This paper is an enquiry into the logical, metaphysical, and physical possibility of time travel understood in the sense of the existence of closed worldlines that can be traced out by physical objects. We argue that none of the purported paradoxes rule out time travel either on grounds of logic or metaphysics. More relevantly, modern spacetime theories such as general relativity seem to permit models that feature closed worldlines. We discuss, in the context of Gödel's infamous argument for (...) the ideality of time based on his eponymous spacetime, what this apparent physical possibility of time travel means. Furthermore, we review the recent literature on so-called time machines, i.e., of devices that produce closed worldlines where none would have existed otherwise. Finally, we investigate what the implications of the quantum behaviour of matter for the possibility of time travel might be and explicate in what sense time travel might be possible according to leading contenders for full quantum theories of gravity such as string theory and loop quantum gravity. (shrink)

Theorem (Will time machines be build?). The probability that in future a time machine that can travel to the past would be build is very low.

Thinking about time travel is an entertaining way to explore how to understand time and its location in the broad conceptual landscape that includes causation, fate, action, possibility, experience, and reality. It is uncontroversial that time travel towards the future exists, and time travel to the past is generally recognized as permitted by Einstein’s general theory of relativity, though no one knows yet whether nature truly allows it. Coherent time travel stories have added flair to (...) traditional debates over the metaphysical status of the past, the reality of temporal passage, and the existence of free will. Moreover, plausible models of time travel and time machines can be used to investigate the subtle relation between space-time structure and causality. -/- It surveys some philosophical issues concerning time travel and should serves as a quick introduction. It includes a new and improved way to define a time machine. (shrink)

We address the question of whether it is possible to operate a time machine by manipulating matter and energy so as to manufacture closed timelike curves. This question has received a great deal of attention in the physics literature, with attempts to prove no- go theorems based on classical general relativity and various hybrid theories serving as steps along the way towards quantum gravity. Despite the effort put into these no-go theorems, there is no widely accepted definition of a (...) time machine. We explain the conundrum that must be faced in providing a satisfactory definition and propose a resolution. Roughly, we require that all extensions of the time machine region contain closed timelike curves; the actions of the time machine operator are then sufficiently "potent" to guarantee that closed timelike curves appear. We then review no-go theorems based on classical general relativity, semi-classical quantum gravity, quantum field theory on curved spacetime, and Euclidean quantum gravity. Our verdict on the question of our title is that no result of sufficient generality to underwrite a confident "yes" has been proven. Our review of the no-go results does, however, highlight several foundational problems at the intersection of general relativity and quantum physics that lend substance to the search for an answer. (shrink)

The concept of time-travel is a modern idea which combines the imaginary signification of rational domination, the imaginary signification of technological omnipotence, the imaginary concept of eternity and the imaginary desire for immortality. It is a synthesis of central conceptual schemata of techno-science, such as the linearity and homogeneity of time, the radical separation of subjectivity from the world, the radical separation of the individual from his/her social-historical environment. The emergence of this idea, its spread during the 20th (...) century as a major theme of science fiction literature alongside its dissemination as a scientific hypothesis, its popularity with both the public and the scientific community, are indications of the religious role of techno-science. It is my opinion, finally, that, as a chimera, time-travel is non-feasible and impossible. In order to support my claims, I will briefly outline the origins of the time-travel concept and its epistemological and metaphysical/ontological conditions. If these conditions prove to be absurd, the logical impossibility of time-travel will have been demonstrated. (shrink)

This provocation critiques the notion of long-term thinking and the claims of its proponents that it will help address failures in dominant conceptions of time, particularly in regard to environmental crises. Drawing on analyses of the Clock of the Long Now and Kim Stanley Robinson’s The Ministry for the Future, the article suggests that we be more wary of the concept’s use in what we might call chronowashing. Like the more familiar greenwashing, where environmental issues are hidden by claims (...) to be addressing the problem, the article explores how these examples of long-term thinking distract from extractivism, racism, and environmental injustice, making it harder to address the complexities involved. In particular, the article discusses examples where long-term thinking provides a veneer of environmental concern that actually disconnects from the work of building more equitable forms of relation. As a contrast, the article’s author asks: What is lost when we diagnose a problem as arising due to short-term thinking and propose long-term thinking as the solution? Against chronowashed environmental time, the author argues for more complex approaches that explicitly take into account the temporalities of inequality, political organization, ethical responsibilities and much else. The article engages with approaches to time that foreground the work needed to create time and move ethically within it, including Charles W. Mills’s white time and Kyle Powys Whyte’s kinship time. The author suggests that a stronger emphasis on the temporality of community, solidarity, and coalition—versus what James Hatley and Deborah Bird Rose have described as temporal narcissism—can better foreground the kinds of work that needs to be done, particularly by those with privilege. (shrink)

Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...) argue that time-tested democratic procedures, such as deliberation and aggregation by voting, provide safeguards that are effective against human and machine advisor shortcomings alike. Additional protective measures may include custom training for advisor LLMs or boosting representatives’ competencies in query formulation. Implementation of adversarial proceedings in which LLM advisors would debate each other and provide dissenting opinions is likely to yield further epistemic benefits. Overall, promising interventions that would mitigate the LLM risks appear feasible. Machine advisors could thus empower human decision-makers to make more autonomous, higher-quality decisions. On this basis, I defend the hypothesis that LLMs’ careful integration into policymaking could augment democracy’s ability to address today’s complex social problems. (shrink)

This paper aims to explore the nature of Zeno-machines by examining their conceptual coherence, from the perspective of contemporary theories on the passage of time. More specifically, it will analyse the following questions: Are Zeno-machines and supertasks coherent if we adopt the eternalist theory of time? What conclusions can be drawn from choosing the eternalist thesis, or the presentist thesis, when examining Zeno-machines? To this end, an overview of the opposing theories of time is (...) provided, alongside the usual objections to Zeno-machines and their theoretical foundations from Zeno’s dichotomy paradox. Keywords: Zeno-machines, philosophy of time, supertasks, Zeno’s paradox. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. It (...) is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)

Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...) South Korea Section 2 From the Outside In: Intelligent Machine Technologies as a Window on Human Morality both as Evolved and as Evident in Internet Discourse, Today Chapter 4 The Emergence of Arti cial Autonomy: A View from the Foothills of a Challenging Climb ............. 51 Fernando da Costa Cardoso, Universidade Nova de Lisboa, Portugal Luís Moniz Pereira, Universidade Nova de Lisboa, Portugal Chapter 5 Semantic Analysis of Bloggers Experiences as a Knowledge Source of Average Human Morality .... 73 Rafal Rzepka, Hokkaido University, Japan Kenji Araki, Hokkaido University, Japan Section 3 From the Inside Out: The Ethics of Human Enhancement from Moral Perception to Competition in the Workplace Chapter 6 Machine Ethics Interfaces: An Ethics of Perception of Nanocognition ............................................... 97 Melanie Swan, Kingston University, UK Chapter 7 Ethical Concerns in Human Enhancement: Advantages in Corporate/Organizational Settings ......... 124 Ben Tran, Alliant International University, USA Section 4 From Far to Near and Near to Far: The Ethics of Distancing Technologies in Education and Warfare Chapter 8 Responsibility and War Machines: Toward a Forward-Looking and Functional Account ................. 152 Jai Galliott, Macquarie University, Australia Chapter 9 Ethical Responsibilities of Preserving Academicians in an Age of Mechanized Learning: Balancing the Demands of Educating at Capacity and Preserving Human Interactivity ................... 166 James E. Willis III, Indiana University, USA Viktoria Alane Strunk, Independent Scholar, USA Section 5 Wrapping Things Up, then Unwrapping Them Again: Integral Visions of Morality in a Technological World, Over Evolutionary Time, with Revolutionary Means, and with Open Questions about the Final Purpose of It All Chapter 10 Bridging Two Realms of Machine Ethics ........................................................................................... 197 Luís Moniz Pereira, Universidade Nova de Lisboa, Portugal Ari Saptawijaya, Universidade Nova de Lisboa, Portugal & Universitas Indonesia, Indonesia Chapter 11 Robots in Warfare and the Occultation of the Existential Nature of Violence ................................... 225 Rick Searle, IEET, USA Chapter 12 Self-Referential Complex Systems and Aristotle’s Four Causes ........................................................ 239 Aleksandar Malecic, University of Nis, Serbia Related References .................................................................................................... ........................ 261 Compilation of References .................................................................................................... ........... 292 About the Contributors .................................................................................................... ................ 325 Index .................................................................................................... ............................................... 329 . (shrink)

A common and enduring early modern intuition is that materialists reduce organisms in general and human beings in particular to automata. Wasn’t a famous book of the time entitled L’Homme-Machine? In fact, the machine is employed as an analogy, and there was a specifically materialist form of embodiment, in which the body is not reduced to an inanimate machine, but is conceived as an affective, flesh-and-blood entity. We discuss how mechanist and vitalist models of organism exist in a more (...) complementary relation than hitherto imagined, with conceptions of embodiment resulting from experimental physiology. From La Mettrie to Bernard, mechanism, body and embodiment are constantly overlapping, modifying and overdetermining one another; embodiment came to be scientifically addressed under the successive figures of vie organique and then milieu intérieur, thereby overcoming the often lamented divide between scientific image and living experience. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical data, enabling faster (...) and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

Autonomous systems are machines that can alter their behavior without direct human oversight or control. How ought we to program them to behave? A plausible starting point is given by the Reduction to Acts Thesis, according to which we ought to program autonomous systems to do whatever a human agent ought to do in the same circumstances. Although the Reduction to Acts Thesis is initially appealing, we argue that it is false: it is sometimes permissible to program a machine (...) to do something that it would be wrong for a human to do. We advance two main arguments for this claim. First, the way an autonomous system will behave can be known in advance. This knowledge can indirectly affect the behavior of other agents, while the same may not be true at the time the system actually executes its programming. Second, a lack of knowledge of the identities of the victims and beneficiaries can provide a justification during the programming phase that would be unavailable to an agent at the time the autonomous system executes its programming. (shrink)

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive (...) analysis of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in “Critiquing the Reasons for Making Artificial Moral Agents”, Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate. (shrink)

Snake encountersin human-populated and wildlife areas pose significant threats to public safety and biodiversity. Each year, many incidents result in snakebites, often leading to serious injuries or fatalities, and frequently result in harm to snakes due to fear-driven responses. Traditional methods forsnake detection, such as visual observation, are typically slow and can lead to delayed or inaccurate responses, increasing the risks associated with human-snake encounters. This study presents a Real-Time Snake Detection and Alert System that employs advanced artificial intelligence (...) (AI) and machine learning (ML) techniques, specifically using YOLO (You Only Look Once), to accurately identify the presence ofsnakes in pre-recorded videos or static images. Oursystem processes these visual inputs to determine if a snake is present, providing an immediate, automated alarm to alert individuals nearby if a snake is detected. This system isintended to be a rapid-response solution for detecting snakesin images or video files, enhancing safety in snake-prone areas by increasing awareness of potential threats without requiring constant live monitoring or additional notifications to authorities. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...) the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

Autonomous systems are machines that can alter their behavior without direct human oversight or control. How ought we to program them to behave? A plausible starting point is given by the Reduction to Acts Thesis, according to which we ought to program autonomous systems to do whatever a human agent ought to do in the same circumstances. Although the Reduction to Acts Thesis is initially appealing, we argue that it is false: it is sometimes permissible to program a machine (...) to do something that it would be wrong for a human to do. We advance two main arguments for this claim. First, the way an autonomous system will behave can be known in advance. This knowledge can indirectly affect the behavior of other agents, while the same may not be true at the time the system actually executes its programming. Second, a lack of knowledge of the identities of the victims and beneficiaries can provide a justification during the programming phase that would be unavailable to an agent at the time the autonomous system executes its programming. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...) the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

The Movie Recommendation System using Machine Learning Techniques is a data-driven approach designed to provide personalized movie suggestions based on user preferences and historical data. This system leverages advanced machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid methods, to predict the most relevant movies for individual users. The system's primary goal is to enhance user experience by recommending movies that align with their tastes, thereby promoting user engagement and satisfaction. The recommendation process starts by collecting user data, such (...) as viewing history, ratings, and genre preferences, which are then processed to generate personalized suggestions. Collaborative filtering analyzes the behavior of similar users to recommend movies based on common interests, while content-based filtering focuses on movie features such as genre, director, actors, and plot summary. By combining both methods, the hybrid approach further refines the recommendation, ensuring diverse and accurate results. The model's effectiveness is evaluated using standard metrics like precision, recall, and F1-score, demonstrating its ability to accurately predict user preferences. The system can be integrated into various platforms, offering real-time recommendations and improving user satisfaction. This project can be expanded by incorporating more sophisticated algorithms, such as deep learning, to further enhance recommendation accuracy and scalability. Overall, this movie recommendation system aims to optimize content discovery and cater to diverse audience needs. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the proposed (...) approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

{June 2018 UPDATE: This work has been greatly surpassed by "Utilizing Future-Viewing Instruments" which will appear in the July 2018 issue of Progress in Physics. It can now be downloaded in PDF form from their website.} This is the full text of the paper that was published as a Kindle book on April 6th, 2014. In most respects, it has since been surpassed by "Potentials of Future-Viewing Machines," which is available here in this repository. Nevertheless, this earlier work addresses (...) minutia and interesting considerations that do not appear elsewhere, and it has a large bibliography, so it remains very useful for research purposes. (shrink)

SMS spam has become a widespread issue, leading to significant inconvenience and security risks for users. Detecting and filtering out such spam messages is crucial for enhancing the user experience and ensuring privacy. TThe dataset used for training and testing the model consists of labeled SMS messages, which are processed using feature extraction techniques such as TF-IDF and word tokenization. Several machine learning algorithms, including Naive Bayes, Support Vector Machine (SVM), and Random Forest, are evaluated to determine the best-performing model (...) for spam detection. The system is trained and tested using a variety of performance metrics, including accuracy, precision, recall, and F1-score. The results show that machine learning models, particularly Naive Bayes, exhibit high accuracy in distinguishing spam from legitimate messages. This system can be implemented in real-time applications such as mobile phones and email services to improve spam detection and reduce unwanted content. By automating the spam filtering process, the system enhances the efficiency and reliability of communication systems. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Heart disease remains one of the leading causes of mortality worldwide. Early prediction and diagnosis are critical in preventing severe outcomes and improving the quality of life for patients. This project focuses on developing a robust heart disease prediction system using machine learning techniques. By analyzing a comprehensive dataset consisting of various patient attributes such as age, sex, blood pressure, cholesterol levels, and other medical parameters, the system aims to predict the likelihood of a patient having heart disease. The project (...) employs various machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Random Forests to classify the data and provide an accurate prediction. The system's performance is evaluated using metrics like accuracy, precision, recall, and F1-score, ensuring that it can offer reliable results in real-world applications. Furthermore, feature selection techniques are applied to identify the most significant factors contributing to heart disease, thus improving the model's interpretability. The proposed solution is intended to aid healthcare professionals by providing early alerts and recommendations, ultimately facilitating timely interventions. (shrink)

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, (...) we critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial (...) are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...) as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

Infinite machines (IMs) can do supertasks. A supertask is an infinite series of operations done in some finite time. Whether or not our universe contains any IMs, they are worthy of study as upper bounds on finite machines. We introduce IMs and describe some of their physical and psychological aspects. An accelerating Turing machine (an ATM) is a Turing machine that performs every next operation twice as fast. It can carry out infinitely many operations in finite (...) class='Hi'>time. Many ATMs can be connected together to form networks of infinitely powerful agents. A network of ATMs can also be thought of as the control system for an infinitely complex robot. We describe a robot with a dense network of ATMs for its retinas, its brain, and its motor controllers. Such a robot can perform psychological supertasks - it can perceive infinitely detailed objects in all their detail; it can formulate infinite plans; it can make infinitely precise movements. An endless hierarchy of IMs might realize a deep notion of intelligent computing everywhere. (shrink)

Wine quality prediction is a significant task in the wine industry, as it helps producers and consumers determine the quality of a wine based on its chemical properties. Traditional methods of evaluating wine quality are subjective and time-consuming, relying on human tasters. However, with the advancement of machine learning (ML), it is now possible to predict wine quality in a more objective, scalable, and efficient manner. This paper explores various machine learning algorithms for predicting wine quality, evaluates their performance, (...) and demonstrates how these models can be applied to improve wine classification systems. (shrink)

Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions.

This thesis is an exploration of the chapters of Francesco Di Giorgio's Trattati di Architettura, Ingegneria e Arte Militare, that pertain to mechanical devices. While it is difficult to imagine actually constructing Di Giorgio's machines from the drawings and descriptions in his treatises, given their apparent inefficiencies and ambiguities, the Aristotelean science and philosophy referenced throughout the Trattati provides a basis for looking at them as demonstrations of concepts beyond their immediate applications for architecture and engineering. By considering these (...) devices in Di Giorgio's own terms, terms suggested by his own experiences, as well as his writings and paintings, strong associations can be made to the science, philosophy and the theology of his time. (shrink)

Energy consumption prediction plays a critical role in optimizing energy usage, reducing waste, and ensuring the sustainability of power grids. With the growing use of smart meters, sensors, and IoT devices, there is a wealth of real-time data that can be leveraged to predict energy usage patterns. This paper explores the application of machine learning (ML) algorithms in predicting energy consumption, focusing on both residential and industrial settings. By utilizing supervised and unsupervised learning techniques, we demonstrate how ML can (...) provide accurate energy consumption forecasts and improve decision-making for energy management systems. (shrink)

In early modern times it was not uncommon for thinkers to tease out from the nature of God various doctrines of substantial physical and metaphysical import. This approach was particularly fruitful in the so-called beast-machine controversy, which erupted following Descartes’ claim that animals are automata, that is, pure machines, without a spiritual, incorporeal soul. Over the course of this controversy, thinkers on both sides attempted to draw out important truths about the status of animals simply from the notion or (...) attributes of God. Automatists – led by Nicolas Malebranche and Antoine Dilly – developed six such arguments, appealing to divine justice, providence, economy, glory (twice) and wisdom, while opponents to animal automatism developed two arguments, appealing to divine wisdom and goodness. In this article I shall examine the substance of all eight of these arguments, along with their origins, patronage, and variations, and the objections they elicited from opponents, with the aim of determining their suitability for use in contemporary debates about animal sentience and consciousness, and hence their relevance for contemporary philosophers. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

The exponential growth of high-frequency real-time financial transactions necessitates scalable machine learning infrastructures capable of processing and forecasting data in real time. This paper proposes a comprehensive design and implementation strategy for such infrastructures using distributed computing frameworks like Apache Spark and cloud services such as Amazon Web Services (AWS). Emphasizing technical specifics, the paper delves into architectural designs, implementation strategies, and optimization techniques that address critical challenges in data ingestion, real-time processing, model training, and deployment. A (...) proof-of-concept implementation demonstrates the feasibility of the proposed architecture on a small scale, highlighting its potential benefits. The findings suggest that implementing a scalable distributed machine learning infrastructure can enhance computational efficiency and significantly improve the accuracy and timeliness of financial forecasts. Future work will involve deploying the proposed architecture in large-scale industry settings to validate its effectiveness in real-world scenarios. (shrink)

{June 2018 UPDATE: This work has been greatly surpassed by "Utilizing Future-Viewing Instruments" which will appear in the July 2018 issue of Progress in Physics. It can now be downloaded in PDF form from their website.} -/- The introduction of new scientific instruments has always played a vital role in the advancement of science and society. All scientific instruments to date have only been able to gain information pertaining to events of our immediate and distant past. Concerning our attempts to (...) gain information about future events, thus far our sciences have been limited to prediction. However, what if we could develop instruments that would enable us, in a wide range of circumstances, to achieve direct empirical access to future states of our world? It is clear that any kind of future-viewing machine would have enormous impacts upon science and society, but what kinds of impacts? Questions as to whether such machines are physically possible and might ever be feasible from an engineering perspective will not be addressed; this work is devoted to exploring the logic of such machines and deducing some of the possibilities of their utilization. (shrink)

With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike traditional methods (...) that rely heavily on predefined indicators of compromise (IoCs), this system utilizes anomaly detection techniques, allowing it to identify new and emerging threats. As cyberattacks evolve, organizations must adopt adaptive methods to secure their networks. This system achieves high accuracy in classifying network traffic, with real-time alerts that provide early warnings of suspicious activities. It also includes intuitive visualizations, helping network administrators gain insights into the nature and scope of attacks. With the rise of increasingly complex and frequent cyberattacks, this NIDS offers a robust solution for enhancing network security and response capabilities. (shrink)

Early modern automata, understood as efforts to ‘model’ life, to grasp its singular properties and/or to unveil and demystify its seeming inaccessibility and mystery, are not just fascinating liminal, boundary, hybrid, crossover or go-between objects, while they are all of those of course. They also pose a direct challenge to some of our common conceptions about mechanism and embodiment. They challenge the simplicity of the distinction between a purported ‘mechanistic’ worldpicture, its ontology and its goals, and on the other hand (...) an attempt to understand ourselves and animals more broadly as flesh-and-blood, affective entities (that is, not just breathing and perspiring, but also desiring and ‘sanguine’ machines, as La Mettrie might have put it). In what follows I reflect on the complexity of early modern mechanism faced with the (living) body, and its mirror image, contemporary theories of embodiment. At times, embodiment theory seems to be governed by a fascination with what the Artificial Life researcher Ezequiel Di Paolo has called ‘biochauvinism’ (Di Paolo, “Extended Life”): an unquestioned belief that ‘living bodies are special’. Yet how does the theorist define this special status? The question is apparently a simple one, or at least promptly yields an aporia which appears simple: to borrow a provocative phrase from Terry Eagleton, embodiment theory is obsessed by the body but terrified of biology. Yet at the same time, at least since Hubert Dreyfus and Andy Clark’s groundbreaking works, embodiment has been a legit part of cognitive science, yielding the even more recently emerged field of ‘embodied cognition’ (see the work of Larry Shapiro), which seeks to depart from traditional cognitive science, especially the latter’s understanding of cognition as computational, in order to instead underscore “the significance of an organism’s body in how and what the organism thinks,” in Shapiro’s words. (shrink)

The advance of mechanism in science and philosophy in the 17th century created a great interest to machines or automata. Leibniz was no exception - in an early memoir Drôle de pensée he wrote admiringly about a machine that could walk on water, exhibited in Paris. The idea of automatic processing in general had a large role in his thought, as can be seen, for example, in his invention of the binary code and the so-called Calculemus!-model for solving controversies. (...) In metaphysics, the idea of an automata was expressed most clearly in the 1695 article New System of the Nature of Substances and their Communication, and the ensuing correspondence with, among others, Foucher, Bayle, Lamy, Jaquelot and Masham. In the article Leibniz discusses the soul as a spiritual machine in the context of pre-established harmony, arguing that God can "give to a substance at the outset a nature or internal force which could produce in it an orderly way (as in spiritual or formal automaton; but a free one, in the case of a substance which is endowed with a share of reason) everything that is going to happen to it, that is to say, all the appearances or expressions it is going to have, and all without the help of any created thing." The basic idea of Leibniz's spiritual machine is that the soul or entelechy is an autonomous and spontaneous unity, consisting of internal active force and producing its own perceptions (both confused and distinct). It is a self-moving machine, driven by its perceptions and appetites, but it follows (without being conscious of it) a lawful series or programme created by God. The entelechy remains the same despite undergoing an infinite number of changes. To Foucher he explained that each state of the individual substance is a consequence of its preceding one, "as if there were only God and the substance in the world". Despite this, the spiritual machine is related not only to God, but through the pre-established harmony to bodies or natural machines. However, as it is not dependent on them, the spiritual machine is superiot to natural machines. In this paper I concentrate on three moral themes related to the spiritual machine: moral deliberation, moral identity and the goal of moral action. All these themes are more or less implicit in the New System, but are discussed in more detail in the ensuing correspondence after the publication of the article. Finally, the themes are given an extended discussion especially in the second book of New Essays on Human Understanding (1704, published 1765), written partly at the same time as some of the correspondence and unpublished related material. I will first give an overview of Leibniz's position concerning the spiritual machine or automaton in the New System and then go on to explore the three moral themes one by one, as they feature in the subsequent correspondence and in the New Essays. I will show that the three topics are essential to the function of the spiritual machine – to Sophie Charlotte Leibniz wrote: "...let us say that everything in bodies happens mechanically, or in accordance with laws of motion, and that everything in the soul happens morally, or in accordance with perceived good or evil." There are many texts that anticipate the views in the New System (notably the unpublished memoir De Affectibus of 1679) and Leibniz continued to discuss its themes later on (in addition to correpondence, for example, in Theodicy of 1710 and Monadology and Principles of Nature and Grace of 1714). Here I focus on the short-term development of Leibniz's views on the topic (around 1695-1705), but I argue that his basic view of the spiritual machine did not change much after finishing the dialogue with Locke in 1704. (shrink)

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction (...) of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

David Estlund has recently asked: how can structural injustice warrant resentment and indignation, given that it cannot fully be traced to culpable conduct? This article answers Estlund’s question. I propose that a social structure is an object that persists through time and is materially constituted by humans in relation. I use accounts of the point of blame to vindicate attitudes of resentment and indignation that target social structures themselves, without necessarily targeting their human constituents.

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random Forest, XGBoost, (...) Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

Smart Farming Technologies raise ethical issues associated with the increased corporatization and industrialization of the agricultural sector. We explore the concept of biomimicry to conceptualize smart farming technologies as ecological innovations which are embedded in and in accordance with the natural environment. Such a biomimetic approach of smart farming technologies takes advantage of its potential to mitigate climate change, while at the same time avoiding the ethical issues related to the industrialization of the agricultural sector. We explore six principles (...) of a natural concept of biomimicry and apply these principles in the context of smart farming technologies. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)