The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including: graph partitioning, centrality, (...) assortativity, hierarchy and structural balance. The analysis yields various patterns. First, we observe that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat camp; the overall campaign was much focused on economy and rights; the Republican Party is the most divisive subject in the campaign, and is portrayed more negatively than the Democrats; and, overall, the media reported positive statements more frequently for the Democrats than the Republicans. This is the first study in which political positions are automatically extracted and derived from a very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts. (shrink)

The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes and (...) 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction. (shrink)

Diffusion MRI is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification of the dMRI volumes or focus on detecting a single type (...) of artifact. In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive sub-volumes or “slabs” extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development and 4,226 from the Healthy Brain Network dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy and participant age, to test whether the use of our model improves the brain-age association. (shrink)

The Spiral Discovery Method was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The (...) behavior of the generalized SDM model, referred to as the Spiral Discovery Network, and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today. (shrink)

The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS and neural network technologies. This framework can automatically obtain the values of spatial factors (...) affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities. (shrink)

Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq deep neural network, such as (...) a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks. (shrink)

The aim of this study is to analyze the use of the Automation and networking among the faculty members and the students of engineering college libraries in Sri Venkateswara University Area. A well structured questionnaire was distributed among the 1314 faculty members and the students under study. The present study demonstrates and elaborates the various aspects of the Automation status, Categories of software used, Areas of Computerization, Source finance for their library automation work, automated services, topology of (...) computer networking is used, to know the types of networks used, satisfaction with digital library services. It was found that the Automation and Networking has become a vital instrument for teaching, research and learning process for these respondents. Some suggestions are set forth to make the services more beneficial for the academic community of the engineering colleges under study. Key words: Automation, Network, Topology. (shrink)

Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their (...) niche to replace some job loss in other sectors? In this paper, we compare the problems created by the current mass production economy and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human–machine production at the microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation. (shrink)

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the (...) central role humans have to play in manufacturing systems, the design of which must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of Things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different human social dimensions associated with CPS and IoT and focuses on their conceptual evolution regarding automated production systems’ sociability, notably by bringing humans back in the loop. Hereby, this paper aims to take stock of current research trends to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. Consequently, different models of sociability as a way to integrate humans in the broad sense and/or the develop future automated production systems have been identified from the literature and analysed. (shrink)

Connected devices, such as smartphones and tablets, are exposed to a large variety of attacks. Their protection is often challenged by their resource constraints in terms of CPU, memory and energy. Security chains, composed of security functions such as firewalls, intrusion detection systems and data leakage prevention mechanisms, offer new perspectives to protect these devices using software-defined networking and network function virtualization. However, the complexity and dynamics of these chains require new automation techniques to orchestrate them. This chapter (...) describes an automated orchestration methodology for security chains in order to secure connected devices and their applications. This methodology exploits process learning to establish behavioral models and infer security constraints represented as logical predicates. It then generates and merges a set of chains of security functions on the basis of these predicates. These chains are finally compiled into low-level configuration rules and deployed into the network, optimizing for the underlying topology. The benefits and limits of such a methodology combining machine learning and verification techniques are evaluated by a set of experimental results. (shrink)

Large language models and other generative artificial intelligence systems are achieving increasingly impressive results, though the quality of those results still seems dull and uninspired. This paper argues that this poor quality can be linked to the philosophical notion of inauthenticity as presented by Kierkegaard, Nietzsche, and Heidegger, and that this inauthenticity is fundamentally grounded in the design and structure of such systems by virtue of the way they statistically level down the materials on which they are trained. Although it (...) seems possible to create the conditions for authenticity in these systems, the resulting authenticity would be grounded in machine intelligence, not human intelligence. The argument extends the criticisms of artificial intelligence articulated by Hubert Dreyfus, updated to account for recent developments in machine learning and artificial neural networks. While more optimistic concerning the prospects for successfully creating artificial intelligence than Dreyfus had been, this paper argues that the resulting intelligence may not align well with human intelligence and may not be desirable for humans, if it is fully authentic. (shrink)

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in depth. We demonstrate that (...) systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)

Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this paper calls for research that studies empirically and theoretically how machine learning experts and users seek (...) to attain machine learning explainability. Focusing on automated trading, we take steps in this direction by analyzing a trading firm’s quest for explaining its deep neural network system’s actionable predictions. We demonstrate that this explainability effort involves a particular form of human–machine interaction that contains both anthropomorphic and technomorphic elements. We discuss this attempt to attain machine learning explainability in light of reflections on cross-species companionship and consider it an example of human–machine companionship. (shrink)

The paper demonstrates that social alignment is distinct from value alignment as it is currently understood in the AI safety literature, and argues that social alignment is an important research agenda. Work provides an important example for the argument, since work is a cooperative endeavor, and it is part of the larger manifold of social cooperation. These cooperative aspects of work are individually and socially valuable, and so they must be given a central place when evaluating the impact of AI (...) upon work. Workplace technologies are not simply instruments for achieving productive goals, but ways of mediating interpersonal relations. They are aspects of a cooperative interface i.e. the infrastructure by which we engage cooperative behavior with others. The concept of the cooperative interface suggests two conjectures to foreground in the social alignment agenda, motivated by the experience of algorithmic trading and social robotics: that AI impacts cooperation through its effects on social networks, and through its effects on social norms. (shrink)

Dementia, a prevalent disorder of the brain, has negative effects on individuals and society. This paper concerns using Spontaneous Speech Challenge of Interspeech 2020 to classify Alzheimer's dementia. We used VGGish, a deep, pretrained, Tensorflow model as an audio feature extractor, and Scikit-learn classifiers to detect signs of dementia in speech. Three classifiers were 59.1% accurate, which was 3% above the best-performing baseline models trained on the acoustic features used in the challenge. We also proposed DemCNN, a new PyTorch raw (...) waveform-based convolutional neural network model that was 63.6% accurate, 7% more accurate then the best-performing baseline linear discriminant analysis model. We discovered that audio transfer learning with a pretrained VGGish feature extractor performs better than the baseline approach using automatically extracted acoustic features. Our DepCNN exhibits good generalization capabilities. Both methods presented in this paper offer progress toward new, innovative, and more effective computer-based screening of dementia through spontaneous speech. (shrink)

Over the past decade, unmanned aerial vehicles (UAVs) have received increasing attention and are being used in the areas of harvesting, videotaping, and the military industry. In this article, the consideration is focused on areas where video recording is required for ground inspections. This paper describes modern communication technologies and systems that enable interaction and data exchange between UAVs and a ground control station (GCS). This article focuses on different architectures of communication systems, establishing the characteristics of each to identify (...) the preferred architecture that does not require a significant consumption of resources and whose data transmission is reliable. A coherent architecture that includes multiple UAVs, wireless sensor networks, cellular networks, GCSs, and satellite network to duplicate communications for enhanced system security has been offered. Some reliability problems have been discussed, the solution of which was suggested to be a backup connection via satellite, i.e., a second connection. This study focused not only on the communication channels but also on the data exchanged between system components, indicating the purpose of their application. Some of the communication problems and shortcomings of various systems, as well as further focus areas and improvement recommendations were discussed. (shrink)

An emerging discourse about automated agricultural machinery imagines farms as places where farmers and workers do not need to be, but also implicitly frames farms as intolerable places where people do not want to be. Only autonomous machines, this story goes, can relieve farmers and workers of this presumed burden by letting them ‘farm at a distance’. In return for this distanced autonomy, farmers are promised increased control over their work-life balance and greater farm productivity from letting ‘smart’ robots assume (...) control over the operational environment. Drawing upon the ways that these machines are promoted by manufacturers in various media, we trace the nascent contours of what we term a liberatory sociotechnical imaginary for agricultural automation across three cases—automated milking systems, self-driving tractors, and robotic strawberry pickers. We show how promises for new freedoms and autonomy are flexibly deployed to respond to distinct frictions that farmers, workers, and even farm animals experience in different modes of industrial agriculture. However, underlying these promises is the purposefully understated self-interest of manufacturers, who stand to gain further control over farms if automated technologies assume a central role in agriculture. Through the liberatory rhetoric, we contend, the imaginary seeks to enroll farmers into a socio-technical network that creates new relations of dependence upon the companies who design, sell, maintain, and often retain ownership over automated technologies. While potentially powerful, this imaginary may nonetheless fail to coalesce as farmers, workers, and agroecosystems exert their own agency on automated imaginaries and technological futures for agriculture. (shrink)

The automated identification of national implementations of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (...) to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets. (shrink)

Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex parameterization and costly computation, and it tends to disconnect with existing psychological accounts of moralization. We present a simple cognitive model for moral inference, Moral Association Graph (MAG), inspired by psychological work on moralization. Our model builds on word association network for inferring moral relevance (...) and draws on rich psychological data. We demonstrate that MAG performs competitively to state-of-the-art language models when evaluated against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. We also show that our model yields interpretable outputs and is applicable to informing short-term moral change. (shrink)

Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The (...) Guardian, Wired, and Gizmodo. The main goals are to identify dominant emphasis frames in AI news reporting over the past decade, to explore whether certain A.I. frames are associated with specific data risks, and what journalists and experts contribute to the media discourse. An automated content analysis serves for inductive frame detection, identification of risk references, and network analysis of news writers. The results show how A.I.’s ubiquity emerged rapidly in the mid-2010s, and that the news discourse became more critical over time. It is further argued that A.I. news reporting is an important factor in building critical data literacy among lay audiences. (shrink)

The somewhat old-fashioned concept of philosophical categories is revived and put to work in automated ontology building. We describe a project harvesting knowledge from Wikipedia’s category network in which the principled ontological structure of Cyc was leveraged to furnish an extra layer of accuracy-checking over and above more usual corrections which draw on automated measures of semantic relatedness.

The possibility of interacting with remote services in natural language opens up new opportunities for sharing knowledge and for automating services. Easy-to-use, text-based interfaces might provide more democratic access to legal information, government services, and everyday knowledge as well. However, the methodology of engineering robust natural language interfaces is very diverse, and widely deployed solutions are still yet to come. The main contribution is a detailed problem analysis on the theoretical level, which reveals that a text-based interface is best understood (...) as an artificial agent that represents the interests of the remote party who is separated in time and space from the client. A possible ethical issue about the development of such an agent is also discussed. (shrink)

This work provides a conceptual foundation for a Bayesian approach to artificial inference and learning. I argue that Bayesian confirmation theory provides a general normative theory of inductive learning and therefore should have a role in any artificially intelligent system that is to learn inductively about its world. I modify the usual Bayesian theory in three ways directly pertinent to an eventual research program in artificial intelligence. First, I construe Bayesian inference rules as defeasible, allowing them to be overridden in (...) certain contexts and therefore allowing them to play a part in a hybrid system, coexisting with non-Bayesian modes of inference. I take seriously the need to find meaningful prior probabilities for hypotheses, and elaborate means for supplying an artificial intelligence with such. And I address the computational complexity of Bayesian inference by reference to simplifications using causal networks and by allowing the probabilistic acceptance of hypotheses and subsequent qualitative inference to supplement Bayesian reasoning. The result is a Pragmatic Bayesian model of induction. (shrink)

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...) two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology. (shrink)

Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks to aid in the detection of cardiac arrhythmias. The data collected by (...) an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat, left bundle branch block beat, right bundle branch block beat, and premature ventricular contraction. The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems. (shrink)

This article compares two cultures of engineering design, one flexible and interactive, the other rigid and hierarchical. It examines the practices of design engineers who use a mixture of paper documents and computer graphics systems and contrasts these with the practices of workers reengineering their own work process and its technological support system, using predesigned software. Based on the idea from actor network theory that objects participate in the shaping of new technologies and the networks that build them, the (...) study reveals that design cultures are intrinsically tied to the way in which their representations are constructed because such representations—sketches, drawings, prototypes—are the heart of design work; such design tools can engage or restrict participation in the design process; politics in the form of management prerogatives can be built into a design tool, influencing the range of creativity allowed and innovation accomplished in a given sociotechnological setting. (shrink)

The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in a (...) pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually. (shrink)

A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but (...) other versions are formally defined systems of logic. Following are six of the most common kinds of semantic networks, each of which is discussed in detail in one section of this article. 1. Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true. (shrink)

This paper proposes a conceptual framework for evaluating how social networking platforms fare as epistemic environments for human users. I begin by proposing a situated concept of epistemic agency as fundamental for evaluating epistemic environments. Next, I show that algorithmic personalisation of information makes social networking platforms problematic for users’ epistemic agency because these platforms do not allow users to adapt their behaviour sufficiently. Using the tracing principle inspired by the ethics of self-driving cars, I operationalise it here and identify (...) three requirements that automated epistemic environments need to fulfil: (a) the users need to be afforded a range of skilled actions; (b) users need to be sensitive to the possibility to use their skills; (c) the habits built when adapting to the platform should not undermine the user’s pre-existing skills. I then argue that these requirements are almost impossible to fulfil all at the same time on current SN platforms; yet nevertheless, we need to pay attention to these whenever we evaluate an epistemic environment with automatic features. Finally, as an illustration, I show how Twitter, a popular social networking platform, will fare regarding these requirements. (shrink)

We are today agents of a peculiar reality, the global network or the system for automated information production. Our condition in the global network is that of agents of the real, since we all contribute to the coproduction of this ever-evolving process. Nevertheless, I will argue, this reality is but the effect of the adoption of a notion of instrumental pragmatic rationality which denies the existence of any other possible reality as the actualization of different determinations of Reason. (...) While following Deleuze s notion of deterritorialization, I ll show that the real philosophical choice does not concern the introduction of new moves that create differentiations within a universal game, but that it is concerned with the possible abolition of the agency which is instantiated within the collective network. (shrink)

Background: Detecting and classifying fish diseases is crucial for maintaining the health and sustainability of aquaculture systems. This study employs deep learning techniques, particularly Convolutional Neural Networks (CNNs), to automate the detection of various fish diseases using image data. Methods: The study utilizes a carefully curated dataset sourced from the Kaggle database, comprising images representing seven distinct types of fish diseases, along with images of healthy fish. Data preprocessing techniques, including resizing, rescaling, denoising, sharpening, and smoothing, are applied to enhance (...) image quality and facilitate accurate disease detection. Data augmentation is employed to increase the model's ability to generalize to unseen data. The CNN architecture is designed with cascading convolutional layers, ReLU activation functions, and pooling operations to extract high-level features associated with fish infections. The model architecture, implemented using the Keras Sequential API, includes convolutional layers, max pooling layers, and densely connected layers for classification. Results: Experimental results demonstrated promising performance across various disease categories, with high accuracy and balanced precision and recall values for most classes. The study also discussed the impact of climate change on fish disease incidence and underscores the importance of effective monitoring and management practices facilitated by technological innovations such as Big Data, IoT, sensors, and robotics in ensuring sustainable fisheries management. (shrink)

Computer assisted theorem proving is an increasingly important part of mathematical methodology, as well as a long-standing topic in artificial intelligence (AI) research. However, the current generation of theorem proving software have limited functioning in terms of providing new proofs. Importantly, they are not able to discriminate interesting theorems and proofs from trivial ones. In order for computers to develop further in theorem proving, there would need to be a radical change in how the software functions. Recently, machine learning results (...) in solving mathematical tasks have shown early promise that deep artificial neural networks could learn symbolic mathematical processing. In this paper, I analyze the theoretical prospects of such neural networks in proving mathematical theorems. In particular, I focus on the question how such AI systems could be incorporated in practice to theorem proving and what consequences that could have. In the most optimistic scenario, this includes the possibility of autonomous automated theorem provers (AATP). Here I discuss whether such AI systems could, or should, become accepted as active agents in mathematical communities. (shrink)

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the (...) above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset. (shrink)

This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. (...) The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN’s ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool. (shrink)

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the (...) classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes. (shrink)

The common law system is a legal system that values precedent, or previous court decisions, in the resolution of current cases. As the availability of legal documents in digital form has increased, it has become more difficult for legal professionals to manually identify relevant past cases due to the vast amount of data. Researchers have developed automated systems for determining the similarity between legal documents to address this issue. Our research explores various representations of a legal document and discusses a (...) novel paragraph filtering process to identify key paragraphs using legal citation information to remove unnecessary text paragraphs without disturbing the concept of the legal document. State-of-the-art techniques like TF-IDF, BERT, Legal Bert, Doc2Vec, and Legal-longformer are used for the performance analysis of the proposed approach with document comparison. It has been shown that a model trained on the proposed filtered paragraphs can achieve better results than a model trained on the complete text and can also shorten the document by over 40%. The proposed filtering strategy could be helpful for models like BERT, where the maximum token length is fixed. (shrink)

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice (...) with multisource data, in which the impact of the network time-varying state on passenger path choice is fully considered. First, according to the number and characteristics of OD candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice. (shrink)

In many social contexts, social influence seems to be inescapable: the behavior of others influences us to modify ours, and vice-versa. However, social psychology is full of examples of phenomena where individuals experience a discrepancy between their public behavior and their private opinion. This raises two central questions. First, how does an individual reason about the behavior of others and their private opinions in situations of social influence? And second, what are the laws of the resulting information dynamics? In this (...) paper, we address these questions by introducing a formal framework for representing reasoning about an individual’s private opinions and public behavior under the dynamics of social influence in social networks. Moreover, we dig deeper into the involved information dynamics by modeling how individuals can learn about each other based on this reasoning. This compels us to introduce a new formal notion of reflective social influence. Finally, we initialize the work on proof theory and automated reasoning for our framework by introducing a sound and complete tableaux system for a fragment of our logic. Furthermore, this constitutes the first tableau system for the “Facebook logic” of J. Seligman, F. Liu, and P. Girard. (shrink)

This study decodes consumers' preference levels using a convolutional neural network in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the (...) proposed method are the main advantages. The experimental procedure required eight healthy participants to view commercial advertisement videos of different durations. The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1%, 87.4%, 85.2%, and for male 85.7, 88.4, 85.1%, respectively. (shrink)

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory, for emotion recognition that takes into account (...) five main emotions. We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information. (shrink)

In the field of applied computer research, the problem of the reinforcement of existing inequalities through the processing of “big data” in neural networks is typically addressed via concepts of representation and fairness. These approaches, however, tend to overlook the limits of the liberal antidiscrimination discourse, which are well established in critical theory. In this paper, I address these limits and propose a different framework for understanding technologically amplified oppression departing from the notion of “mute compulsion” (Marx), a specifically modern (...) form of power based on the social abstraction of wage labor in market exchange. I begin with a reconstruction of the process of technological abstraction in automated pattern recognition, after which I summarize the conceptional shortcomings of representational fairness discourse. In contrast, I demonstrate how the social reproduction of exploitation is conceptualized in critical theory as being mediated through a process of social abstraction, defined as a social practice of reification that manifest in fetishization. To avoid economistic shortcomings, I contextualize the argument of social abstraction against the backdrop of social reproduction theory, which takes the juncture of various historical forms of oppression into account. I conclude by elaborating how technological abstraction corresponds to the problem of mute compulsion by replicating processes of social abstraction. (shrink)

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL‐E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives (...) suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology. (shrink)

Copy of a copy of a copy of a copy of a copy of … Trent Reznor’s formula sounds so much more exciting and seductive than two millennia-old formula of representation and identity also known as A=A. Oversubscribed to central perspective, concerned with clarity and distinctness too content with the content of the image and strongly bonded with its apparatus, photography of the past century repeatedly failed to see the invisible, still acting as a copy of some ideal original, assuming (...) this role from painting. If the painting managed to free itself from representation, what can photography do? With new technologies of image production and dissemination, the pillars of the image of thought begin to crack, with identity and recognition losing the centre stage to difference and repetition. Through the emergence of the ability to photograph everywhere, automated camera and ubiquitous digital dissemination of images, photography is suddenly revealed as the medium of a copy, of uncountable recurrences of the same, and with all of its subjects and objects becoming simulacra, rendering only difference as real and originary. (shrink)

This essay argues that through a new and radical relationship with digital technologies that are oriented towards networking and automaticity, humans have become estranged from what philosopher Arnold Gehlen termed the ‘circle of action’ that expressed our ancient adaptation to tool use and constituted the basis for our capacity for reflective consciousness. The objectification of the material and analogue relationship that enabled humans to ‘act’ upon the world and to construct the basis for our collective endeavours, this paper shows, is (...) beginning to redirect the social orientation that tool use enabled, back to a form of individualism I term a ‘post-modern vanity’. Computer-driven automation on a networked and global scale is engendering a new illusion regarding our relationship with technology. Greek philosophy, later religion, and then the Enlightenment all saw our relation to technology as evidence of either our ‘specialness’ or our inherent ‘mastery over nature’. These perspectives resulted in our acquiring a ‘reasoned self-delusion’ wherein for the individual life had meaning and was ontologically tolerable—we thought ourselves to be of a realm beyond raw nature. Automation, however, to an unprecedented degree, objectifies and exteriorizes the processes of technology use, alienating the individual from the ‘circle of action’. This process is throwing us back to another form of what this essay terms a ‘reasoned self-delusion’ where we increasingly believe that we no longer need each other so much. The cult of computer efficiency and the virtual sphere of the network that it has spawned, I argue, is a largely barren sphere of radical individualism, a post-modern vanity, where we exist at a borderland closer to nature and to the animals than we have been for thousands of years. (shrink)

With automation of routine decisions coupled with more intricate and complex information architecture operating this automation, concerns are increasing about the trustworthiness of these systems. These concerns are exacerbated by a class of artificial intelligence that uses deep learning, an algorithmic system of deep neural networks, which on the whole remain opaque or hidden from human comprehension. This situation is commonly referred to as the black box problem in AI. Without understanding how AI reaches its conclusions, it is (...) an open question to what extent we can trust these systems. The question of trust becomes more urgent as we delegate more and more decision-making to and increasingly rely on AI to safeguard significant human goods, such as security, healthcare, and safety. Models that “open the black box” by making the non-linear and complex decision process understandable by human observers are promising solutions to the black box problem in AI but are limited, at least in their current state, in their ability to make these processes less opaque to most observers. A philosophical analysis of trust will show why transparency is a necessary condition for trust and eventually for judging AI to be trustworthy. A more fruitful route for establishing trust in AI is to acknowledge that AI is situated within a socio-technical system that mediates trust, and by increasing the trustworthiness of these systems, we thereby increase trust in AI. (shrink)

Automation and information technology have transformed the organization of labor to such an extent that the processes of exploitation have moved beyond the labor class and now work upon society as a whole. If this displacement has destroyed the political primacy of the labor class, it has not, however, eliminated exploitation; rather, it has broadened it, implanting it within the given conditions of the most diverse spheres of society. -- from The Winter Is Over In late 1995, in opposition (...) to the conservative agenda of Jacques Chirac and his prime minister Alain Juppé and their proposed widespread welfare cuts, French students rose up against their government; public sector workers, together with all the major trade unions, went on strike. When railway workers and Paris Metro personnel joined in the protests, France's public transportation system came to a halt. These extensive social upheavals, the likes of which had not been seen in France since 1968, found widespread public support and fuelled the creation of many political organizations. Chirac backed down from restructuring the public retirement system. Antonio Negri's _T_he Winter is Over comes out of the glimmer of optimism created by the events of 1995, when the long, cold season of neoliberalism, Thatcherism, Reaganomics, reaction, and counterrevolution appeared to have run its course. Published in Italian in 1996, _The Winter is Over_ brings together a series of articles, speeches, and other documents written by Negri between 1989 and 1995 at the threshold of this thaw. It offers a revealing and wide-reaching account of those years of change and brink-of-change, focusing on such topics as the networks of social production, the decline of "limp thought," the end of applied socialism, the Gulf War, and, finally, Italy's transition to its so-called "Second Republic," as seen by an exile. (shrink)

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes (...) “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)