The article presents an ontological analysis of games. In every game one could distinct four constitutive elements: players, game rules, material substratum of the game and intentional world of the game. The last element correspond with make-believe quality of games. These are two kinds of acts of playing (creating the world of the game): performative and kinetic. The article presents an analysis of these two kinds of acts of playing and present the division of games (...) (performative-based/kinetic-based) which is ontologically fundamental and sets two radically different groups of games. Kinetic games are based on physical, kinetic aspect of the player’s bodies and material tools they use in the game. Performative games are based on transmission some pieces of information. This division enable us to indicate some non-trivial facts about games, like implicit presence of laws of physics in kinetic games or double character or rules in performative games. Interesting fact is that although these two groups of games are very different in the terms of kinds of acts of playing (and also kinds of effort connected with these acts) there is at least one performative-kinetic hybrid game – chessboxing. (shrink)

The paper is devoted to controversies in computer games studies connected with game classification and characteristics. Controversies on walking simulators are discussed in depth; also historical roots of these games and their ergodicity.

The deceptive clues in the impossible puzzle film confirm the viewer’s internal expectations and allow retrospective attributing. In the film, a transcendental object negates an internal expectation, causing a retrospective blockage. Retrospectivity does not stop there; the transcendental object reinterpreting deceptive clues in the associative area leads to repeated attribution. This article consists of three parts. First, it discusses impossible puzzle films in the context of complex narrative classification. The following section introduces the Jungian concept of synchronicity and illustrates (...) how it works. The article concludes with a case study of Long Day’s Journey into Night (2018), which contains more complicated puzzles and explains how mind-game narrative techniques create deceptive clues and induce deceptive retrospective attribution. (shrink)

The objective of this essay is to provide the beginning of a principled classification of some of the ways space is intelligently used. Studies of planning have typically focused on the temporal ordering of action, leaving as unaddressed questions of where to lay down instruments, ingredients, work-in-progress, and the like. But, in having a body, we are spatially located creatures: we must always be facing some direction, have only certain objects in view, be within reach of certain others. How (...) we manage the spatial arrangement of items around us is not an afterthought: it is an integral part of the way we think, plan, and behave. The proposed classification has three main categories: spatial arrangements that simplify choice; spatial arrangements that simplify perception; and spatial dynamics that simplify internal computation. The data for such a classification is drawn from videos of cooking, assembly and packing, everyday observations in supermarkets, workshops and playrooms, and experimental studies of subjects playing Tetris, the computer game. This study, therefore, focuses on interactive processes in the medium and short term: on how agents set up their workplace for particular tasks, and how they continuously manage that workplace. (shrink)

In this paper, I discuss the similarity between Wittgenstein’s use of thought experiments and Relativity Theory. I begin with introducing Wittgenstein’s idea of “thought experiments” and a tentative classification of different kinds of thought experiments in Wittgenstein’s work. Then, after presenting a short recap of some remarks on the analogy between Wittgenstein’s point of view and Einstein’s, I suggest three analogies between the status of Wittgenstein’s mental experiments and Relativity theory: the topics of time dilation, the search for invariants, (...) and the role of measuring tools in Special Relativity. This last point will help to better define Wittgenstein’s idea of description as the core of his philosophical enterprise. (shrink)

Over the past two decades, gamblers have begun taking mathematics into account more seriously than ever before. While probability theory is the only rigorous theory modeling the uncertainty, even though in idealized conditions, numerical probabilities are viewed not only as mere mathematical information, but also as a decision-making criterion, especially in gambling. This book presents the mathematics underlying the major games of chance and provides a precise account of the odds associated with all gaming events. It begins by explaining in (...) simple terms the meaning of the concept of probability for the layman and goes on to become an enlightening journey through the mathematics of chance, randomness and risk. It then continues with the basics of discrete probability, combinatorics and counting arguments for those interested in the supporting mathematics. These mathematic sections may be skipped by readers who do not have a minimal background in mathematics; these readers can skip directly to the Guide to Numerical Results to pick the odds and recommendations they need for the desired gaming situation. Doing so is possible due to the organization of that chapter, in which the results are listed at the end of each section, mostly in the form of tables. The chapter titled The Mathematics of Games of Chance presents these games not only as a good application field for probability theory, but also in terms of human actions where probability-based strategies can be tried to achieve favorable results. Through suggestive examples, the reader can see what are the experiments, events and probability fields in games of chance and how probability calculus works there. The main portion of this work is a collection of probability results for each type of game. Each game s section is packed with formulas and tables. Each section also contains a description of the game, a classification of the gaming events and the applicable probability calculations. The primary goal of this work is to allow the reader to quickly find the odds for a specific gaming situation, in order to improve his or her betting/gaming decisions. Every type of gaming event is tabulated in a logical, consistent and comprehensive manner. The complete methodology and complete or partial calculations are shown to teach players how to calculate probability for any situation, for every stage of the game for any game. Here, readers can find the real odds, returned by precise mathematical formulas and not by partial simulations that most software uses. Collections of odds are presented, as well as strategic recommendations based on those odds, where necessary, for each type of gaming situation. The book contains much new and original material that has not been published previously and provides great coverage of probabilities for the following games of chance: Dice, Slots, Roulette, Baccarat, Blackjack, Texas Hold em Poker, Lottery and Sport Bets. Most of games of chance are predisposed to probability-based decisions. This is why the approach is not an exclusively statistical one, but analytical: every gaming event is taken as an individual applied probability problem to solve. A special chapter defines the probability-based strategy and mathematically shows why such strategy is theoretically optimal.". (shrink)

Games occupy a unique and valuable place in our lives. Game designers do not simply create worlds; they design temporary selves. Game designers set what our motivations are in the game and what our abilities will be. Thus: games are the art form of agency. By working in the artistic medium of agency, games can offer a distinctive aesthetic value. They support aesthetic experiences of deciding and doing. -/- And the fact that we play games shows something (...) remarkable about us. Our agency is more fluid than we might have thought. In playing a game, we take on temporary ends; we submerge ourselves temporarily in an alternate agency. Games turn out to be a vessel for communicating different modes of agency, for writing them down and storing them. Games create an archive of agencies. And playing games is how we familiarize ourselves with different modes of agency, which helps us develop our capacity to fluidly change our own style of agency. (shrink)

Games may seem like a waste of time, where we struggle under artificial rules for arbitrary goals. The author suggests that the rules and goals of games are not arbitrary at all. They are a way of specifying particular modes of agency. This is what make games a distinctive art form. Game designers designate goals and abilities for the player; they shape the agential skeleton which the player will inhabit during the game. Game designers work in the (...) medium of agency. Game-playing, then, illuminates a distinctive human capacity. We can take on ends temporarily for the sake of the experience of pursuing them. Game play shows that our agency is significantly more modular and more fluid than we might have thought. It also demonstrates our capacity to take on an inverted motivational structure. Sometimes we can take on an end for the sake of the activity of pursuing that end. (shrink)

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...) potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether (...) a certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have (...) been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 (...) images belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural (...) network, featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (shrink)

„Errors are the greatest obstacles to the progress of science; to correct such errors is of more practical value than to achieve new knowledge,“ asserted Eugen Bleuler. Basic error of several prevailing classification schemes of pathological conditions, as for example ICD-10, lies in confusing and mixing symptoms with diseases, what makes them unscientific. Considering the need to bring order into the chaos and light into terminological obscureness, I introduce the Causal classification of diseases originating from the notion of (...) bodily wholeness and causality of its loss. (shrink)

Abstract cantaloupe and honeydew melons are part of the muskmelon family, which originated in the Middle East. When picking either cantaloupe or honeydew melons to eat, you should choose a firm fruit that is heavy for its size, with no obvious signs of bruising. They can be stored at room temperature until you cut them, after which they should be kept in the refrigerator in an airtight container for up to five days. You should always wash and scrub the rind (...) of your melon before you cut it to remove any dirt or bacteria on the outside. In this paper, cantaloupe classification approach is presented with a dataset that contains approximately 1,312 of Cantaloupes and honeydews. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.74% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then (...) consider three key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at (...) various levels of AI development, namely, before it starts self-improvement, during its takeoff, when it uses various instruments to escape its initial confinement, or after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level. (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass (...) class='Hi'>classification and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

Communication is an inherently strategic matter. This paper introduces simple game theoretic models of information transmission to identify different forms of uncertainty which may pose a problem of trust in testimony. Strategic analysis suggests discriminating between trust in integrity, trust in competence, trust in (the will to invest) effort and trust in honesty. Whereas uncertainty about the sender's honesty or integrity may directly influence a rational receiver's readiness to rely on sender's statements, neither uncertainty about the competence of a (...) sender nor uncertainty about his willingness to invest effort has any direct impact on rational reliance on its own. In this regard, trust in honesty and trust in integrity appear to be more basic than trust in competence or effort. (shrink)

We contend that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning). Arguments from classification of the most common sorts are shown to be based on defeasible reasoning of various kinds represented by patterns of logical reasoning called defeasible argumentation schemes. We show how such schemes can be identified with heuristics, or short-cut solutions to a problem. We examine a variety of arguments (...) of this sort, including argument from abductive classification, argument from causal classification, argument from analogy-based classification and arguments from classification based on generalizations. (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)

This article explores the nature and theoretical import of a hitherto neglected class of fictions which we term ‘self-involving interactive fictions’. SIIFs are interactive fictions, but they differ from standard examples of interactive fictions by being, in some important sense, about those who consume them. In order to better understand the nature of SIIFs, and the ways in which they differ from other fictions, we focus primarily on the most prominent example of the category: video-game fictions. We argue that (...) appreciating the self-involving nature of video-game fictions is key to understanding various otherwise puzzling phenomena concerning the ways in which consumers respond to them. Video-game fictions are, however, far from being the only extant example of this class; and we suggest that the recent philosophical interest in video games would be better focused on the wider class of self-involving interactive fictions. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

Game theory involves deliberating about what to do in light of what other people are likely to do. One of the central frameworks of game theory is the prisoner’s dilemma, in which participants who make rational choices end up in suboptimal outcomes. Using the prisoner’s dilemma to model competition between firms sets the stage for a new and promising approach to business ethics: the market failures approach.

Sign Language Recognition (SLR) aims to translate sign language into text or speech in order to improve communication between deaf-mute people and the general public. This task has a large social impact, but it is still difficult due to the complexity and wide range of hand actions. We present a novel 3D convolutional neural network (CNN) that extracts discriminative spatial-temporal features from photo datasets. This article is about classification of sign languages are not universal and are usually not mutually (...) intelligible although there are also similarities among different sign languages. They are the foundation of local Deaf cultures and have evolved into effective means of communication. Although signing is primarily used by the deaf and hard of hearing, hearing people also use it when they are unable to speak, when they have difficulty speaking due to a health condition or disability (augmentative and alternative communication), or when they have deaf family members, such as children of deaf adults. In this article we use the 43500 image in the dataset in size 64*64 pixel by use CNN Architecture and achieved 100% accuracy. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our (...) neural network, featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

Scientists represent their world, grouping and organizing phenomena into classes by means of concepts. Philosophers of science have historically been interested in the nature of these concepts, the criteria that inform their application and the nature of the kinds that the concepts individuate. They also have sought to understand whether and how different systems of classification are related and more recently, how investigative practices shape conceptual development and change. Our aim in this paper is to provide a critical overview (...) of some of the key developments in this philosophical literature and identify some interesting issues it raises about the prospects of the so-called “special sciences”, including psychiatry, psychology, and the mind-brain sciences more generally, to discover natural kinds. (shrink)

Game theorists tend to model climate negotiations as a so-called ‘tragedy of the commons’. This is rather worrisome, since the conditions under which such commons problems have historically been solved are almost entirely absent in the case of international greenhouse gas emissions. In this paper, I will argue that the predictive accuracy of the tragedy model might not stem from the model’s inherent match with reality but rather from the model’s ability to make self-fulfilling predictions. I then sketch some (...) possible ways to dispel the tragedy, including (1) recognizing some ways the assumptions of the model fail, (2) taking seriously recent work suggesting that increasing greenhouse gas emissions is not in most nations’ own self-interest, and (3) preferring alternative models like collective risk dilemmas, bargaining games, or cooperative models. (shrink)

Using Bernard Suits’s brilliant analysis (contra Wittgenstein) of playing a game, this paper examines the intrinsic value of game-playing. It argues that two elements in Suits’s analysis make success in games difficult, which is one ground of value, while a third involves choosing a good activity for the property that makes it good, which is a further ground. The paper concludes by arguing that game-playing is the paradigm modern (Marx, Nietzsche) as against classical (Aristotle) value: since its (...) goal is intrinsically trivial, its value is entirely one of process rather than product, journey rather than destination. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical image (...) classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

There are well-known problems for formalist accounts of game-play with regards to cheating. Such accounts seem to be committed to cheaters being unable to win–or even play–the game, yet it seems that there are instances of cheaters winning games. In this paper, I expand the discussion of such problems by introducing cases of pre-game cheating, and see how a formalist–specifically a Suitsian–account can accommodate such problems. Specifically, I look at two (fictional) examples where the alleged game-players (...) cheat prior to a game-instance in such as a way as to cast doubt on whether the alleged game-players are truly playing the game. To escape the worries brought about by these examples of pre-game cheating, I will appeal to the concept of nested games. This concept will give us the needed tools to explain how the alleged players are cheating and how the alleged players are players. On the whole, this discussion should help illuminate some important issues with regards to cheating and rules on a Suitsian account of game-play, and help give support for formalist accounts more generally. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

This is a reply to Elisabeth Camp's and Elijah Millgram's probing discussions of "Games and the Art of Agency", in a symposium in Journal of Ethics and Social Philosophy. Millgram argues that games cannot function as a guide to life, because they are too different from life. Games are limited in a special way: in life, we deliberate about what goals we want to take on, but in games, the goals are fixed and given to us. Camp argues that there (...) is nothing particularly special about games, because the fluidity of agency is all over ordinary life too - games are just like ordinary life. I respond to Millgram: we do deliberate about our ends, in games, when we aesthetically reflect on which games to play. And I respond to Camp: games are distinctive in the way they regulate how our reasons flow across the game/life border. To respond to these criticisms, I add some substantial new features to my account of the motivational structure of game-play. (shrink)

This is my reply to commentators in the symposium on my book, GAMES: AGENCY AS ART. The symposium features commentary by Thomas Hurka, Quill Kukla, and Alva Noe, and originally appeared in Analysis 81 (2).

Comparative cognitive science often involves asking questions like ‘Do nonhumans have C?’ where C is a capacity we take humans to have. These questions frequently generate unproductive disagreements, in which one party affirms and the other denies that nonhumans have the relevant capacity on the basis of the same evidence. I argue that these questions can be productively understood as questions about natural kinds: do nonhuman capacities fall into the same natural kinds as our own? Understanding such questions in this (...) way has several advantages: it preserves the intuition that these are substantive empirical questions worth asking; it helps us to understand why they so frequently give rise to disagreements of the kind described; and it provides clues about how to diagnose and resolve them. (shrink)

The paper has a twofold aim. On the one hand, it provides what appears to be the first game-theoretic modeling of Napoleon’s last campaign, which ended dramatically on 18 June 1815 at Waterloo. It is specifically concerned with the decision Napoleon made on 17 June 1815 to detach part of his army against the Prussians he had defeated, though not destroyed, on 16 June at Ligny. Military historians agree that this decision was crucial but disagree about whether it was (...) rational. Hypothesizing a zero-sum game between Napoleon and Blücher, and computing its solution, we show that it could have been a cautious strategy on the former's part to divide his army, a conclusion which runs counter to the charges of misjudgement commonly heard since Clausewitz. On the other hand, the paper addresses methodological issues. We defend its case study against the objections of irrelevance that have been raised elsewhere against “analytic narratives”, and conclude that military campaigns provide an opportunity for successful application of the formal theories of rational choice. Generalizing the argument, we finally investigate the conflict between narrative accounts – the historians' standard mode of expression – and mathematical modeling. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

A. W. F. Edwards famously named “Lewontin’s fallacy” after Richard Lewontin, the geneticist who showed that most human genetic diversity can be found within any given racialized group. “Lewontin’s fallacy” is the assumption that uncorrelated genetic data would be sufficient to classify genotypes into conventional “racial” groups. In this chapter, I argue that Lewontin does not commit the fallacy named after him, and that it is not a genuine fallacy. Furthermore, I argue that when Edwards assumes that stable classification (...) is all it takes to vindicate racial naturalism, he commits a fallacy of his own: Edwards’ fallacy. The ability to create a classificatory system, and then reliably sort things within that system, is not enough to make that system scientifically respectable. I show that Edwards’ fallacy is rife in debates about the existence of human biological races. (shrink)

This paper examines the ways in which social scientific discourse and classification interact with the objects of social scientific investigation. I examine this interaction in the context of the traditional philosophical project of demarcating the social sciences from the natural sciences. I begin by reviewing Ian Hacking’s work on interactive classification and argue that there are additional forms of interaction that must be treated.

In this paper we propose and analyze a game-theoretic model of the epistemology of peer disagreement. In this model, the peers' rationality is evaluated in terms of their probability of ending the disagreement with a true belief. We find that different strategies---in particular, one based on the Steadfast View and one based on the Conciliatory View---are rational depending on the truth-sensitivity of the individuals involved in the disagreement. Interestingly, the Steadfast and the Conciliatory Views can even be rational simultaneously (...) in some circumstances. We tentatively provide some reasons to favor the Conciliatory View in such cases. We argue that the game-theoretic perspective is a fruitful one in this debate, and this fruitfulness has not been exhausted by the present paper. (shrink)

Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy 96% (...) .Testing dataset organized into one folder (age-gender-test) and contains 1676 audio files related to 39 of age and gender categories and 40 epoch. (shrink)

What can the philosophy of agency learn from Nguyen’s book on games? The most important lesson concerns, to use Nguyen’s terms, the ‘layered’ structure of our agency and the ‘fluidity’ requ...

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

It is plausible that there are epistemic reasons bearing on a distinctively epistemic standard of correctness for belief. It is also plausible that there are a range of practical reasons bearing on what to believe. These theses are often thought to be in tension with each other. Most significantly for our purposes, it is obscure how epistemic reasons and practical reasons might interact in the explanation of what one ought to believe. We draw an analogy with a similar distinction between (...) types of reasons for actions in the context of activities. The analogy motivates a two-level account of the structure of normativity that explains the interaction of correctness-based and other reasons. This account relies upon a distinction between normative reasons and authoritatively normative reasons. Only the latter play the reasons role in explaining what state one ought to be in. All and only practical reasons are authoritative reasons. Hence, in one important sense, all reasons for belief are practical reasons. But this account also preserves the autonomy and importance of epistemic reasons. Given the importance of having true beliefs about the world, our epistemic standard typically plays a key role in many cases in explaining what we ought to believe. In addition to reconciling (versions of) evidentialism and pragmatism, this two-level account has implications for a range of important debates in normative theory, including the interaction of right and wrong reasons for actions and other attitudes, the significance of reasons in understanding normativity and authoritative normativity, the distinction between ‘formal’ and ‘substantive’ normativity, and whether there is a unified source of authoritative normativity. (shrink)

Persons with aphasia suffer from a loss of communication ability as a consequence of a brain injury. A small strand of research indicates effec- tiveness of dialogic interventions for communication development for persons with aphasia, but a vast amount of research studies shows its effectiveness for other target groups. In this paper, we describe the main parts of the hitherto technological development of an application named Dialogica that is (i) aimed at facilitating increased communicative participation in dialogic settings for persons (...) with aphasia and other communication disorders, (ii) based on comput- er game technology as well as on theory in dialogic education and argumenta- tion theory, and (iii) designed for mobile devices with larger screens. (shrink)

How do we prepare engineering students to make ethical and responsible decisions in their professional work? This paper presents an approach that enhances engineering students’ engagement with ethical reasoning by simulating decision-making in a complex scenario. The approach has two principal inspirations. The first is Anthony Weston’s scenario-based teaching. Weston’s concept of a scenario is a situation that changes in response to choices made by participants, according to an inner logic. Scenarios can dynamically explore open-ended complex problems without imposing predetermined (...) results, allowing students to apply their ethical reasoning in a more realistic fashion. The second inspiration is Thi Nguyen’s theory of games. According to Nguyen, games are the “art of agency,” meaning that they provide a structure under which some result is agreed to be valuable, along with prescribed ways of achieving that result, in a relatively low-stakes and temporary social arrangement. By playing many games, Nguyen argues, we can develop a “library of agency,” that is to say, a set of values and means to achieving them that can inform how we make high-stakes decisions in real life. Engaging with carefully designed game scenarios, then, can enable students to practice making ethical decisions within a structured framework, which unfolds in response to their choices, while their decisions are driven by some defined goal. This roleplaying game–based approach thus allows students to build their own ethical libraries of agency, in preparation for professional decision-making. The approach is illustrated using a specific example recently deployed in a seminar on engineered living materials (building materials that incorporate living tissues). The scenario uses techniques from tabletop roleplaying games (e.g. Dungeons & Dragons) for outcome resolution, adventure design, and character creation. Through the game, students practice making difficult decisions in a humanitarian context. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

In psychiatry some disorders of cognition are distinguished from instances of normal cognitive functioning and from other disorders in virtue of their surface features rather than in virtue of the underlying mechanisms responsible for their occurrence. Aetiological considerations often cannot play a significant classificatory and diagnostic role, because there is no sufficient knowledge or consensus about the causal history of many psychiatric disorders. Moreover, it is not always possible to uniquely identify a pathological behaviour as the symptom of a certain (...) disorder, as disorders that are likely to differ both in their causal histories and in their overall manifestations may give rise to very similar patterns of behaviour. -/- Consider delusions as an example. It wouldn’t be correct to define delusions as those beliefs people form as a result of a neurobiological deficit and a hypothesis-evaluation deficit (as some versions of the two-factor theory of delusions suggest), because for some delusions no neurobiological deficit may be found, and reasoning biases and motivational factors may be contributors to the formation of the delusion (e.g. McKay et al., 2005). Moreover, it would be a mistake to define delusions as symptoms of schizophrenia alone, because they occur also in other disorders, including dementia, amnesia, and delusional disorders. Thus, aetiological considerations may appear in the description and analysis of delusions, but do not feature prominently in their definition. -/- In this paper I argue that the surface features used as criteria for the classification and diagnosis of disorders of cognition are often epistemic in character. I shall offer two examples: confabulations and delusions are defined as beliefs or narratives that fail to meet standards of accuracy and justification. Although classifications and diagnoses based on features of people’s observable behaviour are necessary at these early stages of neuropsychiatric research, given the variety of conditions in which certain phenomena appear, I shall attempt to show that current epistemic accounts of confabulations and delusions have limitations. Epistemic criteria can guide both research and clinical practice, but fail to provide sufficient conditions for the identification of delusions and confabulations, and fail to demarcate pathological from non-pathological narratives or beliefs. -/- Another limitation of current epistemic accounts – which I shall not address here – is the excessive focus on epistemic faults of confabulations and delusions at the expense of their epistemically neutral or advantageous features (see Bortolotti and Cox, 2009). This may lead to a misconception of delusions and confabulations, and to an oversimplification in the assessment of the needs of people who require clinical treatment for their psychotic symptoms. (shrink)

On Bernard Suits’s celebrated analysis, to play a game is to engage in a ‘voluntary attempt to overcome unnecessary obstacles’. Voluntariness is understood in terms of the players having the ‘lusory attitude’ of accepting the constitutive rules of the game just because they make possible playing it. In this paper I suggest that the players in Netflix’s hit show Squid Game play the ‘squid games’, but they do not do so voluntarily; they are forced to play. I (...) argue that this means that we should rethink Suits’s analysis by claiming that all that is needed for players to play is that someone has put the rules in force for them for the reason that it makes playing the game possible. I then illustrate the virtues of the revised analysis by showing how it helps us to defuse Hurka’s recent counterexamples to Suits’s view. (shrink)