The core objective of this project is to recognize and reconstruct distorted facial images, particularly in the context of accidents. This involves using deep learning techniques to analyze the features of a distorted face and regenerate it into a recognizable form. Deep learning models are wellsuited for this task due to their ability to learn complex patterns and representations from data the input data consists of distorted facial images, typically obtained from MRI scans of accident victims. These images may contain (...) various types of distortions such as swelling, bruising, or other injuries that affect facial appearance. (shrink)

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions in hiring, (...) skill development, performance management, and retention. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. Future work (...) will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...) (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable data communication. Future (...) work will explore the integration of machine learning techniques for adaptive scheduling and the application of the proposed method to heterogeneous WSNs. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, and simulated (...) annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data.

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate (...) the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. Testing dataset for anomaly detection model classified as attack or normal. Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall and confusion matrix. (shrink)

Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection (...) system's decision-making process. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the GSM network system. The system uses various sensors to detect fire, smoke, and gas, then transmits the message using GSM module. After the message, send by the module the help arrives in 15 minutes. The (...) system is associated with water pump and a sprinkler. The Water system is associated with a water tank and a sprinkler. The water tank uses a Ultrasonic sensor to monitor the level of water level in the tank for refilling the water tank. Once fire is detected, it automatically sends the emergency message and sprinkles the water. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

The paper also discusses the technical architecture, including the network protocols, data management strategies, and user interface design considerations necessary to implement such a system. Additionally, it addresses the challenges related to data security, privacy, and system interoperability. Finally, the paper outlines potential future enhancements, such as the incorporation of AI-driven predictive analytics and advanced AR features, to further elevate the smart home experience.

The World Health Organization has declared the outbreak of the novel coronavirus, Covid-19 as pandemic across the world. With its alarming surge of affected cases throughout the world, lockdown, and awareness (social distancing, use of masks etc.) among people are found to be the only means for restricting the community transmission. In a densely populated country like India, it is very difficult to prevent the community transmission even during lockdown without social awareness and precautionary measures taken by the people. Recently, (...) several containment zones had been identified throughout the country and divided into red, orange and green zones, respectively. The red zones indicate the infection hotspots, orange zones denote some infection and green zones indicate an area with no infection. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemas are existent today (eg., in-person and phone interviews, paper forms, email and web based questionnaires and smartphone apps), they often require active user participation and might miss certain COVID-19 transmission. The Android application updates the locations of the areas in a Google map which are identified to be the containment zones. The application also notifies the users if they have entered a containment zone and to achieve all these functionalities, many tools, and APIs from Google like Firebase and Geofencing API are used in this application. Therefore, this application can be used as a tool for creating further social awareness about the arising need of precautionary measures to be taken by the people of India. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by modern (...) applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset (...) has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. (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. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

The rapid adoption of telemedicine has transformed healthcare delivery, especially during the COVID-19 pandemic. This digital shift has enabled medical professionals to offer consultations and manage patients remotely, ensuring continuity of care while reducing exposure risks. However, the integration of telemedicine has presented both opportunities and challenges for doctors, particularly in terms of their work-life balance. This paper explores the digital adaptation of doctors in public and private hospitals concerning telemedicine practices and its impact on their work-life harmony. The study (...) highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server (...) holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and confidentiality of (...) the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. (shrink)

There are at least three vaguely atomistic principles that have come up in the literature, two explicitly and one implicitly. First, standard atomism is the claim that everything is composed of atoms, and is very often how atomism is characterized in the literature. Second, superatomism is the claim that parthood is well-founded, which implies that every proper parthood chain terminates, and has been discussed as a stronger alternative to standard atomism. Third, there is a principle that lies between these two (...) theses in terms of its relative strength: strong atomism, the claim that every maximal proper parthood chain terminates. Although strong atomism is equivalent to superatomism in classical extensional mereology, it is strictly weaker than it in strictly weaker systems in which parthood is a partial order. And it is strictly stronger than standard atomism in classical extensional mereology and, given the axiom of choice, in such strictly weaker systems as well. Though strong atomism has not, to my knowledge, been explicitly identified, Shiver appears to have it in mind, though it is unclear whether he recognizes that it is not equivalent to standard atomism in each of the mereologies he considers. I prove these logical relationships which hold amongst these three atomistic principles, and argue that, whether one adopts classical extensional mereology or a system strictly weaker than it in which parthood is a partial order, standard atomism is a more defensible addition to one’s mereology than either of the other two principles, and it should be regarded as the best formulation of the atomistic thesis. (shrink)

Resumo: Neste texto, introduzo algumas questões importantes que fazem parte do debate sobre infiltração pragmática. Além de apresentar e problematizar as principais alegações oferecidas pelos proponentes da infiltração pragmática eu irei contrasta-la com algumas teses mais tradicionais, como a Tese Tradicional sobre o Conhecimento e o Contextualismo Epistêmico. Por fim, apresento uma crítica aos proponentes da infiltração pragmática que parece ainda não ter sido tratada. Concluo que embora controversa, tal tese está longe de ser refutada completamente. Palavras-chave: Conhecimento; Contexto; Justificação; (...) Padrões; Infiltração prática. -/- Abstract: In this paper I introduce some central aspects concerning the debate on pragmatic encroachment. After introducing and discussing the main arguments offered by the pragmatic encroachers advocates I will contrast it with more traditional theses such as the Traditional Analysis of Knowledge and Epistemic Contextualism. Finally, I present an objection to the pragmatic encroachment approach that haven't been treated yet. I conclude that despite its difficulties, the pragmatic encroachment view is far from being refuted. Keywords: Knowledge; Context; Justification; Standards; Pragmatic encroachment. (shrink)

A number of philosophers think that grounding is, in some sense, well-founded. This thesis, however, is not always articulated precisely, nor is there a consensus in the literature as to how it should be characterized. In what follows, I consider several principles that one might have in mind when asserting that grounding is well-founded, and I argue that one of these principles, which I call ‘full foundations’, best captures the relevant claim. My argument is by the process of elimination. For (...) each of the inadequate principles, I illustrate its inadequacy by showing either that it excludes cases that should not be ruled out by a well-foundedness axiom for grounding, or that it admits cases that should be ruled out. (shrink)

In recent years, the advent of Artificial Intelligence (AI) has profoundly reshaped the way we access and utilize humanity's vast reservoir of knowledge. Equipped with this powerful tool, I have undertaken a thorough exploration of the Human Mind, motivated by a growing concern: the evident decline in students' enthusiasm for learning. More specifically, as a professor and researcher, my need for understanding has driven me to explore in depth what defines us as Human beings and what sets us apart from (...) other species. This investigative journey prompted me to reevaluate the evolutionary process, revisit Darwin's theories, and contemplate fundamental questions regarding the nature of time and reality. These reflections ultimately culminated in the formulation of a concept we call the ``Total Universe", a realm in which all possible events and interactions coexist. Building on this concept, we formulated a Theory of Consciousness rooted in the exploration of this Universe through our minds. This culminated in the emergence of the ``World of the Mind", which represents Human reality within the Total Universe, the fusion of all existing Human realities. Importantly, this theory not only integrates various theories of consciousness but also provides a clear framework for understanding what it might mean for Artificial Intelligence to attain consciousness. Hence, I invite you to embark on this journey of reflection, guided by this collection of written codes that encapsulate the discoveries made within the Total Universe through my mind, my reality. My perspective may resonate with yours, or it may not, and both are entirely valid. After all, each individual inhabits their own unique reality. (shrink)

This essay is about the relationship between the notions of practical reason, the will, and choice in Kant’s practical philosophy. Although Kant explicitly identifies practical reason and the will, many interpreters argue that he cannot really mean it on the grounds that unless they are distinct, irrational and, especially, immoral action is impossible. Other readers affirm his identification but distinguish the will from choice on the same basis. We argue that proper attention to Kant’s conception of practical reason as a (...) capacity reveals that these distinctions are neither textually grounded nor philosophically necessary. His moral psychology concerns a single capacity, practical reason, which is the will, and whose actualities in this or that individual fall under the title of choice. Practical reason is the will and choice because it is the rational faculty of desire: a rational being’s capacity to be, by means of her representations, the cause of the actuality of their objects. This, we argue, is entailed by his conception of rational action: action not just in accordance with, but in and through the representation of, principles. The possibility of irrational action is explained not by a distinction between capacities but by the finitude, and thereby the fallibility, of human reason. (shrink)

Can the issue of how important it is whether or not there is a God be decided prior to deciding whether or not there is a God? In this paper, I explore some difficulties that stand in the way of answering this question in the affirmative and some of the implications of these difficulties for that part of the Philosophy of Religion which concerns itself with assessing arguments for and against the existence of God, the implications for how its importance (...) may best be defended within secular academe. (shrink)

Sensation should be understood globally: some infant behaviors do not make sense on the model of separate senses; neonates of all species lack time to learn about the world by triangulating among different senses. Considerations of natural selection favor a global understanding; and the global interpretation is not as opposed to traditional work on sensation as might seem.

Many pantheists have claimed that their view of the divine is motivated by a kind of spiritual experience. In this paper, I articulate a novel argument, inspired by recent work on moral exemplarism, that gives voice to this kind of motivation for pantheism. The argument is based on two claims about the emotion of awe, each of which is defended primarily via critical engagement with empirical research on the emotion. I also illustrate how this pathway to pantheism offers pantheists distinctive (...) resources for responding to persistent objections to their view, and how it might lead to more exotic views incorporating pantheistic elements. (shrink)

It has been widely argued that words are analogous to species such that words, like species, are natural kinds. In this paper, I consider the metaphysics of word-kinds. After arguing against an essentialist approach, I argue that word-kinds are homeostatic property clusters, in line with the dominant approach to other biological and psychological kinds.

In this work, we attempt to define a notion of compositeness compatible with Quantum Field Theory. Considering the analytic properties of the S-matrix, we conclude that there is no satisfactory definition of compositeness compatible with Quantum Field Theory. Without this notion, one must claim that all bound states are equally fundamental, that is, one cannot rigorously claim that everyday objects are made of atoms or that atoms are made of protons and neutrons. I then show how an approximate notion of (...) compositeness may be recovered in the regime where the mass of a bound state is close to a multi-particle threshold. -/- Finally, we see that rejecting compositeness solves several of the "problems of everyday objects" encountered in an undergraduate metaphysics course. (shrink)

One of the main contentions of the framework for Responsible Innovation (RI) is that social and ethical aspects have to be addressed by deliberative engagement with stakeholders and the wider public throughout the innovation process. The aim of this article is to reflect on the question to what extent is deliberative engagement suitable for conducting RI in business. We discuss several tensions that arise when this framework is applied in the business context. Further, we analyse the place of deliberative engagement (...) in several theories of business ethics. We conclude that there remains a tension between the ideal of RI and the way in which the competitive market operates. Hence, RI scholars should reflect more critically on changes that are required in the market in order to make RI possible, modify the ideal of deliberative engagement for RI in business, or attempt to strike a balance between these two responses. (shrink)

Can an ugly experiment be a good experiment? Philosophers have identified many beautiful experiments and explored ways in which their beauty might be connected to their epistemic value. In contrast, the present chapter seeks out (and celebrates) ugly experiments. Among the ugliest are those being designed by AI algorithms. Interestingly, in the contexts where such experiments tend to be deployed, low aesthetic value correlates with high epistemic value. In other words, ugly experiments can be good. Given this, we should conclude (...) that beauty is not generally necessary or sufficient for epistemic value, and increasing beauty will not generally tend to increase epistemic value. (shrink)

For two ideally rational agents, does learning a finite amount of shared evidence necessitate agreement? No. But does it at least guard against belief polarization, the case in which their opinions get further apart? No. OK, but are rational agents guaranteed to avoid polarization if they have access to an infinite, increasing stream of shared evidence? No.

In this article, I suggest that combining resources from philosophy and psychology can yield useful tools for philosophical counselling. More specifically, I argue for three theses: a) Iris Murdoch’s notion of just attention and Marshall Rosenberg’s method of non-violent communication are interestingly compatible; b) engaging in non-violent communication serves to support one’s endeavors to acquire the kind of clear vision Murdoch thinks doing well by others requires; and c) non-violent just communication would be beneficial to both counsellors and counselees and (...) thus a useful resource for philosophical counsellors. (shrink)

If what we believe can directly modify our (visual) experience, our experience is doxastically variable. If so, the following seems possible: our false and irrational background beliefs can modify our experience such that in it, things look distorted, or that it conforms with and appears to confirm the false and irrational beliefs that helped bring it about in the first place. If experience is doxastically variable, it seems, its epistemic function can be undermined. However, in this dissertation, I argue that (...) we can devise accounts of (visual) experience that meet two requirements: they are fully compatible with all kinds of doxastic variation and on them, even doxastically variable experience serves to rationally constrain our beliefs. I begin with a novel interpretation of Hanson’s account of theory-laden observation—a valiant, yet ultimately unsuccessful attempt to meet both these requirements. Next, I analyze and reject various contemporary relationalist accounts of experience and the most sophisticated recent representationalist attempt to accommodate phenomena of doxastic variation: Siegel’s (Rich) Content View. Then, based on the lessons learned and drawing on Hanson’s and Gupta’s work, I show what shape a successful account may take. Ultimately, I argue for the following theses: 1) Neither of the two dominant accounts of experience—relationalism and standard representationalism—currently succeeds in satisfactorily meeting both requirements. 2) To arrive at accounts that do meet them, we should drop both the restrictive relationalist conception of experience as a relation to mind-independent items and the standard representationalist conception of experience as justifying beliefs. 3) We make progress by adopting both the general conception of experience as making rational transitions to beliefs, judgments, and actions and a (slightly) modified version of Gupta’s presentationalist account of experiential phenomenology. Finally, 4) the possibility of devising successful accounts is independent of a major issue dividing relationalists and representationalists: whether experience has content. In the final chapters, I address various follow-up questions concerning the nature of views, conceptual capacities, conceptual content, and linkages between a subject’s experience and her responses. In concluding, I show that the account of experience I recommend is widely applicable in philosophy and beyond. (shrink)

Kit Fine (2000) breaks with tradition, arguing that, pace Russell (e.g., 1903: 228), relations have neither directions nor converses. He considers two ways to conceive of these new "neutral" relations, positionalism and anti-positionalism, and argues that the latter should be preferred to the former. Cody Gilmore (2013) argues for a generalization of positionalism, slot theory, the view that a property or relation is n-adic if and only if there are exactly n slots in it, and (very roughly) that each slot (...) may be occupied by at most one entity. Slot theory (and with it, positionalism) bears the full brunt of Fine's (2000) symmetric completions and conflicting adicities problems. I fully develop an alternative, plural slot theory (or pocket theory), which avoids these problems, key elements of which are first considered by Yi (1999: 168 ff.). Like the slot theorist, the pocket theorist posits entities (pockets) in properties and relations that can be occupied. But unlike the slot theorist, the pocket theorist denies that at most one entity can occupy any one of them. As a result, she must also deny that the adicity of a property or relation is equal to the number of occupiable entities in it. By abandoning these theses, however, the pocket theorist is able to avoid Fine's problems, resulting in a stronger theory about the internal structure of properties and relations. Pocket theory also avoids a serious drawback of anti-positionalism. (shrink)

Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be nontrivially Bayes-compatible. We show by contrast that geometric pooling can be nontrivially Bayes-compatible. Indeed, we show that, under certain assumptions, geometric and (...) Bayes-compatible pooling are equivalent. Granting supra-Bayesianism its usual normative status, one upshot of our study is thus that, in a certain class of epistemic contexts, geometric pooling enjoys a normative advantage over linear pooling as a social learning mechanism. We discuss the philosophical ramifications of this advantage, which we show to be robust to variations in our statement of the Bayes-compatibility problem. (shrink)

Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes (...) is then divided into two kinds: one that is unconscious, uncontrolled, and effortless, and another that is conscious, controlled, and effortful. This paper outlines the different epistemological strengths and weaknesses of the two kinds of imaginative process, and argues that a dual process model of imagination helpfully resolves or clarifies issues in the epistemology of imagination and the closely related epistemology of thought experiments. (shrink)

Speaks defends the view that propositions are properties: for example, the proposition that grass is green is the property being such that grass is green. We argue that there is no reason to prefer Speaks's theory to analogous but competing theories that identify propositions with, say, 2-adic relations. This style of argument has recently been deployed by many, including Moore and King, against the view that propositions are n-tuples, and by Caplan and Tillman against King's view that propositions are facts (...) of a special sort. We offer our argument as an objection to the view that propositions are unsaturated relations. (shrink)

Synthetic biologists aim to generate biological organisms according to rational design principles. Their work may have many beneficial applications, but it also raises potentially serious ethical concerns. In this article, we consider what attention the discipline demands from bioethicists. We argue that the most important issue for ethicists to examine is the risk that knowledge from synthetic biology will be misused, for example, in biological terrorism or warfare. To adequately address this concern, bioethics will need to broaden its scope, contemplating (...) not just the means by which scientific knowledge is produced, but also what kinds of knowledge should be sought and disseminated. (shrink)

Summary: Throughout the history of science, indeed throughout the history of knowledge, unification has been touted as a central aim of intellectual inquiry. We’ve always wanted to discover not only numerous bare facts about the universe, but to show how such facts are linked and interrelated. Large amounts of time and effort have been spent trying to show diverse arrays of things can be seen as different manifestations of some common underlying entities or properties. Thales is said to have originated (...) philosophy and science with his declaration that everything was, at base, a form of water. Plato’s theory of the forms was thought to be a magnificent accomplishment because it gave a unified solution to the separate problems of the relation between knowledge and belief, the grounding of objective values, and how continuity is possible amid change. Pasteur made numerous medical advancements possible by demonstrating the interconnection between microorganisms and human disease symptoms. Many technological advances were aided by Maxwell’s showing that light is a kind of electromagnetic radiation. The attempt to unify the various known forces is often referred to as “The Holy Grail” of physics. Some philosophers have even suggested that providing explanations is itself just a sort of unifying of our knowledge. But while unification (like simplicity) has often been hailed as a tremendous virtue in science, the meaning of the term is not altogether clear. Scientists often don’t specify what, precisely, they mean by unification. And in cases where what they mean is clear, different thinkers plainly mean different things by the term. What are the various senses of unification, and why has unification been such an important aim in the history of inquiry? (shrink)

In this journal Steve Maitzen has recently advanced an argument for atheism premised on theodical individualism, the thesis that God would not permit people to suffer evils that were underserved, involuntary, and gratuitous for them. In this paper I advance reasons to think this premise mistaken.