Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high demand for this (...) type of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental case study, we found that not only are solutions like Cloudera’s Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines. (shrink)

Quantum computing technologies have become a hot topic in academia and industry receiving much attention and financial support from all sides. Building a quantum computer that can be used practically is in itself an outstanding challenge that has become the ‘new race to the moon’. Next to researchers and vendors of future computing technologies, national authorities are showing strong interest in maturing this technology due to its known potential to break many of today’s encryption techniques, which would have (...) significant and potentially disruptive impact on our society. It is, however, quite likely that quantum computing has beneficial impact on many computational disciplines. In this article we describe our vision of future developments in scientific computing that would be enabled by the advent of software-programmable quantum computers. We thereby assume that quantum computers will form part of a hybrid accelerated computing platform like GPUs and co-processor cards do today. In particular, we address the potential of quantum algorithms to bring major breakthroughs in applied mathematics and its applications. Finally, we give several examples that demonstrate the possible impact of quantum-accelerated scientific computing on society. (shrink)

At that moment, when I had the TV sound off, I was in a 382 mood; I had just dialed it. So although I heard the emptiness intellectually, I didn’t feel it. My first reaction consisted of being grat...

This paper aims to determine knowledge management (KM) factors which have strong impact on high performance. Also, the study aims to compare KMM between intermediate colleges. This study was applied on three intermediate colleges in Gaza strip, Palestine. Asian productivity organization model was applied to measure KMM. Second dimension which assess high performance was developed by the authors. The controlled sample was 190. Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation (...) using Cronbach’s alpha, “ANOVA”, simple linear regression and step wise regression and LSD test. The overall findings of the current study show that maturity level is in the second level. Findings also support the main hypothesis and its sub- hypotheses. The most important factors effecting high performance are the processes, KM leadership, people and KM outcomes. In addition, there are differences in high performance for college (PTC). Furthermore, the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first comparative study between intermediate colleges in Palestine that explores the status of KMM using the Asian productivity model. Research limitations was that the survey findings were based on intermediate colleges in Gaza Strip. (shrink)

This article draws on the concept of “performance” to argue for greater recognition of preexisting practices in the configuration of users. Through an Australian case study of a computer-based dairy decision-support system introduced via a two-day workshop to participating farmers, the article examines the assembling of imputed farmer users in the design of the software. It then explores how the designer and trainers attempt, through the decision-support system, to mobilize their network and align the imputed user with farmers' preexisting (...) performances. The case study highlights that attempts to make workable on-farm the “new” performances of users inscribed in the software are highly contingent on farmers' preexisting knowledge practices. These “old” performances problematize the designer's and trainers' version of imputed users and contribute to partial translation of the decision-support system. A focus on performance is argued to provide a useful starting point in mapping the effects of preexisting knowledge practices on attempts to enroll users in technosocial programs. (shrink)

Students’ perceptions of learning are important predictors of their learning motivation and academic performance. Examining perceptions of learning has meaningful implications for instruction practices, while it has been largely neglected in the research of computational thinking. To contribute to the development of CT education, we explored the influence of students’ perceptions on their motivation and performance in CT acquisition and examined the gender difference in the structural model using a multigroup structural equation modeling analysis. Two hundred and eighty-five (...) students from a Chinese urban high school were recruited for the study. The analysis revealed that students’ perceptions of CT positively influenced their CT performance and learning motivation, and some motivational constructs, namely self-efficacy and learning goal orientation, also positively influenced their CT performance. Furthermore, in the male student group, perceptions of CT exhibited significant correlations with both self-efficacy and LGO. However, no significant correlation was found in the female student group. Implications for research and teaching practice in CT education are presented herein. (shrink)

Twenty classes in ten schools with 627 sixth?grade students in five cities in Taiwan participated in this study. The research provides information on the performance differences among written computation, pictorial representation, symbolic representation and number sense. The results of One?way ANOVA analysis indicate that significant difference was found among WCT, PRT, SRT and NST tests, with F=536.327, p=0.000. The a posteriori comparisons show for each pair (WCT vs PRT, WCT vs SRT, WCT vs NST, PRT vs SRT and SRT (...) vs NST) significant difference at the 0.001 level (p=0.000), except for the pair comparison between PRT and NST. This implies that these Taiwanese students were highly skilled in written computation but their written skills were not equally transferred to use of non?computational paths that depended on symbolic representation, pictorial representation and number sense to solve similar problems. (shrink)

The paper considers the intellectual computer mathematics system InparSolver, which is designed to automatically explore and solve basic classes of computational mathematics problems on multi-core computers with graphics accelerators. The problems of results reliability of solving problems with approximate input data are outlined. The features of the use of existing computer mathematics systems are analyzed, their weaknesses are found. The functionality of InparSolver, some innovative approaches to the implementation of effective solutions to problems in a hybrid architecture are described. Examples (...) of applied usage of InparSolver for processes mathematical modeling in various subject areas are given. Nowadays, new more complex objects and phenomena in many subject areas are constantly emerging, which are subject to mathematical research on a computer. This encourages the development of new numerical methods and technologies of mathematical modeling, as well as the creation of more powerful computers for their implementation. With the advent and constant development of supercomputers of various architectures, the problems of their effective use, expansion of tasks range should be solved, ensuring the reliability of computer results and increasing the level of intellectual information support for users ‒ specialists in various fields. Today, the issues of solving these problems are given special attention by many specialists in the fields of information technology and parallel programming. The world's leadingscientists in the field of computer technology see the solution to the problems of efficient usage of modern supercomputers in algorithmic software creation that easily adapts to different computer architectures with different types of memory and coprocessors, supports efficient parallelism on millions of cores etc. In addition, improving the efficiency of high-performance computing on modern supercomputers is provided by their intellectualization, transferring to the computer to perform a significant part of the functions. The industry of development and usage of intelligent computer technologies is one of the main directions of science and technology development in modern society. (shrink)

The relationship between abstract formal procedures and the activities of actual physical systems has proved to be surprisingly subtle and controversial, and there are a number of competing accounts of when a physical system can be properly said to implement a mathematical formalism and hence perform a computation. I defend an account wherein computational descriptions of physical systems are high-level normative interpretations motivated by our pragmatic concerns. Furthermore, the criteria of utility and success vary according to our diverse purposes (...) and pragmatic goals. Hence there is no independent or uniform fact to the matter, and I advance the ‘anti-realist’ conclusion that computational descriptions of physical systems are not founded upon deep ontological distinctions, but rather upon interest-relative human conventions. Hence physical computation is a ‘conventional’ rather than a ‘natural’ kind. (shrink)

Computational models of infant word‐finding typically operate over transcriptions of infant‐directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP‐Parser (...) model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP‐Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP‐Parse mimics American English learners' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance. (shrink)

The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new (...) academic discipline of computational entrepreneurship, which centers on: (i) an exponentially growing (and less expensive) computing power, to the extent that almost everybody in a modern society can own and use that; (ii) omnipresent high-speed Internet connectivity, wired or wireless, representing our modern day’s economic connectomics; (iii) growing concern of exploiting “serendipity” for a strategic commercial advantage; and (iv) growing capabilities of lay people in performing calculations for their informed decisions in taking fast-moving entrepreneurial opportunities. Computational entrepreneurship has slowly become a new mode of operation for business ventures and will likely bring the academic discipline of entrepreneurship back to mainstream economics. (shrink)

The first implantation of a brain chip into a human paralysis patient by Neuralink demonstrated much potential for treating debilitating neurological diseases and injuries. Nevertheless, brain chips can also be implanted in healthy people to provide an interface between the human brain with computers, robotic machines, and novel artificial intelligence platforms, which generates new ethical issues. The focus here is on the development of brain chip implants that can significantly improve memory, intelligence, and cognition, thereby boosting performance in national (...) examinations for university admissions and securing civil service jobs, thus providing a “game-changer” and “shortcut” for many students and parents. Given that Islam is a major world religion, constituting a significant portion of the global population, it is crucial for the biomedical industry to comprehend Islamic perspectives on emerging medical technologies, which will enable it to more effectively cater to a substantial and growing demographic. We thus critically examine whether the application of brain chip technology to enhance academic performance in highly competitive examinations is consistent with Islamic principles. Based on the Islamic jurisprudential framework, such an application for intellectual enhancement of normal and healthy people without any mental impairment may conflict with the injunction to preserve intellect (Hifz al-Aql) and “consideration of consequences” (murāʿāt al-ma'ālāt) in Islam. It may also be viewed as tampering with Allah’s creation (Taghyir Khalq Allah). Gaining such unfair advantages in competitive examinations will likely be viewed as unethical, by transgressing the core Islamic precepts of Amanah (trustworthiness), Al-‘Adl (justice), Ikhlas (sincerity), and Mujahadah (striving). (shrink)

It is conventionally understood that computers play a rather limited role in theoretical mathematics. While computation is indispensable in applied mathematics and the theory of computing and algorithms is rich and thriving, one does not, even today, expect to find computers in theoretical mathematics settings beyond the theory of computing. Where computers are used, by those studying combinatorics , algebra, number theory, or dynamical systems, the computer most often assumes the role of an automated and speedy theoretician, performing (...) manipulations and checking cases in a way assumed to be possible for human theoreticians, if only they had the time, the memory, and the precision. Automated proofs have become standard tools in mathematical logic, and it is often expected that proofs be published in a computer-checkable format. It is not surprising, then, that most philosophical work on computers in theoretical mathematics has been on computers' roles as supplementary mathematicians. Donald MacKenzie's 2001 book Mechanizing Proof demonstrates the rich potential for social and historical studies to complement the substantial analytic debate in this area of philosophy. But what of computers in theoretical mathematics behaving as computers, and not as mere mechanized mathematicians? Very little role is commonly assumed for computers working as supplements to mathematicians, rather than as supplementary mathematicians themselves. Accordingly, very little philosophy has attempted to grapple with theoretical mathematics in which computers play an essential but essentially non-theoretical role. My presentation will draw on work I conducted as a researcher in harmonic analysis on fractals at Cornell University. I will analyze the explicit and implicit conceptual apparatus employed in my and my fellow researchers' use of computers in the theoretical study of second order differential equations, such as those for sound and heat flow, on various fractal analogues of the Sierpinski gasket. Such gaskets are easy to visualize in very crude approximation in a low number of dimensions. As one increases the complexity of the gasket or the refinement of one's analysis, visualization and precise computation become impossible, and soon computers are unable to produce even approximate data to model differential equations in these situations. We thus had to carefully choose analytic approaches and methods to make our theoretical mathematics amenable to computer simulation. In my case, studying the transformation of the gaskets as they are expanded into increasingly high dimensions, computer simulation eventually required that the problem be reimagined entirely in terms of interlinked systems of parameters. This computer-approximation-driven theoretical orientation shaped my mathematical intuitions toward the problem and guided my fellow researchers and me in both theoretical and computational directions. We discovered both that computer approximation could be incredibly powerful as an aid to intuition, and that it can be incredibly difficult to transfer computer-oriented mathematics back into the purely theoretical standards of our area of specialty. I will address the philosophical implications of computer-driven theoretical mathematics, asking how computer experiments can shape both the content and standards of theoretical sciences. (shrink)

In this paper I criticize a view of functional localization in neuroscience, which I call “computational absolutism”. “Absolutism” in general is the view that each part of the brain should be given a single, univocal function ascription. Traditional varieties of absolutism posit that each part of the brain processes a particular type of information and/or performs a specific task. These function attributions are currently beset by physiological evidence which seems to suggest that brain areas are multifunctional—that they process distinct information (...) and perform different tasks depending on context. Many theorists take this contextual variation as inimical to successful localization, and claim that we can avoid it by changing our functional descriptions to computational descriptions. The idea is that we can have highly generalizable and predictive functional theories if we can discover a single computation performed by each area regardless of the specific context in which it operates. I argue, drawing on computational models of perceptual area MT, that this computational version of absolutism fails to come through on its promises. In MT, the modeling field has not produced a univocal computational description, but instead a plurality of models analyzing different aspects of MT function. Moreover, CA cannot appeal to theoretical unification to solve this problem, since highly general models, on their own, neither explain nor predict what MT does in any particular context. I close by offering a perspective on neural modeling inspired by Nancy Cartwright’s and Margaret Morrison’s views of modeling in the physical sciences. (shrink)

In recent years, the gastronomy industry has increased the demand for rigorous and reliable tools to evaluate culinary creativity; but conceptually, creativity is difficult to define and even more difficult to measure. In this paper, we propose an AI-based method for assessing culinary product creativity by using the renowned high cuisine restaurant elBulli as a case study to understand the proliferation and scale of an entity’s creativity and innovation. To achieve so, we trained a Random Forest Classifier to assess (...) the level of creativity within the restaurant’s recipes. The application of this model to elBulli’s recipes allowed us to answer the following questions: how can creativity in a culinary product be characterized? Is there a single trait that is the key to culinary creativity? Is the creative culinary product a truly original outcome? Our key findings are (1) although ingredients and preparation techniques prove to be suitable attributes to characterize creativity in a recipe, none of them nor an elite subset clearly identifies creative cuisine, (2) new products are not radically novel with respect to previous creations, (3) creativity is more related to new uses and combinations of existing ingredients and techniques rather than to new ingredients or techniques. We believe that our method can find practical applications in supporting and guiding the creation of culinary products, identifying their strengths and weaknesses, and helping restaurants maintain a sustainable competitive advantage. (shrink)

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that (...) the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller’s performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller’s performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300’s speller performance. We found that several of the RSVP’s event-related potential (ERP) and behavioral features were correlated with the P300 speller’s offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor ( r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor ( r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone. (shrink)

RationaleThere are growing concerns over the occurrence of adverse physiologic events occurring in pilots during operation of United States Air Force and Navy high-performance aircraft. We hypothesize that a heightened inspiratory work of breathing experienced by jet pilots by virtue of the on-board life support system may constitute a “distraction stimulus” consequent to an increased sensation of respiratory muscle effort. As such, the purpose of this study was to determine whether increasing inspiratory muscle effort adversely impacts on attentional (...) performance.MethodsTwelve, healthy participants were recruited for this study. Participants completed six repetitions of a modified Masked Conjunctive Continuous Performance Task protocol while breathing against four different inspiratory threshold loads to assess median reaction times. A computer-controlled threshold loading device was used to set the inspiratory threshold loads. Repeated measures analysis of variances were performed to examine: the efficacy of the threshold loading device to impose significantly higher loading at each loading condition; the effects of loading condition on respiratory muscle effort sensation; and the influence of hypercapnia on MCCPT scores during inspiratory threshold loading. Generalized additive mixed effects models were used to examine the potential non-linear effects of respiratory muscular effort sensation, device loading, and hypercapnia, on MCCPT scores during inspiratory threshold loading.ResultsInspiratory threshold loading significantly augmented inspiratory effort sensation and the inspiratory pressure-time product. Our analyses also revealed that median hit RT was positively associated with inspiratory effort sensation during inspiratory loading trials.ConclusionThe findings of this work suggest that it was not increasing inspiratory muscle effort per se, but rather participant’s subjective perception of inspiratory “load” that impacts negatively on attentional performance; i.e., as the degree of inspiratory effort sensation increased, sotoo did median hit RT. As such, it is reasonable to suggest that minimizing inspiratory effort sensation during high-performance flight operations may prove useful in reducing pilot RTs during complex behavioral tasks. (shrink)

Cloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using (...) traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques. (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)

Verification and validation of computer codes and models used in simulations are two aspects of the scientific practice of high importance that recently have been discussed widely by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to the model’s relation to the real world and its intended use. Because complex simulations are generally opaque to a practitioner, the Duhem problem (...) can arise with verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or the model’s general inadequacy to its target should be blamed in the case of a failure. I argue that a clear distinction between computer modeling and simulation has to be made to disentangle verification and validation. Drawing on that distinction, I suggest to associate modeling with verification and simulation, which shares common epistemic strategies with experimentation, with validation. To explain the reasons for their entanglement in practice, I propose a Weberian ideal–typical model of modeling and simulation as roles in practice. I examine an approach to mitigate the Duhem problem for verification and validation that is generally applicable in practice and is based on differences in epistemic strategies and scopes. Based on this analysis, I suggest two strategies to increase the reliability of simulation results, namely, avoiding alterations of verified models at the validation stage as well as performing simulations of the same target system using two or more different models. In response to Winsberg’s claim that verification and validation are entangled I argue that deploying the methodology proposed in this work it is possible to mitigate inseparability of V&V in many if not all domains where modeling and simulation are used. (shrink)

The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorphoOrg database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of (...) the mouse, rat or human cerebral cortex. Physical estimates of the total number and type of ions involved in neuronal electric spiking based on the obtained morphometric data, combined with energetics of neurotransmitter release and signaling fueled by glucose consumed by the active brain, support highly efficient cerebral computation performed at the thermodynamically allowed Landauer limit for implementation of irreversible logical operations. Individual proton tunneling events in voltage-sensing S4 protein alpha-helices of Na+, K+ or Ca2+ ion channels are ideally suited to serve as single Landauer elementary logical operations that are then amplified by selective ionic currents traversing the open channel pores. This miniaturization of computational gating allows the execution of over 1.2 zetta logical operations per second in the human cerebral cortex without combusting the brain by the released heat. (shrink)

Whale optimization algorithm, known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array is used as an accelerator, of which the high-level (...) synthesis utilizes open computing language as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization and competitive swarm optimizer are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems. (shrink)

The nature of quantum computation is discussed. It is argued that, in terms of the amount of information manipulated in a given time, quantum and classical computation are equally efficient. Quantum superposition does not permit quantum computers to ''perform many computations simultaneously'' except in a highly qualified and to some extent misleading sense. Quantum computation is therefore not well described by interpretations of quantum mechanics which invoke the concept of vast numbers of parallel universes. Rather, entanglement makes available types of (...) computation processes which, while not exponentially larger than classical ones, are unavailable to classical systems. The essence of quantum computation is that it uses entanglement to generate and manipulate a physical representation of the correlations between logical entities, without the need to completely represent the logical entities themselves. (shrink)

Studying the neural correlates of conscious awareness depends on a reliable comparison between activations associated with awareness and unawareness. One particularly difficult confound to remove is task performance capacity, i.e. the difference in performance between the conditions of interest. While ideally task performance capacity should be matched across different conditions, this is difficult to achieve experimentally. However, differences in performance could theoretically be corrected for mathematically. One such proposal is found in a recent paper by Lamy, (...) Salti and Bar-Haim [Lamy D, Salti M, Bar-Haim Y. Neural correlates of subjective awareness and unconscious processing: an ERP study. J Cognitive Neurosci 2009,21:1435-46], who put forward a corrective method for an electroencephalography experiment. We argue that their analysis is essentially grounded in a version of High Threshold Theory, which has been shown to be inferior in general to Signal Detection Theory. We show through a series of computer simulations that their correction method only partially removes the influence of perfor- mance capacity, which can yield misleading results. We present a mathematical correction method based on Signal Detection Theory that is theoretically capable of removing performance capacity confounds. We discuss the limitations of mathemati- cally correcting for performance capacity confounds in imaging studies and its impact for theories about consciousness. (shrink)

Functional near-infrared spectroscopy has attracted increasing attention in the field of brain–computer interfaces owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network -based approach for implementing a subject-independent fNIRS-based BCI. (...) A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication as well as that obtained using conventional shrinkage linear discriminant analysis. To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. (shrink)

Technologies like passive brain-computer interfaces can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory (...) and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination between low and high levels of workload, vigilance, and stress even for high temporal resolution. Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain. (shrink)

Using P300-based brain–computer interfaces in daily life should take into account the user’s emotional state because various emotional conditions are likely to influence event-related potentials and consequently the performance of P300-based BCIs. This study aimed at investigating whether external emotional stimuli affect the performance of a P300-based BCI, particularly built for controlling home appliances. We presented a set of emotional auditory stimuli to subjects, which had been selected for each subject based on individual valence scores evaluated a priori, (...) while they were controlling an electric light device using a P300-based BCI. There were four conditions regarding the auditory stimuli, including high valence, low valence, noise, and no sound. As a result, subjects controlled the electric light device using the BCI in real time with a mean accuracy of 88.14%. The overall accuracy and P300 features over most EEG channels did not show a significant difference between the four auditory conditions. When we measured emotional states using frontal alpha asymmetry and compared FAA across the auditory conditions, we also found no significant difference. Our results suggest that there is no clear evidence to support a hypothesis that external emotional stimuli influence the P300-based BCI performance or the P300 features while people are controlling devices using the BCI in real time. This study may provide useful information for those who are concerned with the implementation of a P300-based BCI in practice. (shrink)

The spread of aggressive memes on social media is a major problem, demanding solutions for the automatic detection of these undesired memes. However, the line between freedom of expression and the dissemination of aggressive messages is very blurred and difficult to define. Moreover, the detection of aggressive elements in memes presents a significant challenge because memes are mostly multimodal, e.g., a composition of an image with a concise textual content which often lacks a direct connection or correlation. The advent of (...) Generative Models presents an interesting alternative to unravel the inherent meaning of aggressive memes by allowing a fully multimodal analysis. This paper examines the use of Generative Models to detect aggressive memes. The proposed methodology groups aggressive memes from existing datasets according to levels of multimodal analysis difficulty and, together with the creation of prompts via zero-shot, tests and verifies how well these models perform in the task of detecting this type of content and where failures may occur. To validate the methodology, a series of experiments using three models and five datasets was conducted. According to manual annotation, these data were divided into three datasets representing three multimodality reasoning levels, which were submitted to the models to perform the task of identifying the presence of aggressive content. The results show the viability of using generative models in this task, as well as highlight the better performance of some models compared to others. Moreover, a comparison with a non-generative model specially trained on the meme datasets shows that generative models can be competitive in the task of detecting memes with aggressive content as long as the multimodality reasoning level required is not very high. The findings can help academics, managers, and decision-makers develop more effective tools for detecting aggressive content while balancing freedom of expression. The research also serves as a reference for improving moderation systems and guiding future policies in online communication. (shrink)

Today, Service-Oriented Architecture (SOA) and Grid and Cloud computing comprise the key technologies in distributed systems. In systems following the SOA approach, functionalities are delivered and consumed as services. Given the variety of resources (i.e. data, computing capabilities, applications, etc) as well as the variation of user-requested Quality of Service (e.g., high performance, fast access, low cost, high media resolution, etc), there is a need for advanced user management, trust establishment and service management mechanisms which (...) adjust, monitor and evaluate service provision according to the users’ requirements and rights. Within this context, security and privacy requirements have become of great importance, as well the need for flexible and efficient identity management. This editorial of the special issue Identity Management in Grid and SOA discusses the importance of identity management within SOA and Grid environments. A number of techniques and existing systems addressing these issues are presented and evaluated. Identity management is considered in various contexts and at different levels, including service composition level, system level and inter-system communication level. (shrink)

To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static (...) software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value. (shrink)

Artificial intelligence (AI) has captured the interest of multiple actors with speculations about its benefits and dangers. Despite increasing scholarly attention to the discourses of AI, there are limited insights on how different groups interpret and debate AI and shape its opportunities for action. We consider AI an issue field understood as a contested phenomenon where heterogeneous actors assert and debate the meanings and consequences of AI. Drawing on computational social science methods, we analyzed large amounts of text on how (...) politicians (parliamentarians) consultancies (high reputation firms), and lay experts (AI-forum Reddit users) articulate meanings about AI. Through topic modeling, we identified diverse and co-existing discourses: politicians predominantly articulated AI as a societal issue requiring an ethical response, consultancies stressed AI as a business opportunity pushing a transformation-oriented discourse, and lay experts expressed AI as a technical issue shaping a techno-feature discourse. Moreover, our analysis details the hopes and fears within AI discourses, revealing that sentiment varies by actor group. Based on these findings, we contribute new insights about AI as an issue field shaped by the discursive work performed by heterogeneous actors. (shrink)

Regarding the continuous development of high-speed trains and the increase of running speeds, the aerodynamic design of high-speed trains has become significantly important, while reduction of drag and noise comprises a significant challenge in order to optimize aerodynamic design of high-speed trains. The design form factor of a high-speed train is highly influenced by aerodynamic aspects including aerodynamic drag, lift force, and noise. With the high-speed train as the object, the paper aims to take bionic (...) concept as the entry point, selecting the hummingbird as the bionic prototype and extracting bionic elements to establish a bionic train model. Then, the finite volume method was used for numerical simulation and analysis of the aerodynamic performance and aerodynamic noise of the bionic high-speed train. Computational results prove that drag and noise of the bionic head type were lower than those of the original train; drag of the head train of the bionic model was reduced by 2.21% in comparison with the original model, while the whole-train drag was reduced by 3.53%, indicating that drag reduction effects are available and implying that the bionic head type could reduce drag and noise. Noise sources of the bionic train are mainly located at positions with easy airflow separation and violent turbulence motion. Large turbulence energy is in bogie areas and mainly exists at the leeward side of the bogie area. Obviously, the bogie area is the major noise source of the train. Aerodynamic noise of the bionic train in far-field comprises a wide-frequency range. Noises were concentrated within 613 Hz~3150 Hz. When the bionic high-speed train ran at 350 km/h, through comparative analysis of total noise levels at observed points of the high-speed train, it is found that this position with the maximum noise level was 25 m away from the head train nose tip, with the maximum value of 88.4 dB. When the bionic train ran at 600 km/h, the maximum sound pressure level at the longitudinal point was 99.7 dB and the average noise level was 96.6 dB. When the running speed increased from 350 km/h to 600 km/h, the maximum noise level increased by 11.3 dB and the average noise level increased by 11.6 dB. Computation results of aerodynamic noise at the point which is 7.5 m away from the rail center show that the maximum aerodynamic noise level existed at the first-end bogie of the head train, while the noise level was larger at the position closer to the ground. (shrink)

Musical performance is a multimodal experience, for performers and listeners alike. This paper reports on a pilot study which constitutes the first step toward a comprehensive approach to the experience of music as performed. We aim at bridging the gap between qualitative and quantitative approaches, by combining methods for data collection. The purpose is to build a data corpus containing multimodal measures linked to high-level subjective observations. This will allow for a systematic inclusion of the knowledge of music (...) professionals in an analytic framework, which synthesizes methods across established research disciplines. We outline the methods we are currently developing for the creation of a multimodal data corpus dedicated to the analysis and exploration of instrumental music performance from the perspective of embodied music cognition. This will enable the study of the multiple facets of instrumental music performance in great detail, as well as lead to the development of music creation techniques that take advantage of the cross-modal relationships and higher-level qualities emerging from the analysis of this multi-layered, multimodal corpus. The results of the pilot project suggest that qualitative analysis through stimulated recall is an efficient method for generating higher-level understandings of musical performance. Furthermore, the results indicate several directions for further development, regarding observational movement analysis, and computational analysis of coarticulation, chunking, and movement qualities in musical performance. We argue that the development of methods for combining qualitative and quantitative data are required to fully understand expressive musical performance, especially in a broader scenario in which arts, humanities, and science are increasingly entangled. The future work in the project will therefore entail an increasingly multimodal analysis, aiming to become as holistic as is music in performance. (shrink)

This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level (...) technologies, second-layer technologies within those brackets, and minutely analyzes each technology in the second layer. The focus includes a comprehensive examination of the evolving landscape of zoonotic disease control and the influence of computational tools in public health. The findings highlight the potential of leveraging computational frameworks to predict, monitor, and control zoonotic diseases more effectively within Society 5.0. The major identified thrust areas include One Health and governmental integration, digital and data-driven solutions, healthcare and medical technologies, technologies for communication and information dissemination, and socio-cultural and ethical considerations. The integration of large-scale healthcare services offer a robust data source for AI-driven models, enabling early detection and dynamic response to zoonotic threats using data acquired from IoT surveillance and data acquisition infrastructures. However, challenges such as data privacy and equitable healthcare access need addressing. The study emphasizes the opportunities arising from the integration of computational frameworks with Society 5.0 for zoonotic disease control, proposing potential benefits in terms of improved disease surveillance and response. To fully realize this potential, the study suggests exploring specific technological landscapes for further investigation in the context of zoonotic disease control. (shrink)

What principles and mechanisms allow humans to encode complex 3D information, and how can it be so fast, so accurately and so flexibly transformed into coordinated action? How do these processes work when developed to the limit of human physiological and cognitive capacity—as they are in high-speed sports, such as alpine skiing or motor racing? High-speed sports present not only physical challenges, but present some of the biggest perceptual-cognitive demands for the brain. The skill of these elite athletes (...) is in many ways an attractive model for studying human performance “in the wild”, and its neurocognitive basis. This article presents a framework theory for how these abilities may be realized in high-speed sports. It draws on a careful analysis of the case of the motorsport athlete, as well as theoretical concepts from: (1) cognitive neuroscience of wayfinding, steering, and driving; (2) cognitive psychology of expertise; (3) cognitive modeling and machine learning; (4) human-in-the loop modellling in vehicle system dynamics and human performance engineering; (5) experimental research (in the laboratory and in the field) on human visual guidance. The distinctive contribution is the way these are integrated, and the concept ofchunkingis used in a novel way to analyze a high-speed sport. The mechanisms invoked are domain-general, and not specific to motorsport or the use of a particular type of vehicle (or any vehicle for that matter); theegocentric chunking hypothesisshould therefore apply to any dynamic task that requires similar core skills. It offers a framework for neuroscientists, psychologists, engineers, and computer scientists working in the field of expert sports performance, and may be useful in translating fundamental research into theory-based insight and recommendations for improving real-world elite performance. Specific experimental predictions and applicability of the hypotheses to other sports are discussed. (shrink)

This article discusses the properties of a controllable, flexible, hybrid parallel computing architecture that potentially merges pattern recognition and arithmetic. Humans perform integer arithmetic in a fundamentally different way than logic-based computers. Even though the human approach to arithmetic is both slow and inaccurate it can have substantial advantages when useful approximations are more valuable than high precision. Such a computational strategy may be particularly useful when computers based on nanocomponents become feasible because it offers a way to (...) make use of the potential power of these massively parallel systems. Because the system architecture is inspired by neuroscience and is applied to cognitive problems, occasional mention is made of experimental data from both fields when appropriate. (shrink)

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: (...) causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms. Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of “downward causation” can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain. (shrink)

The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has (...) a strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and FCM, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved FCM algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing FCM algorithms. (shrink)

The twenty-first century is arguably the century of computing. In such a world saturated by computing, Computational Thinking is now recognized as a foundational competency for being an informed citizen and being successful in STEM work. Nevertheless, how to effectively import different types of teaching methods in university courses is subjected to further evaluation. Currently, the arguments in favor of tangible robots including high interaction, great practicality, and specific operation results make themselves to be often used as (...) a teaching medium and tool for teaching activities between teachers and students. Hence, in addition to cultivating students with computational thinking ability, this paper discussed how to integrate tangible robots into project-based learning courses of thinking skills training to improve the learning performance of the computational thinking ability. This study conducted in one semester on the 105 students from three classes. Experimental results show that the project-based learning method integrated with the teaching material of robotic visual programs approach had significantly better effectiveness in improving students' learning achievements than the traditional teaching method integrated with paper practice teaching materials approach. Analysis of the questionnaire results showed that the proposed learning approach did not increase the students' cognitive burden. In sum, the proposed approach helps students' learning achievement and cognitive load. (shrink)

Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in (...) agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization model quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%–80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework. (shrink)

Data center technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines and social networking applications. Every application has its (...) parameters, like latency and blocking in the DC network. Every data center network has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN. (shrink)

Artificial intelligence (AI) is rapidly changing the way that art is created and consumed, allowing artists to create unique, engaging works with high computing power that can supplement their creative process. This manuscript explores the creative process of using AI technology in digital art to create paintings and evaluates creativity based on the aesthetic value and components of works created by AI. This research seeks to understand how AI technology influences the art world through a practice-led methodology with (...) a descriptive qualitative approach and observational methodology. Pioneering artwork such as Edmond de Belamy, created by French collective obvious using generative adversarial networks (GANs), has raised questions among the creative fields concerning originality, creativity, and authorship. Through analyses of AI-generated artwork and comparison with traditional artwork, the research investigates potential opportunities and challenges that AI presents to the art world. Ultimately, AI technology can provide a creative spark for human artists who, with an appropriate understanding of the limitations of AI algorithms, can create incredible pieces of art that embrace the contributions of both humans and machines. This research seeks to fill in the gaps left by earlier works and provide a more multi-faceted look at the changing landscape of AI in art, gaining a better understanding of the ways AI is impacting the art world and what the future may look like in the coming years. (shrink)

Energy contributes significantly in almost all aspects of human life as well as economic activities and plays a crucial role in the infrastructural development of a county to alleviate poverty. Generating energy from a renewable source such as small hydropower through the application of pump operating as a turbine mode called Pump as Turbine is one of the best alternatives to provide clean and inexpensive energy. Using Pump as Turbine helps in generating reasonably priced hydroelectric power for communities in underdeveloped (...) counties. This study investigates the effects of internal flow behaviour and performance of Pump as Turbine under different rotational speed and flow rate. The rotational speed is an essential physical parameter as it affects the Pump as Turbine operation. A model-specific speed centrifugal pump model with head 32, flow rate of 12.5 and the rotational speed of 2900 rpm, has been selected for the study. Numerical simulations have been conducted using the k-ω turbulence model to solve three-dimensional equations. The pump mode experimental data were used to confirm the results for better analysis. The results predicted that vortex and turbulent kinetic energy increase per rotational speed increase. Also, at the higher rotational speed, very high recirculation of flow is detected at the blade suction chamber, although the pressure side has a smooth flow. This study provides beneficial information which will serve as a reference to help improve PAT performance along with selecting PAT for a small hydropower site. Future works will consider the impact of blade thickness and cavitation in Pump as Turbine. (shrink)

This essay will discuss the hegemonic role that texts have come to play in the historiography of subcontinental mathematical traditions. It will argue that texts need to be studied as records of practices of people's working lives, grounded in social hierarchies. We will take particular mathematical texts to show how different occupational registers have come to shape practices that defy the binaries of concrete and abstract, high and low mathematics or the pure and applied conundrum. Measuring, counting and accounting (...) practices as part of the routine work of practitioners performing their caste occupations then provide us with a spectrum of the computational activities that controlled and regulated the lives of people in the past. In the process the act of computing itself gained certain political values such as cunning and manipulation, identified with professions of village accountant and merchant, for example. Drawn from my earlier work on these records, I discuss the occupational role of the accountant as a political functionary who assessed and authenticated the measurements of land and produce in the village, making values of the labor performed by others, and creating avenues for his own proficiency as a mathematical practitioner. (shrink)