The connected and acyclic components contained in a network are identified by the computation of its complexity, where complexity of a network refers to the total number of spanning trees present within. The article in hand deals with the enumeration of the complexity of various networks’ operations such as sum, product, difference K 2, n ⊖ K 2, and the conjunction of S n with K 2. All our computations have been concluded by implementation of the methods (...) of linear algebra and matrix theory. Our derivations will also be highlighted with the assistance of 3D plots at the end of this article. (shrink)

In Belnap's useful 4-valued logic, the set 2 = {T, F} of classical truth values is generalized to the set 4 = ������(2) = {Ø, {T}, {F}, {T, F}}. In the present paper, we argue in favor of extending this process to the set 16 = ᵍ (4) (and beyond). It turns out that this generalization is well-motivated and leads from the bilattice FOUR₂ with an information and a truth-and-falsity ordering to another algebraic structure, namely the trilattice SIXTEEN₃ with an (...) information ordering together with a truth ordering and a (distinct) falsity ordering. Interestingly, the logics generated separately by the algebraic operations under the truth order and under the falsity order in SIXTEEN₃ coincide with the logic of FOUR₂, namely first degree entailment. This observation may be taken as a further indication of the significance of first degree entailment. In the present setting, however, it becomes rather natural to consider also logical systems in the language obtained by combining the vocabulary of the logic of the truth order and the falsity order. We semantically define the logics of the two orderings in the extended language and in both cases axiomatize a certain fragment comprising three unary operations: a negation, an involution, and their combination. We also suggest two other definitions of logics in the full language, including a bi-consequence system. In other words, in addition to presenting first degree entailment as a useful 16-valued logic, we define further useful 16-valued logics for reasoning about truth and (non-)falsity. We expect these logics to be an interesting and useful instrument in information processing, especially when we deal with a net of hierarchically interconnected computers. We also briefly discuss Arieli's and Avron's notion of a logical bilattice and state a number of open problems for future research. (shrink)

The emergence of an autocatalytic network from an available set of elements is a fundamental step in early evolutionary processes, such as the origin of metabolism. Given the set of elements, the reactions between them (chemical or otherwise), and with various elements catalysing certain reactions, a Reflexively Autocatalytic F-generated (RAF) set is a subset R′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$'$$\end{document} of reactions that is self-generating from a given food set, and with each reaction in R′\documentclass[12pt]{minimal} (...) \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$'$$\end{document} being catalysed from within R′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$'$$\end{document}. RAF theory has been applied to various phenomena in theoretical biology, and a key feature of the approach is that it is possible to efficiently identify and classify RAFs within large systems. This is possible because RAFs can be described as the (nonempty) subsets of the reactions that are the fixed points of an (efficiently computable) interior map that operates on subsets of reactions. Although the main generic results concerning RAFs can be derived using just this property, we show that for systems with at least 12 reactions there are generic results concerning RAFs that cannot be proven using the interior operator property alone. (shrink)

Networked service organisations are increasingly adopting a ‘smarter networking’ philosophy in their design of more agile and customer-focused supply models. Changing consumer behaviours and the emergence of transformative technologies—industry 4.0, artificial intelligence, big data analytics, the Internet of Things—are driving a series of innovations, in terms of ‘products’ and business models, with major implications for the industrial enterprise, in their design of more ‘digitalised’ supply chains. For B2B systems, emerging ‘product-service’ offerings are requiring greater visibility, alignment and integration across an (...) increasingly complex network of multiple partners and collaborators, in order to deliver a better service and customer ‘experience’. To support the design and operation of these multi-organisational service networks, we outline a concept of operations architecture here, underpinned by the literature and network theory, and demonstrate application using a series of exemplar case studies. Focusing on relational elements and the processes key to network integration within service supply networks, the cases inform a set of operating principles and protocols—applicable to all stakeholders ‘cooperating’, within a ‘shared’ environment. Equally critical is to understand how digital technologies may influence future operating philosophies. This article extends our theoretical understanding of network organisations, from a traditional ‘product’ perspective to that of ‘services’, and presents the case for developing a common, unified approach to designing diverse forms of multi-partner service networks. (shrink)

Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces. While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit. Forays into translating BCI systems to children in recent years have revealed that kids can learn to operate simple BCI with proficiency (...) akin to adults. BCI could bring significant boons to the lives of many children with severe physical impairment, supporting their complex physical and social needs. However, children have been neglected in BCI research and a collaborative BCI research community is required to unite and push pediatric BCI development forward. To this end, the pediatric BCI Canada collaborative network was formed, under a unified goal to cooperatively drive forward pediatric BCI innovation and impact. This article reflects on the topics and discussions raised in the foundational BCI-CAN meeting held in Toronto, ON, Canada in November 2019 and suggests the next steps required to see BCI impact the lives of children with severe neurological disease and their families. (shrink)

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between (...) factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image, is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors. (shrink)

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The (...) article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks. (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)

We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for (...) each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument’s dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences’ formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole. We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics. (shrink)

Achieving meaningful usage of the Internet is more than attaining access: multiple social and technological insufficiencies must be overcome and continually readdressed. A wide variety of approaches have been undertaken to address these issues, both to enable individuals to cross the ‘digital divide’ and also to enhance community interactions. In this paper, we focus on one approach–grassroots networked communities. These are communities of locality that have developed their own computer network infrastructure with minimal external support. We analyse eight (...) examples from the UK and present survey results, identifying key characteristics and modes of operation. We argue that such initiatives may offer a viable method of overcoming multiple digital insufficiencies and ensure sustainable and meaningful ICT usage. Further research has commenced to analyse how the presence of social software and near-ubiquitous computer network access affects the sharing and storage of information within communities of locality. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...) styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that the idea of falsification is central to the methodology of machine learning. It is commonly thought that machine learning algorithms infer general prediction rules from (...) past observations. This is akin to a statistical procedure by which estimates are obtained from a sample of data. But machine learning algorithms can also be described as choosing one prediction rule from an entire class of functions. In particular, the algorithm that determines the weights of an artificial neural network operates by empirical risk minimization and rejects prediction rules that lack empirical adequacy. It also exhibits a behavior of implicit regularization that pushes hypothesis choice toward simpler prediction rules. We argue that taking both aspects together gives rise to a falsificationist account of artificial neural networks. (shrink)

An astonishingly diverse biomolecular circuitry orchestrates the functioning machinery underlying every living cell. These biomolecules and their circuits have been engineered not only for various industrial applications but also to perform other atypical functions that they were not evolved for—including computation. Various kinds of computational challenges, such as solving NP‐complete problems with many variables, logical computation, neural network operations, and cryptography, have all been attempted through this unconventional computing paradigm. In this review, we highlight key experiments across three (...) different ‘‘eras’’ of molecular computation, beginning with molecular solutions, transitioning to logic circuits and ultimately, more complex molecular networks. We also discuss a variety of applications of molecular computation, from solving NP‐hard problems to self‐assembled nanostructures for delivering molecules, and provide a glimpse into the exciting potential that molecular computing holds for the future. Also see the video abstract here: https://youtu.be/9Mw0K0vCSQw. (shrink)

Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer (...) variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning. (shrink)

``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing (...) that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine. (shrink)

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that abstraction through information (...) hiding is a primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

Pushing through a logical continuum of closed-to open-system views of organizations necessarily changes the conceptualization of a firm from a strongly bounded entity to a configuration of networks and sub-networks, which exists and operates in a larger systemic network configuration. We unfold a classification of management processes corresponding to views of the firm along the closed/open-systems continuum. We examine ethical issues that are likely to devolve from these classes of management processes, and we suggest typical means by which managers (...) will attempt to control their firms' exposure to such issues. The final class of management processes examined focuses on the achievement of out-comes that are mutually satisfactory in the set of networks and sub-networks that constitute the focal firm, and that support the sustainability of the whole system. The article contributes to organizational theory, business ethics, and computer and information ethics by providing a comprehensive analysis of the impact of managerial views of the firm and of networks - virtual, social, informational - on managerial processes and on our understanding of how business ethics issues are linked to perceptions of what a firm is, does, and can do. (shrink)

In this article we present a new kind of computing device that uses biochemical reactions networks as building blocks to implement logic gates. The architecture of a computing machine relies on these generic and composable building blocks, computation units, that can be used in multiple instances to perform complex boolean functions. Standard logical operations are implemented by biochemical networks, encapsulated and insulated within synthetic vesicles called protocells. These protocells are capable of exchanging energy and information with each other through (...) transmembrane electron transfer. In the paradigm of computation we propose, protoputing, a machine can solve only one problem and therefore has to be built specifically. Thus, the programming phase in the standard computing paradigm is represented in our approach by the set of assembly instructions that directs the wiring of the protocells that constitute the machine itself. To demonstrate the computing power of protocellular machines, we apply it to solve a NP-complete problem, known to be very demanding in computing power, the 3-SAT problem. We show how to program the assembly of a machine that can verify the satisfiability of a given boolean formula. Then we show how to use the massive parallelism of these machines to verify in less than 20 min all the valuations of the input variables and output a fluorescent signal when the formula is satisfiable or no signal at all otherwise. (shrink)

Privacy, Due process and the Computational Turn: The Philosophy of Law Meets the Philosophy of Technology engages with the rapidly developing computational aspects of our world including data mining, behavioural advertising, iGovernment, profiling for intelligence, customer relationship management, smart search engines, personalized news feeds, and so on in order to consider their implications for the assumptions on which our legal framework has been built. The contributions to this volume focus on the issue of privacy, which is often equated with data (...) privacy and data security, location privacy, anonymity, pseudonymity, unobservability, and unlinkability. Here, however, the extent to which predictive and other types of data analytics operate in ways that may or may not violate privacy is rigorously taken up, both technologically and legally, in order to open up new possibilities for considering, and contesting, how we are increasingly being correlated and categorizedin relationship with due process – the right to contest how the profiling systems are categorizing and deciding about us. (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)

In this article we present a new kind of computing device that uses biochemical reactions networks as building blocks to implement logic gates. The architecture of a computing machine relies on these generic and composable building blocks, computation units, that can be used in multiple instances to perform complex boolean functions. Standard logical operations are implemented by biochemical networks, encapsulated and insulated within synthetic vesicles called protocells. These protocells are capable of exchanging energy and information with each other through (...) transmembrane electron transfer. In the paradigm of computation we propose, protoputing, a machine can solve only one problem and therefore has to be built specifically. Thus, the programming phase in the standard computing paradigm is represented in our approach by the set of assembly instructions that directs the wiring of the protocells that constitute the machine itself. To demonstrate the computing power of protocellular machines, we apply it to solve a NP-complete problem, known to be very demanding in computing power, the 3-SAT problem. We show how to program the assembly of a machine that can verify the satisfiability of a given boolean formula. Then we show how to use the massive parallelism of these machines to verify in less than 20 min all the valuations of the input variables and output a fluorescent signal when the formula is satisfiable or no signal at all otherwise. (shrink)

This article examines the connecting lines between the Chilean Project Cybersyn’s interface design, the German Hochschule für Gestaltung Ulm and its cybernetically inspired approaches towards information design, and later developments in interaction design and the emerging field of Human–Computer Interaction in the USA. In particular, it first examines how early works of designers Tomàs Maldonado and Gui Bonsiepe on operative communication, that is, language-independent pictogram systems and visual grammars for computational systems, were intertwined with attempts to ground industrial design (...) in a scientific methodology, to address an era of computing machines, and to develop the concept of the interface as a heuristic for a renovated design thinking. It thereby also reconstructs further historical vanishing lines—e.g. the pictorial grammar of Otto Neurath’s ISOTYPE—of the development of the ‘ulm model’ of design. Second, the article explores how an apprehension of first-order cybernetics in West Germany—e.g. represented by hfg ulm staff like Max Bense or Abraham Moles, merged with Cybersyn’s second-order cybernetics ideas, as represented by Stafford Beer’s Viable System Model. And third, it asks about a further conceptual turn regarding an understanding of design which resulted in a focus on communicative interaction, e.g. in the later works of Fernando Flores and Terry Winograd on HCI, or in Beer’s Team Syntegrity approach. As an effect, the text will explore a specific and international network of cybernetic thinking between Latin America, Europe, and North America which emerged around Project Cybersyn, and which was occupied with questions of HCI, a democratization of design, and intelligence amplification. (shrink)

During the second half of the 20th century, attempts were made to operationally redefine various social activities, including those related to science, the military, administration and industry. These attempts were aided by scientific and technical innovations developed in the Second World War, and subsequently by the increase in use of automation in various domains. This Ph.D. thesis addresses these attempts from a sociohistorical perspective, focusing on the specific case of archaeology. During this period, the domain of archaeology underwent a process (...) of disciplinarisation and professionalisation. The same occurred in applied mathematics and then computer science: this thesis focuses on the relationships between these three domains. In France, during the 1950's and 1960's, there were significant methodological and conceptual innovations. Their subsequent scientific recognition, was, however, relatively minor. In archaeology, innovations related to applied mathematics and automatics did not lead to the emergence of an archaeological speciality based on computation. This situation was in striking contrast to what happened in other scientific domains and in archaeology in other countries, where new theoretical and methodological Anglophone definitions in ‘New Archaeology’ were spreading worldwide. This thesis explores three collective attempts to redefine the conceptual and methodological basis of archaeology, led by Georges Laplace, Jean-Claude Gardin and Jean Lesage, across France, Spain and Italy. These cases are completed by other people who had significant careers in both engineering and archaeology. In general, this thesis studies a scientific activity by investigating the cognitive and social aspects of peoples’ methodological contributions. Three models of the relationships between experts in a scientific domain and experts in an applied science (here mathematics and computing) are empirically identified and described. The effects of introducing mathematical and automation procedures on the division of labour and the distribution of recognition are analysed. The success or failure of the methodological propositions are discussed with reference to several factors and models of scientific innovation. This thesis generates new information on the development of rescue and preventive archaeology and on the use of digital technologies in human sciences. The analysis draws on 82 interviews, 23 archives and several bibliometric datasets (extracted from pre-existing databases or constructed for the purpose of this research). Mirroring the archaeological propositions under study, this research also intends to illustrate the possible use of computing and formalised procedures in social sciences. The documentation and demonstrative principles underlying this work, implemented by using Wiki, the methods of literate programming and reproducible research, are themselves analysed. (shrink)

I propose that a sociological and historical examination of nanotechnologists can contribute more to an understanding of nanotechnology than an ontological definition. Nanotechnology emerged from the convergent evolution of numerous "technical knowledge communities"-networks of tightly-interconnected people who operate between disciplines and individual research groups. I demonstrate this proposition by sketching the co-evolution of computational chemistry and computational nanotechnology. Computational chemistry arose in the 1950s but eventually segregated into an ab initio, basic research, physics-oriented flavor and an industry-oriented, molecular modeling and (...) visualization, biochemical flavor. Computational nanotechnology arose in the 1990s as a synthesis of these two subgroups, infused by people and practices from computational materials science, engineering, computer science, and elsewhere. I show that to understand the aims and outcomes of computational nanotechnology-and nanotechnology more generally-we need to understand relationships between different, but related, nano knowledge communities and their dependence on particular practices, artifacts, and theories. (shrink)

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation (...) of the ITS was done by a group of students and teachers. The results were acceptable. (shrink)

We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic (...) representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks’ mechanisms in terms of lived experience. (shrink)

Metaphor's peculiar property to yield cognitive insight-- often in otherwise false sentences -- has been the focus of contemporary studies of metaphor. In Metaphor: Its Linguistic Structure and Cognitive Force, Eva Kittay develops the semantic field theory of metaphor (SFTM). The task of the present work is to formalize some of the central claims of SFTM. Formalization forces us to make the central concepts of SFTM precise and operational, and it enables us to evaluate the consistency and explanatory power of (...) SFTM through empirical testing. To empirically test our formalization of SFTM, we constructed a working computer program from it. This program, called NETMET, is highly successful in generating syntactically complex and semantically interesting metaphors. Its success provides empirical confirmation of SFTM. (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 (to exclude application to quantum physics; and a cause cannot follow an effect). 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 aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of recent results (...) based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant. (shrink)

Explaining the complex dynamics exhibited in many biological mechanisms requires extending the recent philosophical treatment of mechanisms that emphasizes sequences of operations. To understand how nonsequentially organized mechanisms will behave, scientists often advance what we call dynamic mechanistic explanations. These begin with a decomposition of the mechanism into component parts and operations, using a variety of laboratory-based strategies. Crucially, the mechanism is then recomposed by means of computational models in which variables or terms in differential equations correspond to (...) properties of its parts and operations. We provide two illustrations drawn from research on circadian rhythms. Once biologists identified some of the components of the molecular mechanism thought to be responsible for circadian rhythms, computational models were used to determine whether the proposed mechanisms could generate sustained oscillations. Modeling has become even more important as researchers have recognized that the oscillations generated in individual neurons are synchronized within networks; we describe models being employed to assess how different possible network architectures could produce the observed synchronized activity. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can (...) support cognitive and emotional processes underlying human creativity. (shrink)

1. Do models formulated in programming languages use explicit rules where connectionist models do not? 2. Are rules as found in programming languages hard, precise, and exceptionless, where connectionist rules are not? 3. Do connectionist models use rules operating on distributed representations where models formulated in programming languages do not? 4. Do connectionist models fail to use structure sensitive rules of the sort found in "classical" computer architectures? In this chapter we argue that the answer to each of these (...) questions is negative. (shrink)

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This paper aims to (...) critically assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles. (shrink)

Developments in neurotechnology took a leap forward with the demonstration of the first Brain to Brain Interface (BBI). BBIs enable direct communication between two brains via a Brain Computer Interface (BCI) and bypasses the peripheral nervous system. This discovery promises new possibilities for future battlefield technology. As battlefield technology evolves, it is more likely to place greater demands on future soldiers. Future soldiers are more likely to process large amounts of data derived from an extensive networks of humans and (...) machines. This raises several ethical and philosophical concerns. This paper will look at BBI technology in current stages of research, future BBI applications in the military and how the potential use of BBIs in military operations challenges the way we understand the concept of responsibility. In this paper, I propose that an individual connected to a BBI ought not to be held fully responsible for her actions. The justification for this proposition is based on three key points such as an individual connected to a BBI does not have the ability to act freely, has a diminished sense of self-agency and may not be able to demonstrate authenticity of the thoughts and memories generated when connected to the interface. (shrink)

In this paper we study the emergence of Minkowski space–time from a discrete causal network representing a classical information flow. Differently from previous approaches, we require the network to be topologically homogeneous, so that the metric is derived from pure event-counting. Emergence from events has an operational motivation in requiring that every physical quantity—including space–time—be defined through precise measurement procedures. Topological homogeneity is a requirement for having space–time metric emergent from the pure topology of causal connections, whereas physically (...) homogeneity corresponds to the universality of the physical law. We analyze in detail the case of 1+1 dimensions. If we consider the causal connections as an exchange of classical information, we can establish coordinate systems via an Einsteinian protocol, and this leads to a digital version of the Lorentz transformations. In a computational analogy, the foliation construction can be regarded as the synchronization with a global clock of the calls to independent subroutines in a parallel distributed computation. Thus the Lorentz time-dilation emerges as an increased density of leaves within a single tic-tac of a clock, whereas space-contraction results from the corresponding decrease of density of events per leaf. The operational procedure of building up the coordinate system introduces an in-principle indistinguishability between neighboring events, resulting in a network that is coarse-grained, the thickness of the event being a function of the observer's clock. The illustrated simple classical construction can be extended to space dimension greater than one, with the price of anisotropy of the maximal speed, due to the Weyl-tiling problem. This issue is cured if the causal network is quantum, as e.g. in a quantum cellular automaton, and isotropy is recovered by quantum coherence via superposition of causal paths. We thus argue that in a causal network description of space–time, the quantum nature of the network is crucial. (shrink)

Issue Title: Moral Luck, Social Networking Sites, and Trust on the Web I argue that the problem of 'moral luck' is an unjustly neglected topic within Computer Ethics. This is unfortunate given that the very nature of computer technology, its 'logical malleability', leads to ever greater levels of complexity, unreliability and uncertainty. The ever widening contexts of application in turn lead to greater scope for the operation of chance and the phenomenon of moral luck. Moral luck bears down (...) most heavily on notions of professional responsibility, the identification and attribution of responsibility. It is immunity from luck that conventionally marks out moral value from other kinds of values such as instrumental, technical, and use value. The paper describes the nature of moral luck and its erosion of the scope of responsibility and agency. Moral luck poses a challenge to the kinds of theoretical approaches often deployed in Computer Ethics when analyzing moral questions arising from the design and implementation of information and communication technologies. The paper considers the impact on consequentialism; virtue ethics; and duty ethics. In addressing cases of moral luck within Computer Ethics, I argue that it is important to recognise the ways in which different types of moral systems are vulnerable, or resistant, to moral luck. Different resolutions are possible depending on the moral framework adopted. Equally, resolution of cases will depend on fundamental moral assumptions. The problem of moral luck in Computer Ethics should prompt us to new ways of looking at risk, accountability and responsibility.[PUBLICATION ABSTRACT]. (shrink)

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a (...) reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. (...) This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state. (shrink)

Vehicular ad hoc network is a multihop mobile wireless communication network that can realize many vehicle-related applications through multitop communication. In the open wireless communication environment, security and privacy protection are important contents of VANET research. The most basic method of VANET privacy protection is anonymous authentication. Even through, there are many existing schemes to provide anonymous authentication for VANETs. Many existing schemes suffer from high computational cost by using bilinear pairing operation or need the assistance of the (...) trust authorities during the authentication process or rely on an ideal tamper-proof device, which requires very strong security assumption. In this study, an anonymous authentication and key negotiation scheme by using private key and group key is proposed, which is based on pseudonym using the nonsingular elliptic curve. In this scheme, there is no third party trust center to participate in the authentication, there is no need to query the database, and there is no need of the local database to save the identity information of many vehicles, which reduce the storage space and the authentication time compared with other schemes. The proposed scheme only needs realistic TPDs. In the proposed scheme, TPDs do not need to preinstall the system key as many other schemes do; hence, the failure of a single TPD does not affect the security of the entire system. The security of the scheme is proved under the random oracle model. Compared with the related schemes using bilinear pairings, the computational cost and communication cost of the proposed scheme are reduced by 82% and 50%, respectively. (shrink)

Active participation closely associates with the sustainable operation of co-creation communities. Different from recent studies on the promotion of sustainable operation by identifying the internal and external motivations of user participation, this paper aims to analyze the mechanism regarding how different motivations affect the decision of user participation from group-level perspective. To better understand the mechanism, internal and external motivations are, respectively, captured by return-cost analysis and user interactive network. Afterwards, a network evolutionary game model was formulated to (...) analyze the dynamic strategy selection of all users. In addition, the stable equilibrium and evolutionary path of strategies are analyzed through computational experiments. Results indicate the following: Rewards have an influence on the promotion of active participation. However, with the continued growth of rewards, this promotion does not make sense sustainably. The promotional effect of information noise on the selection of active participation can be found when passive participation is the dominant strategy. However, the inhibitory effect can be seen in populations that mainly adopt active participation. The scale-free feature of user interactive network inhibits the selection of active participation when active participation is the dominant strategy in populations. Results found here is beneficial for managers to implement the specified policies and thus to achieve the sustainability of co-creation community. (shrink)

Memory, attention, and decision-making are three major areas of psychology. They are frequently studied in isolation, and using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural networks (...) in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.The topics covered includeThe hippocampus and memoryReward and punishment related learning: emotion and motivationVisual object recognition learningShort term memoryAttention, short term memory, and biased competitionProbabilistic decision-makingAction selectionDecision-makingAlso included are tutorial appendices onNeural networks in the brainNeural encoding in the brain'Memory, Attention and Decision-Making' will be valuable for those in the fields of neuroscience, psychology, and cognitive neuroscience from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, evolutionary biology, psychiatry, medicine, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work. (shrink)

A standard system, which is a powerful tool in maintaining the normal operations and development of a specific industry, is intrinsically a complex network composed of numerous standards which coordinate and interact with each other. In a networked standard system, the identification of critical standards is of great significance when drafting and revising standards. However, a majority of the existing literature has focused on the citation relationships between standards while ignoring the intrinsic interdependent relationships between the contents of (...) standards. To overcome this limitation, we utilize the text similarity approach to quantify the relationship intensity between each pair of standards, in order to generate a directed weighted network. The critical contribution of this study is that the similarity computed by the TSA is incorporated into the traditional PageRank algorithm for the identification of critical standards. The improved algorithm comprehensively considers the quantity and importance of neighboring standards and the citation intensity, as quantified by TSA. The algorithm is finally validated using the Chinese environmental health standards through comparison with the traditional PageRank algorithm and different classic measurements. (shrink)

This article analyzes US drone operations through a historical and ethnographic analysis of the remote split paradigm used by the US Air Force. Remote split refers to the globally distributed command and control of drone operations and entails a network of human operators and analysts in the Middle East, Europe, and Southeast Asia as well as in the continental United States. Though often viewed as a teleological progression of “unmanned” warfare, this paper argues that historically specific technopolitical (...) logics establish the conditions of possibility for the work of war to be divisible into discreet and computationally mediated tasks that are viewed as effective in US military engagements. To do so, the article traces how new forms of authorized evidence and expertise have shaped developments in military operations and command and control priorities from the Cold War and the “electronic battlefield” of Vietnam through the Gulf War and the conflict in the Balkans to contemporary deployments of drone operations. The article concludes by suggesting that it is by paying attention to divisions of labor and human–machine configurations that we can begin to understand the everyday and often invisible structures that sustain perpetual war as a military strategy of the United States. (shrink)

This article explores the ways in which data centre operators are currently reconfiguring the systems of energy and heat supply in European capitals, replacing conventional forms of heating with data-driven heat production, and becoming important energy suppliers. Taking as an empirical object the heat generated from server halls, the article traces the expanding phenomenon of ‘waste heat recycling’ and charts the ways in which data centre operators in Stockholm and Paris direct waste heat through metropolitan district heating systems and urban (...) homes, and valorise it. Drawing on new materialisms, infrastructure studies and classical theory of production and destruction of value in capitalism, the article outlines two modes in which this process happens, namely infrastructural convergence and decentralisation of the data centre. These modes arguably help data centre operators convert big data from a source of value online into a raw material that needs to flow in the network irrespective of meaning. In this conversion process, the article argues, a new commodity is in a process of formation, that of computation traffic. Altogether data-driven heat production is suggested to raise the importance of certain data processing nodes in Northern Europe, simultaneously intervening in the global politics of access, while neutralising external criticism towards big data by making urban life literally dependent on power from data streams. (shrink)

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm based (...) on elite heuristic operation and migration strategy and the improved coyote optimization algorithm based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search, and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology. (shrink)

We propose a model of evolving protection against systemic risk related to recovery. Using the failure potential in network-agent dynamics, we present a process-based simulation that provides insights into alternative interventions and their mechanical uniqueness. The fundamental operating principle of this model is that computation allows greater emphasis on optimizing the recovery within the general regularity of random network dynamics. The rules and processes that are used here could be regarded as useful techniques in systemic risk measurement relative (...) to numerical failure reduction analyses. (shrink)

With the rapid development of the Internet information age, social networks, mobile Internet, and e-commerce have expanded the scope of Internet applications. The “big data” era is a challenge and chance for companies and has a great impact on social economy, politics, culture, and people’s lives. An accurate marketing system is developed based on J2EE, and the architecture is selected from the user layer, business logic layer, and data layer and the B/S3 layer application, including three layers of crip-dm and (...) semma. And, other process methods are used. Data-mining-based marketing system information solutions consist of several parts, such as requirement analysis, design, implementation, and testing. This paper introduces data mining technology to the marketing business based on the practical use and design IT solutions for precision marketing, attribute selection tools, attribute analysis tools, modeling prediction tools, and others. This paper introduces a precision marketing system based on data mining technology. The system passes the actual test and the deployment and the operation of this system are confirmed. The system, which can improve marketing activity, is tested, and the development and operation of this system markedly increase the company’s earnings. (shrink)

Borrowing Timothy Morton’s notion of hyperobject, this article explores questions of network and scale in generative adversarial networks (GAN) images. In this context, the term network refers to the omnipresence of algorithmic images today and their significant impact on our lives. Such images are massively distributed in time and space beyond any sensible human-scale. Scale, in this context, denotes the relations between different operational layers of algorithmic images, such as the pictorial layer in contrast to the data layer. (...) An algorithmic image is simultaneously a visual image, a symbol, a data point and part of a mass visual milieu. Its meaning is thus polymorphic and can, arguably, never be exhausted. The article explores these terms through analysis of the website www.thispersondoesnotexist.com. (shrink)