The design process in Kashmiri carpet weaving is distributed over a number of actors and artifacts and is mediated by a weaving notation called talim. The script encodes entire design in practice-specific symbols. This encoded script is decoded and interpreted via design-specific conventions by weavers to weave the design embedded in it. The cognitive properties of this notational system are described in the paper employing cognitive dimensions (CDs) framework of Green (People and computers, Cambridge University Press, Cambridge, 1989) and Blackwell (...) et al. (Cognitive technology: instruments of mind—CT 2001, LNAI 2117, Springer, Berlin, 2001). After introduction to the practice, the design process is described in ‘The design process’ section which includes coding and decoding of talim. In ‘Cognitive dimensions of talim’ section, after briefly discussing CDs framework, the specific cognitive dimensions possessed by talim are described in detail. (shrink)

The squared paper or graphs are grid-based design representations used in engineering, industrial and craft design practices wherein designs are drawn over symmetrical grids. This paper reports grid-processing strategies undertaken by actors in a native craft practice, viz. Kashmiri carpet-weaving having three task contexts: (1) _design_, wherein designs are drawn on graph sheets and color scheme given by assigning practice-specific symbolic codes to the motifs by designers; (2) _coding_, wherein a cryptic script, called _talim_, is generated from these encoded (...) graphs by talim-writers; and (3) _weaving_, wherein this script is read, decoded and communicated in an equally cryptic trade-language by weavers among their teams. 32 video-recorded think-aloud sessions were conducted with talim-writers to understand their graph-processing and resultant coding strategies. The observations and qualitative analysis of transcripts revealed highly _situated_ strategies undertaken by experts keen on imbibing visuality of design embedded in the grids, in contrast to _embodied_ strategies undertaken by less-experienced coders keen on processing grids’ structural features and getting overwhelmed by grid-clutter in the process. In this landscape, the findings revealed a negotiated nature of errors instead of being deviations from the underlying grid and thereby objectively assessable. The paper makes recommendations for CAD systems to include more of those grid features exploited by experts during their graph processing to facilitate designers. (shrink)

Cognition is predominantly seen as information processing in multidisciplinary landscape of cognition studies, despite having had a formidable opposition from embodied and embedded perspectives in the last few decades. This paper analyses cognitive processes involved in different task domains of Kashmiri carpet weaving practice from the theoretical frameworks of cognitivism and situated-distributed cognition. After introducing the practice and its task domains (Section −1), paradigmatic cognitive activities involved in them are discussed and how these are explained by the two theoretical (...) paradigms and why the latter framework explains these processes better (Section-2). During this discussion (Section – 3), a way via culture impacts the practice and resultant cognitive operations turns out to be workspace of the actors. (shrink)

The design process in Kashmiri carpet weaving is a distributed process encompassing a number of actors and artifacts. These include a designer called naqash who creates the design on graphs, and a coder called talim-guru who encodes that design in a specific notation called talim which is deciphered and interpreted by the weavers to weave the design. The technological interventions over the years have influenced these artifacts considerably and triggered major changes in the practice, from heralding profound cognitive accomplishments in (...) manually driven design process causing major alterations in the overall structure of the practice. The recent intervention is by the digital technology: on the one hand, it has brought precision and speedy processing in the design process, and on the other, it has eliminated some of the crucial actors from the practice. This paper, which forms part of a larger study on the situated and distributed cognitive process in Kashmiri carpet-weaving practice, describes the technological makeover of the design artifacts involved in this practice over the years and their resultant cognitive impact on the design process as well as on the practice. (shrink)

The design process in Kashmiri carpet weaving is a distributed process encompassing a number of actors and artifacts. These include a designer called naqash who creates the design on graphs, and a coder called talim-guru who encodes that design in a specific notation called talim which is deciphered and interpreted by the weavers to weave the design. The technological interventions over the years have influenced these artifacts considerably and triggered major changes in the practice, from heralding profound cognitive accomplishments in (...) manually driven design process causing major alterations in the overall structure of the practice. The recent intervention is by the digital technology: on the one hand, it has brought precision and speedy processing in the design process, and on the other, it has eliminated some of the crucial actors from the practice. This paper, which forms part of a larger study on the situated and distributed cognitive process in Kashmiri carpet-weaving practice, describes the technological makeover of the design artifacts involved in this practice over the years and their resultant cognitive impact on the design process as well as on the practice. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by (...) a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning, such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. (...) Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms. (shrink)

Beginning with a survey of the shortcoming of theories of organology/media-as-externalization of mind/body—a philosophical-anthropological tradition that stretches from Plato through Ernst Kapp and finds its contemporary proponent in Bernard Stiegler—I propose that the phenomenological treatment of media as an outpouching and extension of mind qua intentionality is not sufficient to counter the ̳black-box‘ mystification of today‘s deep learning‘s algorithms. Focusing on a close study of Simondon‘s On the Existence of Technical Objectsand Individuation, I argue that the process-philosophical work of (...) Gilbert Simondon, with its critique of Norbert Wiener‘s first-order cybernetics, offers a precursor to the conception of second-order cybernetics (as endorsed byFrancisco Varela, Humberto Maturana, and Ricardo B. Uribe) and, specifically, its autopoietic treatment of information. It has been argued by those such as Frank Pasquale that neuro-inferential deep learning systems premised on predictive patterning, suchas AlphaGo Zero, have a veiled logic and, thus, are ̳black boxes‘. In detailing a philosophical-historical approach to demystify predictive patterning/processing and the logic of such deep learning algorithms, this paper attempts to shine a light on such systems and their inner workingsàla Simondon. (shrink)

I argue that 'deep' ecology (as exemplified by the work of Arnie Naess) involves three inter-related commitments: (1) to an ethics of nature or axiological anti-humanism in which natural entities, processes or systems can possess intrinsic value independently of human beings; (2) a metaphysical naturalism or anti-humanism in which human beings are themselves conceptualized as natural products; (3) a transformative aspect. Although (3) is sometimes cast in personal or psychological terms, I think the idea can be given a properly (...) philosophical reconstruction along the lines of a kind of Ideologiekritik. I give the examples of Heidegger and Adorno to show how deeply our understanding of nature has been corrupted, and hence the depth of conceptual transformation required correctly to conceive nature. But both these thinkers simultaneously demonstrate a hostility to metaphysical naturalism and therefore have ultimately limited value for deep ecology. I then outline three possible relations of axiological to metaphysical commitments: (a) the abandonment of naturalism about values; (b) a projective or transcendentally idealist account of values; (c) an account of values as themselves lodged within the operation of natural systems. My reconstruction of the Ideologiekritiker shows them belonging to (b). So that if deep ecology is to avoid (a), it must embrace (c). Deleuze is much touted by environmental philosophers as contributing to (c). I argue that this is correct, but that the axiological commitments Deleuze manifests in his account of nature are ultimately quite different from those to which deep ecologists appear sympathetic. (shrink)

Deep Brain Stimulation (DBS) is a relatively new, experimental treatment for patients suffering from treatment-refractory Obsessive Compulsive Disorder (OCD). The effects of treatment are typically assessed with psychopathological scales that measure the amount of symptoms. However, clinical experience indicates that the effects of DBS are not limited to symptoms only: patients for instance report changes in perception, feeling stronger and more confident, and doing things unreflectively. Our aim is to get a better overview of the whole variety of changes (...) that OCD patients experience during DBS treatment. For that purpose we conducted in-depth, semi-structured interviews with 18 OCD patients. In this paper, we present the results from this qualitative study.We list the changes grouped in four domains: with regard to (a) person, (b) (social) world, (c)characteristics of person-world interactions, and (d) existential stance. We subsequently provide an interpretation of these results. In particular, we suggest that many of these changes can be seen as different expressions of the same process; namely that the experience of anxiety and tension gives way to an increased basic trust and increased reliance on one’s abilities. We then discuss the clinical implications of our findings, especially with regard to properly informing patients of what they can expect from treatment, the usefulness of including CBT in treatment, and the limitations of current measures of treatment success. We end by making several concrete suggestions for further research. (shrink)

Kierkegaard’s ideal supports a radical form of “deep diversity,” to use Charles Taylor’s expression. It is radical because it embraces not only irreducible conceptions of the good but also incompatible ones. This is due to its paradoxical nature, which arises from its affirmation of both monism and pluralism, the One and the Many, together. It does so in at least three ways. First, in terms of the structure of the self, Kierkegaard describes his ideal as both unified (the “positive (...) third”) and plural (a “negative unity”). Second, he affirms a process which brings together unity, as implied by the linear notion of “stages”, with plurality, in the form of “spheres of existence” (aesthetic, ethical, and religious). And third, the culmination of the process implies that we should embrace both a unified dialectic (“Religiousness A”) alongside the plural remnants of the ethical/aesthetic, that is, both the infinity of the former and the finitudes of the latter. Unsurprisingly, while Kierkegaard describes those who are able to exemplify his ideal in practice as “always joyful,” he also considers the ideal to be “extremely hard, the hardest task of all.” This is why those such as Hubert L. Dreyfus are wrong to claim that it provides an experience of bliss; on the contrary, those who realize it “are always in danger.” As I shall show, one form this danger takes is that it threatens to dirty the hands of those who manage to uphold Kierkegaard’s ideal. Moreover, it does so in ways that, I claim, tend to be missed by Kierkegaard himself. Nevertheless, the danger is also essential to the creativity of his approach, and I conclude by pointing out how this creativity makes it capable of tackling one of the profoundest challenges in contemporary ethics: that arising from what just war theorists call a “supreme emergency.” . (shrink)

Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral (...) blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions. (shrink)

__Deep in Thought_ provides an introduction to intellectual virtues—the personal qualities and character strengths of good thinkers and learners—and outlines a pragmatic approach for teachers to reinforce them in the classroom._ With a combination of theoretical expertise and practical experience, philosopher Jason Baehr endorses intellectual virtues as a rich, meaningful way to think about and understand the purpose of education. He makes a persuasive case for prioritizing intellectual virtues in the classroom to facilitate deeper learning, encourage lifelong learning, and enrich (...) teacher practice. Baehr profiles nine key virtues that enable learners to initiate the process of learning, maintain forward momentum, and overcome common obstacles. With engaging anecdotes and concrete examples, he presents a wealth of principles, postures, and practices that educators can employ in promoting essential habits of mind such as curiosity, open-mindedness, and intellectual courage. Baehr illustrates how opportunities to practice these intellectual habits can be integrated into the classroom in ways that align with current teaching practices. In addition, he shows how educators can adapt these practices to accommodate students’ identities, developmental abilities, and interests. This thought-provoking book supports all educators, especially middle and high school teachers, in teaching for intellectual virtues. _Deep in Thought_ is a philosophical and yet practical guide to one of the most important aims of education: helping students become skilled thinkers and learners. (shrink)

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language (...) processing. Additionally, we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation (...) looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

This paper explores the impacts of neurological intervention on selfhood with reference to recipients’ claims about changes to their self-understanding following Deep Brain Stimulation for treatment of Parkinson’s Disease. In the neuroethics literature, patients’ claims such as: “I don’t feel like myself anymore” and “I feel like a machine”, are often understood as expressing threats to identity. In this paper I argue that framing debates in terms of a possible threat to identity—whether for or against the proposition, is mistaken (...) and occludes what is ethically salient in changes from DBS. Rather, by adopting a relational narrative approach to identity and autonomy, I show that the ethically salient issue from DBS is impacts on autonomous agency—whether one’s actions and beliefs are one’s own, and how DBS may hinder, or foster, embodied, relational autonomy competences. This approach recognizes that if sufficiently significant, impacts on autonomy competences may pose a threat to one’s ability to contribute to the process of authoring one’s own life and so pose a threat to identity formation. I argue this approach resolves the confusion in the literature about whether and how DBS threatens identity and provides a complex picture of how DBS may affect selfhood by disrupting narrative identity formation and revision, distorting agency and/or undermining autonomy. (shrink)

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language (...) processing. Additionally, we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

With the early success of the deep ecology movement in attracting adherents and with the increasing threat of a global ecological catastrophe, one would have expected this movement to have triumphed. We should be in the process of radically transforming society to create a harmonious relationship between humans and the rest of nature. Instead, deep ecology has been marginalized. What has triumphed instead is an alliance of managerialism, transnational corporations and neo-liberalism committed to replacing communities with markets and (...) transforming every facet of nature and society into means for increasing the profitability of corporations and expanding GDP. This outcome is here explained by the failure of deep ecologists to understand the ideological, political and economic forces driving ecological destruction, and thereby what is required to overcome these. To regain the initiative, it is argued, it will be necessary to recover the whole project of the Radical Enlightenment upholding the Renaissance quest for liberty that was subverted by the scientific materialism and possessive individualism of the Moderate Enlightenment. Not only will it be necessary to transform science to develop a comprehensive alternative conception of nature and humanity and its destiny, it will be necessary to revive and develop the institutions of the Radical Enlightenment to subordinate markets to democratically organized communities at multiple levels, as communities of communities committed to augmenting the life of these communities, including the life of ecological communities. Doing so will involve reformulating the grand narrative of emancipation as a dialogic, polyphonic grand narrative to orient individuals, organizations and nations to create a global ecological civilization. -/- . (shrink)

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning, given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their (...) trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios. (shrink)

With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional (...) experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”. (shrink)

Deep brain stimulation (DBS) to different sites allows interfering with dysfunctional network function implicated in major depression. Because a prominent clinical feature of depression is anhedonia--the inability to experience pleasure from previously pleasurable activities--and because there is clear evidence of dysfunctions of the reward system in depression, DBS to the nucleus accumbens might offer a new possibility to target depressive symptomatology in otherwise treatment-resistant depression. Three patients suffering from extremely resistant forms of depression, who did not respond to pharmacotherapy, (...) psychotherapy, and electroconvulsive therapy, were implanted with bilateral DBS electrodes in the nucleus accumbens. Stimulation parameters were modified in a double-blind manner, and clinical ratings were assessed at each modification. Additionally, brain metabolism was assessed 1 week before and 1 week after stimulation onset. Clinical ratings improved in all three patients when the stimulator was on, and worsened in all three patients when the stimulator was turned off. Effects were observable immediately, and no side effects occurred in any of the patients. Using FDG-PET, significant changes in brain metabolism as a function of the stimulation in fronto-striatal networks were observed. No unwanted effects of DBS other than those directly related to the surgical procedure (eg pain at sites of implantation) were observed. Dysfunctions of the reward system--in which the nucleus accumbens is a key structure--are implicated in the neurobiology of major depression and might be responsible for impaired reward processing, as evidenced by the symptom of anhedonia. These preliminary findings suggest that DBS to the nucleus accumbens might be a hypothesis-guided approach for refractory major depression. (shrink)

This paper discusses the use of deep brain stimulation for the treatment of neurological and psychiatric disorders in children. At present, deep brain stimulation is used to treat movement disorders in children and a few cases of deep brain stimulation for psychiatric disorders in adolescents have been reported. Ethical guidelines on the use of deep brain stimulation in children are therefore urgently needed. This paper focuses on the decision-making process, and provides an ethical framework for (future) (...) treatment decisions in pediatric deep brain stimulation. I defend a shared decision-making model in case of deep brain stimulation for neurological and psychiatric disorders in children. To protect the vulnerable child patient, a dual consent process is needed where parents or parental guardians give their consent, and the child gives his/her assent. (shrink)

In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep learning (...) that includes deep learning investment, deep cognitive-emotional experience, deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers’ emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis and confirmatory factor analysis and verified that the perceived teacher emotional support has an impact on the four factors of students’ deep learning using the SEM. (shrink)

Deep ecology and ecofeminism are contemporary environmental philosophies that share the desire to supplant the predominant Western anthropocentric environmental frameworks. Recently thinkers from these movements have focused their critiques on each other, and substantial differences have emerged. This essay explores central aspects of this debate to ascertain whether either philosophy has been undermined in the process and whether there are any indications that they are compatible despite their differences.

Deeply understood, democracy is more than a "formal" institutional framework for which America provides the model, acting as a preferable alternative to the modern totalitarian regimes that have distorted social life around the world. At its core, as John Dewey understood, democracy is a realistic ideal, a desired and desirable future possibility that is yet-to-be. In this period of global crises in differing cultures, a shared environment, and an increasingly globalized political economy, this book provides a clear contemporary articulation of (...) deep democracy that can guide an evolutionary deepening of democratic institutions, of habits of the heart, and of the processes of education and social inquiry that support them. (shrink)

Deep ecology is an ecological philosophy that promotes an ecocentric lifestyle to remedy the problems of depleting resources and planetary degradation. An integral part of this ecosophy is the process of forming a metaphysical connection to the earth, referred to as self-realisation; an unfolding of the self out into nature to attain a transcendental, non-egoic state. Findings from our research indicate that secondary school students in environment clubs align with the principles of deep ecology, and show a capacity (...) to become student eco-philosophers, and they report empathy for becoming ecocentric beings. This study explores the capacity for students to engage in environmental philosophy. (shrink)

Parkinson´s disease (PD) is a neurodegenerative disorder characterized by motor symptoms following dopaminergic depletion in the substantia nigra. Besides motor impairments however, several non-motor detriments can have the potential to considerably impact subjectively perceived quality of life in patients. Particularly emotion recognition of facial expressions has been shown to be affected in PD, and especially the perception of negative emotions like fear, anger or disgust is impaired. While emotion processing generally refers to automatic implicit as well as conscious explicit (...) processing, the focus of most previous studies in PD was on explicit recognition of emotions only, while largely ignoring implicit processing deficits. Deep brain stimulation of the subthalamic nucleus (STN-DBS) is widely accepted as therapeutic measure in the treatment of PD and has been shown to advantageously influence motor problems. Among various concomitant non-motor effects of STN-DBS, modulation of facial emotion recognition under subthalamic stimulation has been investigated in previous studies with rather heterogeneous results. While there seems to be consensus regarding the processing of disgust which significantly deteriorates under STN stimulation, findings concerning emotions like fear or happiness report heterogeneous data and seem to depend on various experimental settings and measurements. In the present review, we summarized previous investigations focusing on STN-DBS influence on recognition of facial emotional expressions in patients suffering from PD. In a first step, we provide a synopsis of disturbances and problems in facial emotion processing observed in patients with PD. Secondly, we present findings of STN-DBS influence on facial emotion recognition and especially highlight different impacts of stimulation on implicit and explicit emotional processing. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

I consider the issue of the self and its relation to the environment, focusing on the accounts given in ecofeminism and deep ecology. Though both stress the relatedness of the human self to nature, these accounts differ in various ways. Ecofeminism stresses the value of personal relations with particular others, whereas deep ecology argues that we should expand our sense of self to include all natural others and the whole of nature. Deep ecology’s views on the self, (...) which are loosely based on scientific ecology, are examined further and I argue that the implications are that selves are not to be seen as static things but rather as processes, and as constituted by their relations with others. This understanding of the self, I argue, enriches both deep ecology and ecofeminism’s claims on selfhood, and will enable the resolution of some of the difficulties they perceive in each other’s account. (shrink)

This study concerned the role of gestures that accompany discourse in deep learning processes. We assumed that co‐speech gestures favor the construction of a complete mental representation of the discourse content, and we tested the predictions that a discourse accompanied by gestures, as compared with a discourse not accompanied by gestures, should result in better recollection of conceptual information, a greater number of discourse‐based inferences drawn from the information explicitly stated in the discourse, and poorer recognition of verbatim of (...) the discourse. The results of three experiments confirmed these predictions. (shrink)

The adaptive BCI known as ‘closed-loop deep brain stimulation’ (clDBS) is a device that stimulates the brain in order to prevent pathological neural activity and automatically adjusts stimulation levels based on computational algorithms that detect or predict those pathological processes. One of the prominent ethical concerns raised by clDBS is that, by inhibiting or modulating the undesirable neural states of a cognitive agent automatically, the device potentially undermines her autonomy. It has been argued that clDBS is not a threat (...) because autonomy is fundamentally relational, i.e., it essentially depends on external (e.g., social or cultural) factors. If the relational approach to autonomy includes human-computer interaction, then the mechanisms of clDBS, even if external to the brain and exerting some degree of control over the individual, may support her autonomy. However, DBS applications are substantially different from one another, each involving a specific neurological or psychiatric condition, neural target, mechanism and symptom(s), and therefore at least some of them may not fit into the relational analysis. I examine different clDBS applications for treatment resistant depression and claim that while internal capsule/ventral striatum (VC/VS) clDBS is a case of relational autonomy, subgenual cingulate gyrus (Cg25) clDBS is not. Autonomy (relational or otherwise) requires some degree of self-regulation of our motivational states, which is supported by VC/VS DBS but is absent in Cg25 DBS. In Cg25 DBS the device itself directly influences motivational states, thus substituting or overriding (instead of supporting) the auto-regulatory cognitive processes required for autonomous action. (shrink)

The Anthropocene concept allows human history to be imagined within the temporal framework of planetary processes. Accordingly, some environmentalists increasingly favour massively lengthening the temporal horizons of moral concern. Whilst there are defensible reasons for doing so, I wish to take issue with the ‘secular time’ perspective underlying some such approaches. To make my case, I present, in the first section, two recent manifestations of the long view perspective: a) ‘deep future’ narratives in popular climate science and futurism; b) (...) the ideas behind the Long Now Foundation. In the second section, I apply a critical lens to these perspectives via classic analyses of secular time by Charles Taylor, Hannah Arendt and Giorgio Agamben. I conclude by suggesting that these post-secular critiques should be considered alongside recent approaches to the Anthropocene and the ‘geological turn’ from new materialist perspectives. (shrink)

Background -/- Innovative neurosurgical treatments present a number of known risks, the natures and probabilities of which can be adequately communicated to patients via the standard procedures governing obtaining informed consent. However, due to their novelty, these treatments also come with unknown risks, which require an augmented approach to obtaining informed consent. -/- Objective -/- This paper aims to discuss and provide concrete procedural guidance on the ethical issues raised by serious unexpected complications of novel deep brain stimulation treatments. (...) -/- Approach -/- We illustrate our analysis using a case study of the unexpected development of recurrent stereotyped events in patients following the use of deep brain stimulation (DBS) to treat severe chronic pain. Examining these unexpected complications in light of medical ethical principles, we argue that serious complications of novel DBS treatments do not necessarily make it unethical to offer the intervention to eligible patients. However, the difficulty the clinician faces in determining whether the intervention is in the patient's best interests generates reasons to take extra steps to promote the autonomous decision making of these patients. -/- Conclusion and recommendations -/- We conclude with clinical recommendations, including details of an augmented consent process for novel DBS treatment. (shrink)

Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson’s disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson’s (...) disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS’ leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions (p = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions (p < 0.001). The sweet spot-guided suggestions of each contact’s effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects. (shrink)

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely (...) the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)

Deep learning techniques have revolutionised artificial systems’ performance on myriad tasks, from playing Go to medical diagnosis. Recent developments have extended such successes to natural language processing, an area once deemed beyond such systems’ reach. Despite their different goals, these successes have suggested that such systems may be pertinent to theoretical linguistics. The competence/performance distinction presents a fundamental barrier to such inferences. While DL systems are trained on linguistic performance, linguistic theories are aimed at competence. Such a barrier (...) has traditionally been sidestepped by assuming a fairly close correspondence: performance as competence plus noise. I argue this assumption is unmotivated. Competence and performance can differ arbitrarily. Thus, we should not expect DL models to illuminate linguistic theory. (shrink)

There is an international deep ecology social movement with key terms, slogans, and rhetorical use of language comparable to what we find in other activist “alternative” movements today. Some supporters of the movement partake in academic philosophy and have developed or at least suggested philosophies, “ecosophies,” inspired by the movement. R. A. Watson does not distinguish sufficiently between the movement and the philosophical expressions with academic pretensions. As a result, he falsely concludes that deep ecology implies setting man (...) apart from nature-a kind of “anthropocentrism” in his terminology: humans and only humans have no right to interfere with natural processes. What the deep ecology movement insists on is rather that life on Earth has intrinsic value and that human behavior should and must change drastically-and soon. (shrink)

Historical sciences like paleontology and archaeology have uncovered unimagined, remarkable and mysterious worlds in the deep past. How should we understand the success of these sciences? What is the relationship between knowledge and history? In Scientific Knowledge and the Deep Past: History Matters, Adrian Currie examines recent paleontological work on the great changes that occurred during the Cretaceous period - the emergence of flowering plants, the splitting of the mega-continent Gondwana, and the eventual fall of the dinosaurs - (...) to analyse the knowledge of historical scientists, and to reflect upon the nature of history. He argues that distinctively historical processes are 'peculiar': they have the capacity to generate their own highly specific dynamics and rules. This peculiarity, Currie argues, also explains the historian's interest in narratives and stories: the contingency, complexity and peculiarity of the past demands a narrative treatment. Overall, Currie argues that history matters for knowledge. (shrink)

Despite technological innovations, clinical expertise remains the cornerstone of psychiatry. A clinical expert does not only have general textbook knowledge, but is sensitive to what is demanded for the individual patient in a particular situation. A method that can do justice to the subjective and situation-specific nature of clinical expertise is ethnography. Effective deep brain stimulation (DBS) for obsessive-compulsive disorder (OCD) involves an interpretive, evaluative process of optimizing stimulation parameters, which makes it an interesting case to study clinical expertise. (...) The aim of this study is to explore the role of clinical expertise through an ethnography of the particular case of DBS optimization in OCD. In line with the topic of the special issue this article is a part of, we will also use our findings to reflect on ethnography as a method to study complex phenomena like clinical expertise. This ethnography of DBS optimization is based on 18 months of participant observation and nine in-depth interviews with a team of expert clinicians who have been treating over 80 OCD patients since 2005. By repeatedly observing particular situations for an extended period of time, we found that there are recurrent patterns in the ways clinicians interact with patients. These patterns of clinical practice shape the possibilities clinicians have for making sense of DBS-induced changes in patients’ lived experience and behavior. Collective established patterns of clinical practice are dynamic and change under the influence of individual learning experiences in particular situations, opening up new possibilities and challenges. We conclude that patterns of clinical practice and particular situations are mutually constitutive. Ethnography is ideally suited to bring this relation into view thanks to its broad temporal scope and focus on the life-world. Based on our findings, we argue that clinical expertise not only implies skillful engagement with a concrete situation but also with the patterns of clinical practice that shape what is possible in this specific situation. Given this constraining and enabling role of practices, it is important to investigate them in order to find ways to improve diagnostic and therapeutic possibilities. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing (...) to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Sociological research on moral panics, long understood as “struggles for cultural power,” has focused on the social groups and media conditions that enable moral panics to emerge, and on the consequences of moral panics for the social control systems of societies. In this article I turn instead to modeling the specific cultural process of how the conditions for a moral panic are turned into an actual moral panic, moving the understanding of moral panic away from its Durkheimian origins and towards (...) a process-relational cultural sociology. Drawing on Roland Barthes’ theory of myth and Kenneth Burke’s dramatism, the paper posits the cultural process of resignification via synecdoche and metanarrative as the driver of the disproportion, concern, hostility, consensus, and volatility of moral panics. This process can be carefully traced in the case of the Salem Witch Trials; a retrospective reading reveals the same process at work in the “Mods and Rockers” panic analyzed by Stanley Cohen. Beyond moral panics, theorizing resignification as a non-exclusive counterpoint to framing and ideational embeddedness enriches the theoretical repertoire of cultural sociology. “Deep culture” and mythological signification can, using the schema proposed here, be understood as practical accomplishments—rhetorical responses to particular situations that, when performed successfully, legitimate violence and other forms of domination. (shrink)

Although an invasive medical intervention, Deep Brain Stimulation (DBS) has been regarded as an efficient and safe treatment of Parkinson’s disease for the last 20 years. In terms of clinical ethics, it is worth asking whether the use of DBS may have unanticipated negative effects similar to those associated with other types of psychosurgery. Clinical studies of epileptic patients who have undergone an anterior temporal lobectomy have identified a range of side effects and complications in a number of domains: (...) psychological, behavioural, affective and social. In many cases, patients express difficulty adjusting from being chronically ill to their new status as ‘treated’ or ‘seizure free’. This postoperative response adjustment has been described in the literature on epilepsy as the ‘Burden of Normality’ (BoN) syndrome. Most of the discussion about DBS postoperative changes to self is focused on abnormal side effects caused by the intervention (ie, hypersexuality, hypomania, etc). By contrast, relatively little attention is paid to the idea that successfully ‘treated’ individuals might experience difficulties in adjusting to becoming ‘normal’. The purpose of this paper is (1) to articulate the postoperative DBS psychosocial adjustment process in terms of the BoN syndrome, (2) to address whether the BoN syndrome illustrates that DBS treatment poses a threat to the patient’s identity, and (3) to examine whether the current framework for rehabilitation after DBS procedures should be updated and take into account the BoN syndrome as a postoperative self-change response. (shrink)

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision process. (...) Then, the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (...) model are combined to design the SARIMA-BPNN model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the “University-Industrial Research Collaboration,” students will have more practice in the teaching process in response to social needs. “University-Industrial Research Collaboration” guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided. (shrink)

An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse)is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected (...) for advantageous knowledge. From its substrate of Mathematics, the knotted information processing loops determine emergent Physics and thence the evolution of super-emergent Life (biological and artificial intelligence). RGB-tricoloured information is processed in sequence in an Elemental feedback loop (R), then an Operational feedback loop (G), then a Structural feedback loop (B) and back to an Elemental feedback loop (R), and so on around the trefoil in deep learning macrocycles. It is postulated that hierarchical information correspondence from Mathematics through Physics to Life is mapped and conserved within each colour. The substrate of Mathematics has RGB-tricoloured feedback loops which are respectively Algebra (R), Algorithms (G) and Geometry (B). In Mathematics, the trefoil macrocycle is Algebraic Algorithmic Geometry and its correlation system is a Tensor Neural Knot Network enabling Qutrit Entanglement. Emergent Physics has corresponding RGB-tricoloured feedback loops of Quantum Mechanics (R), Quantum Deep Learning (G) and Quantum Geometrodynamics(B). In Physics, the trefoil macrocycle is Quantum Intelligent Geometrodynamics and its correlation system is Quantum Darwinism. Super-emergent Life has corresponding RGB-tricoloured loops of Variation (R), Selection (G) and Heredity (B). In the evolution of Life, the trefoil macrocycle is Variational Selective Heredity and its correlation ecosystem is Darwin’s ecologically “Entangled Bank”. (shrink)

In this article, Heather Greenhalgh-Spencer argues that deep listening is the foundational component of bridge-building; that it is deep listening that foments the trust and desire for action that undergirds our building of bridges. While “listening” is not a new topic, Greenhalgh-Spencer adds to the literature by expanding on what are the essential components of the kind of listening — which she calls “deep listening” — that can lead to ethical action, change, and connection. She identifies desire, (...) care, acknowledgment of difference, acknowledgment of power, and courage as the key components of this form of listening. Furthermore, she aims to provide concrete examples of how deep listening enables — in fact, is the primary foundation for — bridge-building. She begins the article by defining “deep listening” and then moves to an example of deep listening in action, one taken from the context of community-engaged partnerships — a type of bridge-building in which university faculty create partnerships with community members. In taking this approach, Greenhalgh-Spencer provides a clear illustration of the essential role deep listening plays in the ethical bridge-building process. (shrink)