air quality, heat island reduction, and community engagement, offering a blueprint for AI-driven ecological urban planning. (shrink)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, (...) focusing on applications in transportation, energy, public safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (shrink)

This timely book tells the story of the smart technologies that reconstruct our world, by provoking their most salient functionality: the prediction and preemption of our day-to-day activities, preferences, health and credit risks, criminal intent and spending capacity. Mireille Hildebrandt claims that we are in transit between an information society and a data-driven society, which has far reaching consequences for the world we depend on. She highlights how the pervasive employment of machine-learning technologies that inform so-called ‘ (...) class='Hi'>data-driven agency’ threaten privacy, identity, autonomy, non-discrimination, due process and the presumption of innocence. The author argues how smart technologies undermine, reconfigure and overrule the ends of the law in a constitutional democracy, jeopardizing law as an instrument of justice, legal certainty and the public good. Finally, the book calls on lawyers, computer scientists and civil society not to reject smart technologies, explaining how further engaging these technologies may help to reinvent the effective protection of the rule of law. (shrink)

Since 2008 I have been closely following the conceptual/performance/video work of Daniel Peltz. Gently rendered through media installation, ethnographic, and performance strategies, Peltz’s work reverently and warmly engages the inner workings of social systems, leaving elegant rips and tears in any given socio/cultural quilt. He engages readymades (of social and media constructions) and uses what are identified as interruptionist/interventionist strategies to disrupt parts of an existing social system, thus allowing for something other to emerge. Like the stereoscope that requires two (...) identical images to create an illusion of a three-dimensional image, Peltz sometimes visualizes two separate elements to create an object or moment that requires space and depth to focus on its varied layers. They say your brain has to process and make meaningful sense out of all that visual information before it can accept the illusion. I say your brain has to do a similar thing when looking and seeing the divine, magical, and faithful (social) art making of Daniel Peltz. Daniel Peltz is Associate Professor of Film/Animation/Video at RISD. He divides his time between Rhode Island and Sweden and is currently at work on an exhibition at Botkyrka Konsthall, Stockholm opening February 2013 and a new project to be included in the IASKA: Spaced Biennial (Australia) in 2014. This interview took place on Skype and is part one of a two-part interview. Feliz Lucia Molina: Are you in Providence, RI? Daniel Peltz: I’m at my house in Providence. I’ve been back for six days teaching winter session at RISD and doing this workshop series—these investors’ drum circles with a group of wealth managers. a client of the firm, 2013, photo Shirin Adhami FLM: The wealth managers were all drumming together? DP: They were all drumming in response to the performance of their retirement portfolios. FLM: Is this part of the “Unrealized Gain/Loss” piece? DP: Yes, it’s part of a show I’m working on in Sweden. I can show you because it was just today. I’ll show you some of this. Lets see—share screen [click, click, click]. This is the room. I’ve been using the camera system that’s already installed in the conference rooms to record them. So the video is being recorded directly by the wealth management office’s tech staff. These are the people I worked with in the last workshop. This conference table is less wondrous. They’ve been learning to drum in response to their retirement portfolios. I started by bringing in my little bit of wealth for them to propose a management strategy, I came to the follow-up meeting with a counter proposal, which was this workshop. So they agreed to do this series of workshops instead of managing my wealth. FLM: Does everybody in the room manage your wealth? That’s kind of a lot of people. DP: That woman in the back there and Craig —they handle new client business. So when you come there with your wealth, they sit down with you and suggest to you how they would manage it. They’re very nice, responsible people. FLM: And where is this? DP: This is in Providence and the one I was showing you is in Waltham, where I was today. Then I’m going to Newport for the last one on Friday. So I will have done four of them. This is the group in Waltham that I just played with today. I played directly with the two groups in Providence and I’m working with an ethnomusicologist at Brown, Asha Tamarisa, who is facilitating the last two. She’s helped develop the workshop in terms of figuring out how to train a group to do this and thinking about the compositional challenges of working in response to retirement portfolio data. FLM: Screensharing helps me to figure out tiny bits here and there about the project. DP: I might even be able to play you a little of the audio I’ve also been working with a group of professional percussionists on what will become a quarterly public investors’ drum circle event. Their stuff was really nice, but I want you to hear what we sounded like in the workshops. They’ve all been recorded by the a/v system in the offices. We use a simple structure where the drummer investors interpret the sensation of gain or loss as embellishments to a base heartbeat rhythm. The group holds the heartbeat and, as each person experiences gain or loss, they embellish that rhythm. "Screensharing helps me to figure out tiny bits here and there about the project" FLM: Is the process all very spontaneous? DP: Its actually quite structured. We produce a custom stock ticker that shows the performance of their collective portfolios. In advance of the workshop each participant submits assets in their retirement portfolio. Then we make a stock ticker that shows the real-time performance of their assets so that they can respond to almost live data. FLM: Are they reading the stock ticker projected up on the screen in the room? Do people drum in correspondence to the visual live data of the performance of their assets? DP: Exactly. They’re looking at the stock ticker as opposed to each other, so it’s a slightly shifted drum circle. They’re looking at the ticker but they’re listening to each other—that’s what we’re practicing. For example, NVO—the price is 172.08 but it’s down -1.16 and as that moves across the screen, the person who’s retirement is invested in that asset starts to embellish when that arrow first appears, based on their experience of loss. They stop when it exits the screen and return to the heartbeat. You could have one ticker for each retirement portfolio, but the way the workshop is constructed is that we just isolate one asset from each person’s portfolio so that everyone in the group is represented in a single ticker. So they’re drumming and looking at the ticker, but they’re hearing each other experience gain and loss. We’ve removed direct visual engagement from the social structure of a drum circle but the oral engagement is still there. FLM: So they had their own sound interpretation of gain and loss? DP: Yeah and that part is quite spontaneous as you were saying. In some of the preparatory exercises we’ve been working to give the participants more strategies for interpreting the sensations of gain and loss. We’re trying to develop their capacity to embellish a heartbeat or base rhythm but I’m not invested in having a melodious result. I’m quite curious about what this kind of structure will result in without any desire for a particular result. FLM: For context, can you talk about the project you did a couple years ago in Bali, “Unrealized Gain/Loss” in relation to this current wealth management project? DP: The workshops I’ve been doing use a similar strategy to the other components in this project. It started in Indonesia on my sabbatical and I was really trying to understand where I was physically and where I was being on sabbatical—this kind of strange jubilee structure where every seventh year you’re supposed to renew your self. And it was around the time of the global financial crisis. I was on sabbatical for the global financial crisis. I remember watching my father, in particular, respond to the financial crisis by monitoring his retirement portfolio and trying to make sense of it. I realized on a visit to their home that he checked his retirement portfolio every single day and I was really struck by that because in some way our parents are mysteries to us as children, especially their moods. We know how important their moods are but we don’t know what governs them. And somehow it was like I’d figured it out, it’s the performance of the DOW! So I think there was something in that. Then I was in Indonesia and I had been drawn there by an interest in their highly ritualized Hindu culture, where so much of life is driven by a ceremonial calendar. I was interested in Bali as this predominantly Hindu pocket within a predominantly Islamic country and life there being organized by this ritual calendar functioning as a kind of resistance to the dominant global religion of free-market capitalism. So I started to explore that correlation between the ways in which this culture that I was living in was sort of “living for the afterlife” and this idea within certain segments of American society of “living for the afterwork life”. The idea that your wellbeing in the “afterwork life” is tied to forces that are unseen and largely beyond your control has strong similarities to many religious understandings of the universe. So I started to explore that and the crafts and materials that were around me were primarily Batik and percussion. Percussion is a huge part of daily life in Bali. These gamelan troupes were everywhere. And I also happened to be there for Nyepi, the day of silence, which is preceded by a very elaborate procession and construction of demonic statues. These were the things that were around me and I started studying Batik with one of the Batik artists there and also started a conversation with two master Batik artists, one who is American and her Indonesian husband. I developed these patterns that were based on symbols from the performance of my retirement portfolio and worked with the batik artists to produce two sarongs. d. peltz - 2012 In producing the designs, I treated the performance of my retirement portfolio since my arrival at RISD [7 years] as a significant interval and then I looked at the ways in which decisions are made within retirement portfolios as having an extension outwards from the individual assets that underlie the retirement portfolio, which are kind of like the base elements of the retirement world. Then there’s the allocation of your assets, which is a global way of understanding an individual according to typologies, which are often referred to in terms of risk—this notion of a ‘risk profile’. What type of ‘person’ you are is determined by your attitude towards risk, or potentiality, and I found that to be a really fascinating way of understanding the universe. I remember looking at the tabs in my retirement portfolio and finding this one for viewing ‘Unrealized Gain/Loss’ and that’s often how I work—is just going through the Cambridge Parking Code, for example, and finding this section of the code that was called ‘Crossing Non-Signalized Locations’ and just feeling “I can’t do any better than that,” you know? That’s what I was talking about in terms of ready-mades that exist in the social sphere. So I found that tab, actually, a long time ago, and I pulled it out of a journal when I was there [in Bali] working on these pieces and I wound up making these two sarongs and later on a series of porcelain vessels for holding one’s unrealized gains and losses. At the time, I knew that I wanted a performance to come out of them, but I wasn’t sure what it was. I was inspired very much by this sort of thing: Pulls out a TIAA-CREF brochure with a man in a suit seated at a table. This guy is probably an actor, and [the brochure] says “TIAA-Cref announces “Individual counseling sessions at the Rhode Island School of Design. Individual counseling sessions at no additional cost to you. You can discuss your personal financial situation with an experienced TIAA consultant on a confidential basis. We are available to help you discuss how to achieve your financial goals by investing in financial solutions such as mutual funds, brokerage, life insurance, and annuities...etc....What retirement benefits best fits your situation?” Often I encounter this and I think O.K., this is one way to prepare for the after-work life and it seems inadequate to me. But, I also find it really inspiring. I really like the visual language of it. I kind of want to be that man. I’d like to see if I could maybe buy his clothes. FLM: What it is about the man on the brochure that interests you? DP: He’s offering personalized objective advice and a detailed evaluation of everything you need to know and do. Who wouldn’t want that? But he’s also something of a contemporary priest or priestess, mediating between the unseen all-powerful universe of global capitalism and the common worker. So I started off developing Unrealized Gain/Loss directly from the charts that represent the performance of my retirement portfolio. Then I came home and I wanted to use those. I had this word in my journal “unrealized gain/loss vessel.” I had this notion of vessels that would contain unrealized gains and losses. That felt really important to me that they would have somewhere to go. I had been thinking a lot about altars and making offerings—that somehow this really fickle, massive, difficult-to-comprehend-universe of the financial world—that somehow it might be nice if you could making an offering to it. [In Bali] they made such beautiful elaborate offerings. So I studied offering making as well with one of the women there and she taught me some of the standard forms created by folding leaves and the significance of the floral arrangements. I started working in clay and then moved to porcelain and I made these unrealized gain/loss vessels. I made a few of them, they have holes on either side that you can’t get your fingers in. But something can go in there and something can go out of there. It’s a nice size for putting on an altar. Then I got this commission from Artists in Context who was interested in my doing something for this project “Artists Perspectives for the Nation” project. I proposed initiating these investors’ drum circles as a new public performance form. I’m interested in bringing together those two symbols—the symbol of the djembe and the drum circle. unrealized gain/loss vessel - d.peltz - 2012 FLM: I can imagine the public digital stock ticker performing like a soft fleeting stream of information, a kind of (meaningless) illusory comfort blanket. DP: In some way you have to understand its relevance outside of the obvious, right? Because the obvious is illogical. Nobody is actually using that data to day trade, for example. People aren’t sitting there with their computers watching the market data in Times Square or setting up an outdoor office and being like “OK it’s up 3 points or it’s up 1.56, trade! Ok now buy, Ok now sell!” That would be a kind of a nice performance, actually. But actual day traders would want more up to date data than that. To understand what that data is doing is really important. And that’s something I’ve realized—that I’m interested in a particular kind of data visualization, which is not about what data can tell people, but what data can tell people who are visualizing it. For example, in the Cambridge Project “Crossing Non-Signalized Locations” I was interested in the 10,000 excuses archive of data recording five years worth of excuses for why people thought they shouldn’t have received their parking ticket. I was not interested in making that excuse wall so that the public could see and understand this data. Inevitably the data will be seen by others but I was really interested in what the action of visualizing the data told those who were visualizing it. The parking attendants themselves were writing those excuses on the wall—I was interested in what that kind action of writing the excuses on the wall told them about the data. Similarly, I’m interested in designing a way to allow people to pass this data from the unseen universe of the market, through their own bodies, which happens through the merging of the drum and the stock ticker. I’m interested in those two also as symbols; the drum as this symbol of the earth, the body and a pagan counterculture, and the stock ticker referring to the ethereal world of global markets—bringing those two together and making them dependent. I’m often drawn to conceptual propositions that I become invested in testing in a sincere way—at first they often they sound humorous to others, but I have to remember that there is humor in them. I don’t sit around and laugh about these things. I stop finding them funny at all. I’m interested in the proposition that we could know something about the after-work life by drumming in response to our retirement portfolio. So then I become really interested in how to craft that into a viable performance form for myself and others. FLM: The aspect of using sound in “Unrealized Gain/Loss” as a way of embodying the information to the asset holder is really intriguing—using sound as a means of embodying the asset data. Was sound a medium that made sense to use immediately or were you considering other means of attempting to embody it? DP: Well I do use other mediums within the project like textile, ceramic, and batik patterns. I was first drawn to the history of Batik patterns as a socio-economic stratification system and the vessels as a way of embodying or manifesting this data of unrealized gain and loss. My first approach was to work with meditation actually—a meditation workshop with my colleagues. I was going to offer this “Unrealized Gain/Loss” workshop where you would explore the sensations of gain and loss by adapting the way yogi-nidra brings you into contact with your physical surroundings and stretches your perception. FLM: At Naropa University there are/were business & compassion workshops—a sort of mash-up of business and compassion and how compassion could be incorporated into a business model. This process and engagement of “embodying data” also has to do with “Participatory Democracy and the Future of Karaoke” you created at the DNC in Denver in 2008. DP: That kind of appropriation and instrumentalization of spiritual practice could be disturbing and thus compelling as a strategy. For various reasons, I’ve come to be drawn to both the form of a quarterly public performance and the established performance form of a drum circle. Its something I’ve seen myself do before, that is to mess up a really good functional social system like a drum circle or karaoke. I remember I was developing language for the karaoke project and started calling it “Participatory Democracy and the Future of Karaoke” and one of my assistants on the project, who’s an incredible artist, said, “but don’t you think the future of karaoke is guitar hero?” and I realized that maybe he thought I was trying to improve the form. I’m just trying to get people to have this shifted embodied experience, I need to craft the performance context so that they can do that. In the case of the karaoke project, for example, what did I need in order to be able to do this [a person to stand up in front of a bar and deliver a karaoke speech]? I realized that the body is very vulnerable so I built a podium so that the speaker can feel secure enough to do this, otherwise you couldn’t get to this state achieved by passing these speeches through the body. Then I was, like, well you need to be able to practice the lyrics because you don’t know all the “songs” by heart, which became an insert to the massive track books carried by most karaoke VJs. And then sometimes the crowd in the bar isn’t there with me, so I needed to extract the voice of the crowd cheering from the original venue and I needed the local audience to cheer if they wanted to. I’m interested in both where the form succeeds and fails. In particular, one of the most interesting things is this auditory gap between the space where you are, the reduced scale of applause in your own little bar, and this grand scale of applause at the convention center where the speech was originally delivered. FLM: How did the concept of “Participatory Democracy Karaoke” come about and why did you use karaoke as a means of engaging what was happening at the DNC in 2008? DP: I was looking at a lot of different readymade media infrastructures within the city at the time. I was interested in the emergency broadcast systems and I developed a proposal to repurpose that system and karaoke became an important symbol because it was this populist form that was already engaged in exploring the sensation of celebrity but also visualizing the gap between ourselves and those with more power and influence. So it had this readymade capacity to play with power and celebrity and I felt like the Obama presidential campaign, the first one in particular, had some very curious overlaps with this culture of celebrity. Obama of course rose to power partly based on his oratory abilities and I was interested in how karaoke as a vehicle was so adept at offering people all that was left of authentic expression in a political speech. Managing a politician and constructing their identity is such a developed practice that it becomes, kind of automatically, a metaphor for the way that our own identities are constructed. Of course the Obama campaign was very hip and savvy and deploying this notion of grassroots. It was really pioneering in its use of social media and this deployment of notions of populism. I was interested at that time in the capacity of karaoke to track speech patterns, precise tempos of anyone’s delivery, and that to me was a way of thinking about what might be left of the authentic self. I was interested in karaoke’s capacity to extract that authentic component and offer it to others. So not just to stand up and be them, but to [literally] pass their speech patterns through your own body. A long time ago, it started to strike me as odd that one would make media at all. It struck me as a kind of un-contemporary way of going about making art in an age of media overflow. That logic extended into my thinking on delivery devices and installation as well. Why would one buy a screen or even set one up when there are so many out there? And the way in which these media displays function in karaoke bars and bars in general is very interesting to me. I’m drawn to this passive consumption of media where your primary social interaction is with the bartender or a few other individuals but these screens are around you and your attention is shifting back and forth between these spaces. So much political rhetoric is spoken to a half-listening audience. I was interested in what was happening in the slippage and that karaoke was a kind of slippage amplifier. So if you put people in a bar and they are delivering a Kucinich speech and Kucinich comes up on a screen then your getting a sound bite of Kucinich and an image of Kucinich and your friend is talking to you— FLM: A kind of magic takes place in that incongruency between sound and image and the karaoke participant who’s relaying the speech-text at the same time. DP: Exactly and there’s some kind of truth in that experience of reality. FLM: Yeah, there’s something uncanny about seeing the body close-up like that as though it can’t lie to you in that moment or context. It’s a weird moment of luminous clarity. The work you’ve done and all of what you’re saying about karaoke is so very interesting to me. Growing up, my dad and uncles sang karaoke a lot (and still do) and being first-generation Filipino American is a different cultural subtext entirely. I’m also seeing it from the position of witnessing family members who are carriers of these stories and songs. And seeing them cherish these Tony Bennett, Frank Sinatra, Elvis Presley songs so much, I also see how their bodies are literal vessels of popular music that signifies something greater—it signifies their cultural and socioeconomic place in the world. The way that they cherish these American 1950s and 1960s pop songs is similar to having a certain kind of reverence for their spiritual faith or Catholicism. It’s a similar kind of care, focus, and attention exerted in karaoke and Catholicism—that these two structures and systems give something to focus on. Setsuko, Seiji and Hitoshi in Obama, Japan as Obama in Indiana — d. peltz — 2008 DP: Sure there something about the relationship we have to our candidate or the way we go about choosing a candidate, which is very similar to a deity or idol. I learned a lot about karaoke culture through this project. I’d never done karaoke. I’d never been interested in it as a form and then I travelled to Japan as part of the project when I made “Setsuko, Seiji and Hitoshi in Obama, Japan as Barack Obama in Indiana” and it was quite fascinating because my image of karaoke had always been this very public forum in a karaoke bar and then I discovered there’s this whole other world of karaoke where people even go on their own, they go and rent a room or cubicle and sing, or they go on a date—just the two of them go and sit in a room and do karaoke together. FLM: I’m interested in issues that take place within or as a result of specific karaoke culture(s). In the Philippines within the last several years there’s been occurring the “My Way Killings” phenomenon. Apparently baklas or gay men are employed by karaoke establishments to help “smooth over conflicts over karaoke singing”—these social forms of conduct, or lack thereof that arise out of the infrastructures of this social sport. In this one rural part of the Philippines there’s a village karaoke machine that the whole village shares—the Aeta indigenous people have an appointed “keeper” of the karaoke machine. I also see karaoke as a proxy to the confessional box in Catholicism where one goes to pour out their sins (minus the penance and redemption). The karaoke machine enables one to literally sing out whatever’s going on internally, but through highly saturated popular song lyrics. While karaoke is a public and social sport, it can also be a private one. Karaoke is a means of communion with each other. DP: Right, this preference for this kind of mediated communication. In Japan I was trying to organize people to work on this project and I was talking about throwing a party and they were like, well, we have to rent a karaoke machine because what else are we going to do? And I think its kind of serving that function of surrogacy—emotional surrogacy. FLM: And karaoke tools can be read as ritual tools—the magic mic that holds everything. There’s got to be some overlap at some point—between religious ceremony and devotion to the karaoke machine. DP: The way I designed the piece was so that it could slip right on top of the ready-made karaoke infrastructure. There was a flat-packed podium and it was made of a single sheet of plywood with no fasteners that slotted into itself. Those were sent out to a network of karaoke bars that I invited to become “karaoke convention centers.” The local VJs downloaded our custom-made, speech-extracted tracks that were designed to play on their existing equipment. In this sense the piece is a permanent installation. If you go to Denver today, some of the VJs still have the tracks in their library of offerings, the Ramones and Romney. It was this notion of re-purposing readymade infrastructure to create a distributable populist form. Obama was coming and the convention was in the Pepsi Center and 30,000 people were coming including 10,000 journalists. The impetus behind the larger art project that commissioned international artists to make works in Denver, was that local people weren’t going to have much access to this convention. It was like an invasion, the city was being descended upon, but if you lived a block away from the site, you had the same access as people in Zimbabwe&mash;30 second media bits excerpted from the speeches. So it seemed to me that the fundamental gesture was how do I take that signal, which was travelling out of the convention center, and create a local interruption? FLM: Is that what you mean by ‘intervention’? The term is frequently used to help describe your work. DP: It depends on the day. Around that karaoke project I had a conversation with Krzysztof Wodiczko and he was proposing that maybe rather than intervention, we should consider the word interruption. Because intervention is kind of an overused term in the field of art and it has militaristic and therapeutic associations outside of the art context. Fundamentally, the idea of intervening speaks to the readymade social world as your primary material. So it’s basically suggesting a kind of subtractive process, which gets back to this question: what is the role of the contemporary media artist in a world that’s so saturated with media? You can’t work additively in a saturated field. If you want to make a visible mark you have to work subtractively. That’s what intervention is about to me, just another way of saying “to work subtractively.” FLM: So the interventions or interruptions aren’t necessarily adding or subtracting, but are they putting orange cones there? What are they doing exactly? DP: I think you’re right about that, they’re not really subtracting. They’re adding to the scope of possibility. I’ve been thinking (with this exhibition I’m mounting next month) about the work as explorations and expansions of social possibility. Maybe it’s more insertion. I started calling the pieces ‘insertions’ that I was making in Rejmyre—a small town in Sweden where I’ve been working for six years now. My favorite site to install there is the tourist bureau. I started calling the video pieces that I made for the tourist bureau, video insertions. This idea that you’re inserting something into the readymade media infrastructure of the world resonates with me. Insertion leverages a context, creating a possibility that the inserted object might be naturalized in the process — that someone can encounter my video in Rejmyre as tourist information. And then all of a sudden tourist information can include some American guy prostrating through town and it can include really bad relationship advice. Maybe insertion is a better word. tourist information – d. peltz 2009-present  . (shrink)

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase (...) overall dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning, which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning, incremental learning, and cyclic incremental learning. We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order (...) to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Electric-mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high-profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing technologies. An ontology schema forms the terminology relationships needed to build (...) two intelligent supply chain management modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high-end equipment manufacturers using mass customization. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT (...) class='Hi'>edge applications security and approval. There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, (...) class='Hi'>machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%. (shrink)

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare (...) sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by (...) the interaction between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract “sense” from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts. (shrink)

Machine learning (ML) has emerged as a promising tool in psychiatry, revolutionising diagnostic processes and patient outcomes. In this paper, I argue that while ML studies show promising initial results, their application in mimicking clinician-based judgements presents inherent limitations (Shatte et al. in Psychol Med 49:1426–1448. https://doi.org/10.1017/S0033291719000151, 2019). Most models still rely on DSM (the Diagnostic and Statistical Manual of Mental Disorders) categories, known for their heterogeneity and low predictive value. DSM's descriptive nature limits the validity of psychiatric (...) diagnoses, which leads to overdiagnosis, comorbidity, and low remission rates. The application in psychiatry highlights the limitations of supervised ML techniques. Supervised ML models inherit the validity issues of their training data set. When the model's outcome is a DSM classification, this can never be more valid or predictive than the clinician’s judgement. Therefore, I argue that these models have little added value to the patient. Moreover, the lack of known underlying causal pathways in psychiatric disorders prevents validating ML models based on such classifications. As such, I argue that high accuracy in these models is misleading when it is understood as validating the classification. In conclusion, these models will not will not offer any real benefit to patient outcomes. I propose a shift in focus, advocating for ML models to prioritise improving the predictability of prognosis, treatment selection, and prevention. Therefore, data selection and outcome variables should be geared towards this transdiagnostic goal. This way, ML can be leveraged to better support clinicians in personalised treatment strategies for mental health patients. (shrink)

A sustainable supply chain management strategy reduces risks and meets environmental, economic and social objectives by integrating environmental and financial practices. In an ever-changing environment, supply chains have become vulnerable at many levels. In a global supply chain, carefully tracing a product is of great importance to avoid future problems. This paper describes a self-updating smart contract, which includes data validation, for tracing global supply chains using blockchains. Our proposal uses a machine learning model to detect anomalies (...) on traceable data, which helps supply chain operators detect anomalous behavior at any point in the chain in real time. Hyperledger Caliper has been used to evaluate our proposal, and obtained a combined average throughput of 184 transactions per second and an average latency of 0.41 seconds, ensuring that our proposal does not negatively impact supply chain processes while improving supply chain management through data anomaly detection. (shrink)

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for (...) tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus. (shrink)

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach (...) exact and fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data. (shrink)

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing (...) servers, and terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training. (shrink)

Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could (...) provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children’s motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism motor signatures analyzing their coordinates of movements. We used a smart tablet device to record coordinates of dragging movements carried out by 60 children during a cognitive task. Machine learning analysis of children’s motor patterns identified autism with 93% accuracy, demonstrating that autism can be computationally identified. The analysis of the features that most affect the prediction reveals and describes the differences between the groups, confirming that motor abnormalities are a core feature of autism. (shrink)

Many theories of complex cognitive-motor skill learning are built on the notion that basic cognitive processes group actions into easy-to-perform sequences. The present work examines predictions derived from laboratory-based studies of motor chunking and motor preparation using data collected from the real-time strategy video game StarCraft 2. We examined 996,163 action sequences in the telemetry data of 3,317 players across seven levels of skill. As predicted, the latency to the first action is delayed relative to (...) the other actions in the group. Other predictions, inspired by the memory drum theory of Henry and Rogers, received only weak support. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their (...) tolerable limit. From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance (...) based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate (...) risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care. Recurrent neural network, long short-term memory, gated recurrent units, and bidirectional long short-term memory are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance. (shrink)

This paper outlines the development of a proof-of-concept workflow using machine learning and computer vision techniques to unlock the data within digitized handwritten US Census forms from the 1950s. The 1950s US Census includes over 6.5 million page images and was only recently made available to the public on April 1, 2022, following a 72-year access restriction period. Our project uses computational treatments to assist researchers in their efforts to recover and preserve the history of the erased Sacramento (...) Japantown. Sacramento once housed the fourth largest Japantown in the United States before experiencing WWII Japanese American Incarceration and the 1950s US Government program of urban renewal. The goal is to augment a researcher’s work in selecting a subset of Census pages for further transcription and analysis. We demonstrate a workflow for extracting demographic information using computer vision for image segmentation, and machine learning for handwritten character recognition. The workflow consists of a computational filtering process for Census records and a user interface for page review. These computational techniques are suitable for other cities, states, and communities, and demonstrate new strategies to unlock vital demographic information. The approach highlights the potential benefits of computational techniques for the analysis of form-based historical records of the twentieth century that can have an impact on social justice. (shrink)

Comprehensively improving the level of vocational education and teaching quality has become an important initiative to meet the new round of technological revolution and industrial change. The traditional teaching mode can no longer meet the needs of industries and enterprises for job competences, and all higher education institutions are actively thinking about how to carry out teaching reform. Virtual reality can effectively solve the above-mentioned drawbacks, but the hardware facilities of the existing VR systems are extremely expensive, making it (...) impossible to popularize it in teaching. In this context, this paper is based on VR for the design of an interactive teaching platform. This paper discusses the key technologies of virtual reality, introduces the relevant theoretical foundation, and describes the current virtual reality devices with high popularity and their advantages and disadvantages for systematic analysis. From the perspective of teaching design, the design of human-computer interaction teaching process using VR technology to develop learning scenes is studied, introducing 3D modeling techniques, model construction, the production of picture and text panels, the production of video materials, and the establishment of virtual tour guide 3D scenes. The principle of this paper is to build a 3D vision for building 3D views of educational needs to design high quality images and video animation using 3DMAX and unity 3D engines. Then, we introduce the use of VR glasses, smart phones, and Bluetooth wireless handle to build a simple interactive teaching platform; the platform is of low cost of construction and student coverage, to solve the teaching resources shortage, equipment conditions lagging behind, the curriculum content aging, teachers and students who cannot interact in real time, and a series of problems. (shrink)

As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology (...) can help us make the inductive leap. I do so by thinking through a thought experiment, Goodman’s New Riddle of Induction (Fact, fiction, and forecast, Harvard University Press, 1955). Goodman’s riddle helps flesh out three problems of induction: (1) the problem of equal goodies, that there are often too many equally good inductive results given the same data; (2) the problem of diverging performance, that these equally good results can give opposite predictions in the future; and (3) the problem of mediocrity, that when averaged across all equally possible datasets and tasks, no inductive algorithm outperforms any other. I show that all these three problems are manifested as real obstacles in ML practice, namely, the Rashomon effect (Breiman in Stat Sci 16(3):199–231, 2001), the problem of underspecification (D’Amour et al. in J Mach Learn Res, 2020, https://doi.org/10.48550/arXiv.2011.03395 ), and the No Free Lunch theorem (Wolpert in Neural Comput 8(7):1341–90, 1996, https://doi.org/10.1162/neco.1996.8.7.1341 ). Lastly, I argue that proper data ontology can help mitigate these problems and I demonstrate how using concrete examples from climate science. This research highlights the links between philosophers’ discussions of induction and implications in ML practice. (shrink)

In order to improve the effect of smart city construction, this paper combines smart buildings and ethical computing to conduct research on smart city edge computing. The new smart city architecture based on the flexible deployment of edge computing and data slicing capabilities provides support for the transformation of smart city construction from hardware embedded technology, access means, and software data processing. Moreover, this paper uses information technology to collect, process, analyze, (...) use the information to achieve intelligence, and integrate resources and information of cities and people to build a smart city functional architecture. Moreover, this paper combines simulation technology for experimental research. Through experimental analysis, it can be seen that the smart city edge computing method based on smart buildings and ethical computing proposed in this paper has good results. (shrink)

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about$750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and (...) affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences. (shrink)

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that (...) different social and psychological backgrounds of individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)

The relatedness between a country or a firm and a product is a measure of the feasibility of that economic activity. As such, it is a driver for investments at a private and institutional level. Traditionally, relatedness is measured using networks derived by country-level co-occurrences of product pairs, that is counting how many countries export both. In this work, we compare networks and machine learning algorithms trained not only on country-level data, but also on firms, which is something (...) not much studied due to the low availability of firm-level data. We quantitatively compare the different measures of relatedness, by using them to forecast the exports at the country and firm level, assuming that more related products have a higher likelihood to be exported in the future. Our results show that relatedness is scale dependent: the best assessments are obtained by using machine learning on the same typology of data one wants to predict. Moreover, we found that while relatedness measures based on country data are not suitable for firms, firm-level data are very informative also for the development of countries. In this sense, models built on firm data provide a better assessment of relatedness. We also discuss the effect of using parameter optimization and community detection algorithms to identify clusters of related companies and products, finding that a partition into a higher number of blocks decreases the computational time while maintaining a prediction performance well above the network-based benchmarks. (shrink)

In this paper, I reflect on the puzzle that machine learning presents to social theory to develop an account of its distinct impact on social reality. I start by presenting how machine learning has presented a challenge to social theory as a research subject comprising both familiar and alien characteristics (1.). Taking this as an occasion for theoretical inquiry, I then propose a conceptual framework to investigate how algorithmic models of social phenomena relate to social reality and what (...) their stochastic mode of operation entails in terms of their sociality (2.). Analyzed through a theoretical lens that relies on central tenets of sociological systems theory, I find that machine learning implies a distinct epistemic transformation, based on how algorithmic modeling techniques process meaning as represented in data embedded in vector space. Building on this characterization, I introduce my conceptualization of stochastic technology as distinct from mechanistic technologies that rely on causal fixation (3.). Based on this understanding, I suggest that real-world applications of machine learning are often characterized by a constitutive tension between the stochastic properties of their outputs and the ways in which they are put to use in practice. Focussing on the large language models LaMDA and ChatGPT, I examine the epistemological implications of LLMs to account for the confusion of correlation and causality as the root of this tension. Next, I illustrate my theoretical conception by way of discussing an essay on image models by German media artist Hito Steyerl (4.). Following a critical reflection on Steyerl's characterization of Stable Diffusion as a “white box ”, I finally propose to conceive ofmachine learning-based technologies as stochastic contingency machines that transform social indeterminacy into contingent observations of social phenomena (5.) In this perspective, machine learning constitutes an epistemic technology that operates on meaning as extractable from data by means of algorithmic data modeling techniques to produce stochastic accounts of social reality. (shrink)

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering (...) it incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)