If this can be seen as a long way from the beginning of the ancient history, where humans have envisioned different new things and then invented them to make their life’s working smoother and easier, then it can be found that they have attributed their discoveries to various aspects and names of Gods and tried to signify their belief in the form of portraying the God’s powers through the nature of their discovery. Rather, in much modern times, when humans have (...) developed the natural insights and prorated those in the form of a much more logically biased discovery, then, the atheists have started to dominate the scientific arena for their foreplay in the work of their discoveries. However, as time passes by, five special types of research communities have gathered to allocate their discovered data’s in the respective norms as such, i) Atheists to whom, there is in essence no God and nothing in this universe can be attributed to them. ii) Believer of only one God as a whole, where the principle of religious biasing is effluent and supreme in their regards of the physical laws associated with the life and stimuli, iii) Believer of multiple God’s as a whole which poses no such scientific evaluation but can be thought of as a conjugate mechanism for the creator of the universe, iv) A divine supreme, where there is exactly no God, but the whole universe is a manifestation of the nature, where nature indirectly focuses the presence of God’s or the supreme creator that maintains symmetry and working of the universe, v) The not-so-sure about mechanism of the God’s presence, where the researcher sometimes tends to belief in God when something magical happens and attribute all to their divine grace, or in times of failures where they accused them for not being philosophically intrinsic as to their own needs, and in times with the believe that, ‘we won’t believe what we can’t see!’, either in the middle of the good or bad moments or in general in a casual and carefree mode. But, this should be kept in mind, that, the Eastern mysticism has always signifies God’s action in terms of every scientific actions with a proper mythological essence which when accompanied by knowledge produces something so scientific in the ancient of the days, that modern day researchers also have had no options left than to attribute affirmation to the fantastic achievements in the ‘so’ ancient times discovery with a strict modern effluence. Therefore, to model this paper, I have gathered data’s and ideas from researchers of various fields mostly (PhDs) in their respective discipline, so as to emulate their minds in the norms of God and its associated beliefs. (shrink)

Disagreements that resist rational resolution, often termed “deep disagreements”, have been the focus of much work in epistemology and informal logic. In this paper, I argue that they also deserve the attention of philosophers of mathematics. I link the question of whether there can be deep disagreements in mathematics to a more familiar debate over whether there can be revolutions in mathematics. I propose an affirmative answer to both questions, using the controversy over Shinichi Mochizuki’s work (...) on the abc conjecture as a potential example of both phenomena. I conclude by investigating the prospects for the resolution of mathematical deep disagreements in virtue-theoretic approaches to informal logic and mathematical practice. (shrink)

Deep brain stimulation has been of considerable interest to bioethicists, in large part because of the effects that the intervention can occasionally have on central features of the recipient’s personality. These effects raise questions regarding the philosophical concept of authenticity. In this article, we expand on our earlier work on the concept of authenticity in the context of deep brain stimulation by developing a diachronic, value-based account of authenticity. Our account draws on both existentialist and essentialist approaches (...) to authenticity, and Laura Waddell Ekstrom’s coherentist approach to personal autonomy. In developing our account, we respond to Sven Nyholm and Elizabeth O’Neill’s synchronic approach to authenticity, and explain how the diachronic approach we defend can have practical utility, contrary to Alexandre Erler and Tony Hope’s criticism of autonomy-based approaches to authenticity. Having drawn a distinction between the authenticity of an individual’s traits and the authenticity of that person’s values, we consider how our conception of authenticity applies to the context of anorexia nervosa in comparison to other prominent accounts of authenticity. We conclude with some reflections on the prudential value of authenticity, and by highlighting how the language of authenticity can be invoked to justify covert forms of paternalism that run contrary to the value of individuality that seems to be at the heart of authenticity. (shrink)

We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building successful (...) models requires pragmatic understanding to apply modelling strategies that encourage the model to learn data patterns that will facilitate reliable generalisation. (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 adaptation’ refers to the personal and collective changes that might help us to prepare for – and live with – a climate-influenced breakdown or collapse of our societies. It is a framework for responding to the terrifying realization of increasing disruption by committing ourselves to reducing suffering while saving more of society and the natural world. This is the first book to show how professionals across different sectors are beginning to incorporate the acceptance of likely or unfolding societal (...) breakdown into their work and lives. They do not assume that our current economic, social and political systems can be made resilient in the face of climate change but, instead, they demonstrate the caring and creative ways that people are responding to the most difficult realization with which humanity may ever have to come to terms. Edited by the originator of the concept of deep adaptation, Jem Bendell, and a leading climate activist and strategist, Rupert Read, this book is the essential introduction to the concept, practice and emerging global movement of Deep Adaptation to climate chaos. (shrink)

I intend to bring recent work applying virtue theory to the study of argument to bear on a much older problem, that of disagreements that resist rational resolution, sometimes termed "deep disagreements". Just as some virtue epistemologists have lately shifted focus onto epistemic vices, I shall argue that a renewed focus on the vices of argument can help to illuminate deep disagreements. In particular, I address the role of arrogance, both as a factor in the diagnosis of (...) deep disagreements and as an obstacle to their mutually acceptable resolution. Arrogant arguers are likely to make any disagreements to which they are party seem deeper than they really are and arrogance impedes the strategies that we might adopt to resolve deep disagreements. As a case in point, since arrogant or otherwise vicious arguers cannot be trusted not to exploit such strategies for untoward ends, any policy for deep disagreement amelioration must require particularly close attention to the vices of argument, lest they be exploited by the unscrupulous. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is (...) a technique used to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

In 2015, Fainos Mangena published an essay entitled “How Applicable is the Idea of Deep Ecology in the African Context?” where he presented a number of arguments to support his thesis that deep ecology as discussed in the West has no place in the African context. Mangena later presented a counter-version of deep ecology that he claims is based on African philosophy. In this paper, I interrogated Mangena’s arguments for rejecting deep ecology and found that they (...) were based on certain erroneous presuppositions. Further, I developed a critique of Mangena’s Shona version of deep ecology which shows it to be impractical, unappealing, and based on a misunderstanding of the true nature of the modern African environment. I employ the method of conversationalism in this work. Keywords: Deep Ecology, African Environmental Ethics, Fainos Mangena. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Deep decarbonization – slashing global greenhouse gas emissions to net-zero – now dominates global climate policy. Two recent books assess feasible routes to achieve deep decarbonization. Bill Gates’ How to Avoid a Climate Disaster explains in depth why deep decarbonization requires significant innovations in tech, and Danny Cullenward and David Victor’s Making Climate Policy Work emphasizes the importance of policy innovation (beyond carbon pricing) for driving clean tech breakthroughs. In this critical review essay, I summarize and (...) assess both books. In the final section, I raise several normative questions, which the pair of books might lead us to. (shrink)

Case reports about patients undergoing Deep Brain Stimulation (DBS) for various motor and psychiatric disorders - including Parkinson’s Disease, Obsessive Compulsive Disorder, and Treatment Resistant Depression - have sparked a vast literature in neuroethics. Questions about whether and how DBS changes the self have been at the fore. The present chapter brings these neuroethical debates into conversation with recent research in moral psychology. We begin in Section 1 by reviewing the recent clinical literature on DBS. In Section 2, we (...) consider whether DBS poses a threat to personal identity. In Section 3 we argue for engagement with recent empirical work examining judgements of when identity changes. We conclude in Section 4 by highlighting a range of ethical issues raised by DBS, including various cross-cultural considerations. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. (...) Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Arthur C. Danto proposes a complex and controversial relationship between surface and deep interpretations in The Philosophical Disenfranchisement of Art (1986). We detail the analogy between understanding human actions and interpreting works of art that both develops a motivation for Danto's view and clarifies it. We object to the most plausible version of content dependency among surface and deep interpretations and in so doing, we also clarify the way in which an interpretation is constitutive of an artwork.

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (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)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of (...) expectations: the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

Charles Carlini interviews Nathan Salmón about the philosophical work of his mentor and friend, the late Saul Kripke, one of the foremost philosophers of the 20th Century.

Naess and Sessions “Deep Ecology Platform” provided a loose framework for a movement that was gaining momentum after a series of successful social and political actions and events throughout the 1970s and early 1980s. Most of the points are essentially adopted from Naess’ earlier work, which provided the basis for a number of the core concepts expressed in the later eight points and is largely an expression of a movement that sought to create a shift in consciousness of (...) society towards the natural world. It is probably better understood as a lose framework that encapsulates a world view and perspective of nature which is better understood as an aesthetic movement rather than a moral or ethical movement. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

From the local level to international politics, deliberation helps to increase mutual understanding and trust, in order to arrive at political decisions of high epistemic value and legitimacy. This book gives deliberation a dynamic dimension, analysing how levels of deliberation rise and fall in group discussions, and introducing the concept of 'deliberative transformative moments' and how they can be applied to deeply divided societies, where deliberation is most needed but also most difficult to work. Discussions between ex-guerrillas and ex-paramilitaries (...) in Colombia, Serbs and Bosnjaks in Bosnia-Herzegovina, and police officers and locals in Brazilian favelas are used as case studies, with participants addressing how peace can be attained in their countries. Allowing access to the records and transcripts of the discussions opens an opportunity for practitioners of conflict resolution to apply this research to their work in trouble spots of the world, creating a link between the theory and practice of deliberation. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is (...) used as a technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

Recent work in judgment and decisionmaking has stressed that institutions, like individuals, often rely on decisionmaking heuristics. But most of the institutional decisionmaking heuristics studied to date are highly firm- and industry-specific. This contrasts to the individual case, in which many heuristics are general-purpose rules suitable for a wide range of decision problems. Are there also general-purpose heuristics for institutional decisionmaking? In this paper, I argue that a number of methods recently developed for decisionmaking under deep uncertainty have (...) a good claim to be understood as general-purpose decisionmaking heuristics suitable for a broad range of institutional decision problems. (shrink)

The ocean terrain spanning the globe is vast and complex—far from an immense flat plain of mud. To map these depths accurately and wisely, we must understand how cartographic abstraction and generalization work both in analog cartography and digital GIS. This chapter explores abstraction practices such as selection and exaggeration with respect to mapping the oceans, showing significant continuity in such practices across cartography and contemporary GIS. The role of measurement and abstraction—as well as of political and economic power, (...) and sexual and personal bias—in these sciences is illustrated by the biographies of Marie Tharp and Bruce Heezen, whose mapping of the Mid-Atlantic Ridge precipitated a paradigm shift in geology. (shrink)

Deep Democracy draws upon the insights of American thinkers whose work has received less attention than the "holy trinity" of Peierce, James and Dewey, in order to investigate current philosophical problems and questions. The work does carry out a sustained interaction with the work of Dewey, in the course of exploring the nature of, obstacles to, and prospects for strengthening the fabric of democracy in the contemporary world. But Green also puts Dewey in conversation with Jane (...) Addams, Alain Locke, Martin Luther King, Malcolm X and Cornel West. Green draws these thinkers together—and draws out the links between them—in order to develop her notion of “deep democracy.”. (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)

The work of Emmanuel Levinas has been both abundantly recognized and criticized in moral philosophy. This Janus-faced attitude is also present in ecological theories, which find fertile ground in Levinas’ thought without being able to explain its apparent anthropocentrism. Opposing hermeneutical paths tend to focus either on otherness as an absolute alterity, implying a potentially unlimited responsibility for all alterities, or on otherness as a re-foundation of humanism, leading to the conclusion that responsibility is unlimited only among humans. Here (...) I seek to disentangle Levinas from these two extreme interpretations, first by reviewing ecological readings of his philosophy, in both the Francophone and Anglophone spheres, and second by analyzing specific Levinasian terms that might be particularly helpful for a non-anthropocentric or deep ecology. The main results of this investigation are a distinction between the ethics of responsibility and the ethics of vulnerability, an explanation of Levinasian compulsory or methodological humanism, and a schematic model of sensibility as enjoyment/vulnerability capable of supporting deep ecology without the traditional notions of free will, rights, or values. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

Using hokku poet Basho’s aesthetics of wandering, as defined by Thomas Heyd, I argue that, by detailing the excruciating pointlessness of work undertaken according to commands that take little or no account of their feasibility, Richard Flanagan’s novel, The Narrow Road to the Deep North (which takes its title from Basho's work) transforms the features of this aesthetics into the lived experience of prisoners of war on the ‘line’. In doing so, Flanagan transfers Basho’s aesthetics into a (...) represented actuality through the privileging of subjectivity over identity and the dissolution of the body on the line. The three prongs to Basho’s aesthetics are found in Flanagan’s novel. In this, Flanagan is identifying the complexity of meanings evident in the terminology of such aesthetics, rendering what appears positive in the context of Basho’s poetry negative in its practical application as this is articulated through the prisoners’ wartime experiences. Rather than being formative, Flanagan’s novel suggests wartime experience has a complexly ‘opposite’ effect. This is apparent in the complications of identity represented in post-war terms as a disunity (rather than a coherent unity), as articulated through the use of spatial metaphors that reverse the formative intensities of subjectivity and body through symbolic acts of dispersal and dissolution. (shrink)

The question of how to conceive and represent the context of work is explored from the theoretical perspective of distributed cognition. It is argued that to understand the office work context we need to go beyond tracking superficial physical attributes such as who or what is where and when and consider the state of digital resources, people’s concepts, task state, social relations, and the local work culture, to name a few. In analyzing an office more deeply, three (...) concepts are especially helpful: entry points, action landscapes, and coordinating mechanisms. An entry point is a structure or cue that represents an invitation to enter an information space or office task. An activity landscape is part mental construct and part physical; it is the space users interactively construct out of the resources they find when trying to accomplish a task. A coordinating mechanism is an artifact, such as a schedule or clock, or an environmental structure such as the layout of papers to be signed, which helps a user manage the complexity of his task. Using these three concepts we can abstract away from many of the surface attributes of work context and define the deep structure of a setting—the invariant structure that many office settings share. A long-term challenge for context-aware computing is to operationalize these analytic concepts. (shrink)

In this paper, we focus on the most efficacious AI applications for life extension and anti-aging at three expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the existing research and commercial work performed by a select number of startups and academic projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension are geroprotector-combination discovery, detection of aging biomarkers, and personalized anti-aging therapy. These advances could help currently (...) living people reach longevity escape velocity and survive until more advanced AI appears. When AI comes close to human level, the main contribution to life extension will come from AI integration with humans through brain-computer interfaces, integrated AI assistants capable of autonomously diagnosing and treating health issues, and cyber systems embedded into human bodies. Lastly, we speculate about the more remote future, when AI reaches the level of superintelligence and such life-extension methods as uploading human minds and creating nanotechnological bodies may become possible, thus lowering the probability of human death close to zero. We suggest that medical AI based superintelligence could be safer than, say, military AI, as it may help humans to evolve into part of the future superintelligence via brain augmentation, uploading, and a network of self-improving humans. Medical AI’s value system is focused on human benefit. (shrink)

Plato's entire body of work has survived intact to this day, decisively influencing Western culture. For Plato, dialogue is the only tool capable of highlighting the research character of philosophy, the key element of his thinking. Certainly the written word is more precise and in-depth than the oral one, but the oral discourse allows an immediate exchange of views on the subject under discussion. The main protagonist of the dialogues is Socrates, except for the last dialogues where he is (...) assigned a secondary role, disappearing completely in Laws and Epinomis. Platonic dialogues have been grouped by many commentators in various classifications. According to some, a classification would be chronological: the first dialogues would be characterized by the strong influence of Socrates, those of maturity in which he would have developed the theory of ideas, and the last period in which he felt the need to defend his own conception from attacks on the address of his philosophy, realizing a deep self-criticism of the theory of Ideas. -/- DOI: 10.13140/RG.2.2.27508.01922. (shrink)

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)

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some (...) theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. (shrink)

Existing studies of the multi-group dynamics of prejudiced societies focus on the social- psychological knowledge behind the relevant processes. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on prosperity. Levels of prosperity are tracked for individuals as well as larger social structures including groups and factions. We model social interactions using the Continuous Prisoner's Dilemma (CPD) and a new agent type called a prejudiced agent. Our simulations show that even modeling prejudice (...) as an exclusively out-group phenomenon nonetheless generates implicit in-group promotion, leading to higher relative prosperity of a prejudiced population. This skew in prosperity is seen to be correlated to factors such as size difference between groups, and the fraction of prejudiced agents in a group. Our model is a step towards a deeper understanding of the origins, propagation, and ramifications of prejudice through simulative studies grounded in apt theoretical backgrounds. (shrink)

Academics should produce quality scholarly research. However, the demands of the marketised, neoliberal higher education institution and the increase in the academic’s bureaucratic and administrative tasks do not allow for adequate engagement with the deep work and slow forms of scholarship that are needed to produce cutting-edge and insightful research. Many academics find it challenging to think critically and creatively under such conditions, yet they are unwilling to fill their time with shallow work instead. Thus, they are (...) torn between producing an institutionally-determined number of research outputs, and striving towards producing quality scholarly work that advances knowledge in the academic’s discipline. Reading groups serve to rework this tension by providing a communal and scheduled place and mind-space for reading major academic works. Reading takes time and persistence. We find that setting aside time and creating creative spaces for reading groups offer an opportunity for silent revolt against the pressures of the higher education institution. In this (post)qualitative inquiry we diffractively read – “through one another” – two autoethnographical accounts to experiment with our experiences of various reading groups over a period of three years while positioning ourselves in relation to Barad’s notion of diffraction. This speculative experimentation entails a myriad of ideas and methods that serve to decentre hegemonic, monolithic knowledge systems through seeking alternative ways of knowing, and recognises the importance of the entanglement of matter and meaning in tracing (and countering) the social relations produced in neoliberalist contexts. Such (re)thinking is thus a vital counterpoint to the neoliberal obsessions of the higher education context, (re)directing the scholar to new non-autonomous and mutable landscapes. (shrink)

This article describes various ways actors in Kashmiri carpet weaving practice deploy a range of artifacts, from symbolic, to material, to hybrid, in order to achieve diverse cognitive accomplishments in their particular task domains: information representation, inter and intra-domain communication, distribution of cognitive labor across people and time, coordination of team activities, and carrying of cultural heritage. In this repertoire, some artifacts position themselves as naïve tools in the actors’ environment to the point of being ignored; however, their usage-in-context unfolds (...) their cognitive involvement in the tasks. These usages-in-context are shown through artifact analysis of their routine, improvised, and opportunistic uses, where cognitive artifacts like talim—the central artifact of this practice—are shown to play not only multifunctional roles beyond representation, but are also complemented by trade-specific skills bearing strong cognitive implications in a task. (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)

Despite the fact that Mary Shelley and E. M. Cioran have never been previously analyzed in the same context (they belong not only to different ages but also to divergent genres), we will find that they share at least two similar themes. The motif of solitude, common among Romantic poets (Coleridge, Byron, Poe), finds a deep expression in Shelley’s Frankenstein and in Cioran’s early oeuvre. A more thorough investigation of the British novelist and the Romanian-French self-described “anti-philosopher” discloses that (...) hatred (a theme that is not frequently researched from a philosophical point of view) might be another of their obsessions. The concept of the nihilistic not-man becomes useful when we will follow the tripartite shape of hatred (of others, of myself and of God) not only in literature or philosophy but also in pop culture. (shrink)

Most literature on religious beliefs and disagreements among traditions focuses on a bit of mainstream assumptions: religions should be construed in substantive terms; religions are to be individuated by their core belief systems; adherents to a single tradition assent to the same belief system; religious beliefs have factual content; incompatible religious beliefs cannot be both true; and so on. In my work I question all these claims in order to defend a non kantian approach to deep pluralism. In (...) the first part I develop a narrative theory of doxastic practices. My fundamental intuition is that ambiguity, vagueness, and indeterminacy of meaning are non amendable features of any ordinary belief. Consequently, no proposition has a definite meaning, and there is no a priori reason to assume that if two believers assume the same belief, they both hold the same content. What I'm trying to do is to argue in support of a realist epistemology, without assuming a normative and rationalist stance. My evidential body is mainly drawn from psychology and psychoanalysis. In the second part I apply such a narrative theory to the study of the doxastic character of religions. I reject both functionalist and substantive approaches to religion, and I defend my own viewpoint which I label experentialism. After providing a characterization of religious beliefs, I refute how Alston, Ward and Hick account for the the doxastic features of religions. I then propose a definition of religious diversity which turns out to be alternative to the mainstream one. I work by empirical evidence from semiotics, sociology of religion and history of religions. In the third and conclusive part of my book, I give reasons against the mainstream approach to religious diversity, and I explain how my definition can be more appropriate to the sociological study of religious beliefs than the mainstream one. Finally, I provide an account of deep pluralism, I show that my approach to religious epistemology and religions is compatible with (and recommended by) deep pluralism, I differentiate kantian from non kantian pluralism, and I explain why non kantian deep pluralism resists the traditional objections to pluralism. Throughout the book I discuss relevant materials from Hinduism, Judaism, Christianity, and Islam. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular (...) analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

The work of Thomas White represents a systematic attempt to combine the best of the new science of the seventeenth century with the best of Aristotelian tradition. This attempt earned him the criticism of Hobbes and the praise of Leibniz, but today, most of his attempts to navigate between traditions remain to be explored in detail. This paper does so for his ontology of accidents. It argues that his criticism of accidents in the category of location as entities over (...) and above substances was likely aimed at Francisco Suárez, and shows how White’s worries about the analysis of location were linked with his broader cosmological views. White rejects real qualities, but holds that the quantity of a substance is somehow distinct from its bearer. This reveals a common ground with some of his scholastic interlocutors, but lays bare a deep disagreement with thinkers like Descartes on the nature of matter. (shrink)

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a (...) dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. when such (...) failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring. (shrink)

This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers whose research addresses the topic of decision making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision Making developed by Robert Lempert and colleagues at RAND. We discuss decision-theoretic and voting-theoretic motivations for robust satisficing, (...) then use these motivations to select among candidate formulations of the robust satisficing norm. We also discuss two challenges for robust satisficing: whether the norm might in fact derive its plausibility from an implicit appeal to probabilistic representations of uncertainty of the kind that deep uncertainty is supposed to preclude; and whether there is adequate justification for adopting a satisficing norm, as opposed to an optimizing norm that is sensitive to considerations of robustness. GPI Working Paper No. 15-2020. (shrink)

The work of Thomas Kuhn has been very influential in Anglo-American philosophy of science and it is claimed that it has initiated the historical turn. Although this might be the case for English speaking countries, in France an historical approach has always been the rule. This article aims to investigate the similarities and differences between Kuhn and French philosophy of science or ‘French epistemology’. The first part will argue that he is influenced by French epistemologists, but by lesser known (...) authors than often thought. The second part focuses on the reactions of French epistemologists on Kuhn’s work, which were often very critical. It is argued that behind some superficial similarities there are deep disagreements between Kuhn and French epistemology. This is finally shown by a brief comparison with the reaction of more recent French philosophers of science, who distance themselves from French epistemology and are more positive about Kuhn. Based on these diverse appreciations of Kuhn, a typology of the different positions within the philosophy of science is suggested. (shrink)

This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The (...) class='Hi'>work has five chapters. Chapter 1 lays down its conceptual bases: it explains what learning is taken to be in the current philosophical debate and it points out how Bayesian cognitive science (particularly in its predictive processing formulations) might be well-suited to capture the kind of learning involved in our engagement with the arts. The following chapters test this latter hypothesis with respect to particular art forms, namely literature and literary language (Chapter 2), narrative (Chapter 3), and visual art, music and motor activities (Chapter 4). The fine-grained discussions conducted in each of these areas will enable us to see that the relationship between art and learning is indeed fundamental and pervasive. The final chapter (Chapter 5) examines the consequences of this fact for our understanding of the role of art in our epistemic practices, its ultimate usefulness and value, and its place in the interdisciplinary study of the human mind. The upshot is a novel and wide-ranging picture, both philosophically informed and empirically sound, that bypasses many of the problems and dead ends of the current philosophical debate on the topic and captures the deep sense in which art and learning are interrelated. (shrink)

The relation between Martin Heidegger and radical environmentalism has been subject of discussion for several years now. On the one hand, Heidegger is portrayed as a forerunner of the deep ecology movement, providing an alternative for the technological age we live in. On the other, commentators contend that the basic thrust of Heidegger’s thought cannot be found in such an ecological ethos. In this article, this debate is revisited in order to answer the question whether it is possible to (...) conceive human dwelling on earth in a way which is consistent with the technological world we live in and heralds another beginning at the same time. Our point of departure in this article is not the work of Heidegger but the affordance theory of James Gibson, which will prove to be highly compatible with the radical environmentalist concept of nature as well as Heidegger’s concept of the challenging of nature. (shrink)