In this paper, we introduce and defend the recurrent model for understanding bodily spatial phenomenology. While Longo, Azañón and Haggard (2010) propose a bottom-up model, Bermúdez (2017) emphasizes the top-down aspect of the information processing loop. We argue that both are only half of the story. Section 1 intro- duces what the issues are. Section 2 starts by explaining why the top- down, descending direction is necessary with the illustration from the ‘body-based tactile rescaling’ paradigm (de Vignemont, (...) Ehrsson and Haggard, 2005). It then argues that the bottom-up, ascending direction is also necessary, and substantiates this view with recent research on skin space and tactile field (Haggard et al., 2017). Section 3 discusses the model’s application to body ownership and bodily self-representation. Implications also extend to topics such as sense modality individuation (Macpherson, 2011), the constancy- based view of perception (Burge, 2010), and the perception/cognition divide (Firestone and Scholl, 2016). (shrink)

The paper analyzes dynamic epistemic logic from a topological perspective. The main contribution consists of a framework in which dynamic epistemic logic satisfies the requirements for being a topological dynamical system thus interfacing discrete dynamic logics with continuous mappings of dynamical systems. The setting is based on a notion of logical convergence, demonstratively equivalent with convergence in Stone topology. Presented is a flexible, parametrized family of metrics inducing the latter, used as an analytical aid. We show maps induced by action (...) model transformations continuous with respect to the Stone topology and present results on the recurrent behavior of said maps. (shrink)

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

[This is an edited version of a keynote talk at the conference on Neural Information Processing Systems (NeurIPS) on November 28, 2022, with some minor additions and subtractions.] -/- There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their (...) lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future. (shrink)

This essay distinguishes some significant commonalities and differences between the film-philosophies of Chilean filmmaker Raúl Ruiz (especially in his book Poetics of Cinema) and U.S. philosopher Stanley Cavell. I argue that despite shared senses of the poetics of the film image and certain shared philosophical references, Ruiz and Cavell differed over their conceptions of the model spectator and their relations to autonomous films and worlds from which spectators are excluded (on Cavell's picture) versus fragments out of which the spectator (...) might create their own films and worlds in which a spectator might become included (on Ruiz's picture). I then argue that a striking reconciliation between these perspectives takes place via each's conception of television, since it was precisely the features of the medium and its heteronomous parts that Cavell found bemusing from the perspective of his view of film (and its supposedly autonomous works) that allowed for Ruiz's natural career-long relationship with television and heteronomy, especially in Latin American telenovelas. I thus approach Ruiz's relation to telenovelas via Cavell's understanding of the possibilities of soap operas to explore arguments between different temporalities: "dialectical," narrative time and "undialectical" recurrences of needs and drives. I further explore this possibility with a reading of Ruiz's neglected, late-career Chilean TV series Litoral (2008), focused on the construction of stories by sailors aboard a ghost ship. I argue that the series' treatment of these different temporalities reaches its culmination in the poignant image of a sailor-storyteller stuck within a story of his own creation: a stark rendering of his taking a sideways-on view of the role of his own fantasies in its construction. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact (...) to raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research (...) employs Artificial Neural Networks, Deep Neural Networks, and Gated Recurrent Units, combining high-resolution 3D urban models with environmental data to analyze UHI trends. The results indicate strong associations between urban form, weather patterns, and UHI intensity, highlighting the need for customized urban planning and policy measures to reduce UHI impacts and foster sustainable urban settings. This research enhances understanding of UHI dynamics and serves as a valuable tool for urban planners and policymakers to address the challenges of climate change, urbanization, and air pollution, ultimately aiding in the improvement of health outcomes and building energy consumption. Moreover, the methodology effectively demonstrates the ability of the GRU to link its scores with UHI projections, offering crucial insights into potential health impacts. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like (...) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

Two models of human perfection proposed by Nietzsche and the Buddha are investigated. Both the overman and the arahant need practice and individual effort as key to their realization, and they share roughly the same conception of the self as a construction. However, there are also a number of salient differences. Though realizing it to be constructed, the overman does proclaim himself through his assertion of the will to power. The realization of the true nature of the self does not (...) lead the overman to seek the way to be released from saṃsara as does the arahant. On the contrary, he rejoices in the eternally recurring situation. The arahant, however, has totally relinquished any attachment to the self, constructed or otherwise. The arahant does not care about the Eternal Recurrence, as he only focuses on the present moment. Finally, they are both beyond good and evil, but in a substantively different way. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further (...) improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The results show that the hybrid CNN-LSTM model outperforms the individual models in terms of prediction accuracy and robustness, suggesting that combining convolutional layers with recurrent units is beneficial for capturing both spatial and temporal patterns in air quality data. This study demonstrates the potential of deep learning methods to offer real-time, accurate air quality forecasting systems, which can aid policymakers and urban planners in managing air pollution more effectively.

Speech Emotion Recognition (SER) is an essential component in human-computer interaction, enabling systems to understand and respond to human emotions. Traditional emotion recognition methods often rely on handcrafted features, which can be limited in capturing the full complexity of emotional cues. In contrast, deep learning approaches, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, offer more robust solutions by automatically learning hierarchical features from raw audio data. This paper reviews recent advancements in (...) deep learning-based speech emotion recognition, discusses the various architectures used, and evaluates the challenges in real-world applications. We focus on the application of deep learning models to enhance the accuracy and robustness of SER, particularly in noisy environments. The study also discusses future directions for research, including multimodal emotion recognition and transfer learning to address challenges such as small datasets and cross-domain applications. (shrink)

. Speech Emotion Recognition (SER) is an interdisciplinary field that leverages signal processing and machine learning techniques to identify and classify emotions conveyed through speech. In recent years, SER has gained significant attention due to its potential applications in human-computer interaction, healthcare, education, and customer service. Emotions such as happiness, anger, sadness, fear, surprise, and disgust can be inferred from various acoustic features including pitch, intensity, speech rate, and spectral characteristics. However, accurately recognizing emotions from speech is challenging due to (...) factors such as speaker variability, cultural differences, background noise, and the subtleties of emotional expression. This paper explores the state-of-the-art methodologies for speech emotion recognition, with an emphasis on deep learning approaches, feature extraction techniques, and the use of large-scale emotion-labeled datasets. We review traditional approaches, such as hidden Markov models and support vector machines, and compare them with modern advancements in neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Additionally, we discuss the challenges in the field, including emotion detection in spontaneous speech, the impact of cross-lingual and crosscultural recognition, and the limitations of current benchmarks. Finally, we provide an overview of real-world applications of SER systems, including their integration into virtual assistants, mental health diagnostics, and interactive entertainment. We conclude by highlighting emerging trends in multimodal emotion recognition and the potential for future research in improving the robustness and accuracy of SER systems in diverse environments. (shrink)

I here present some doubts about whether Mandik’s (2010) proposed intermediacy and recurrence constraints are necessary and sufficient for agentive experience. I also argue that in order to vindicate the conclusion that agentive experience is an exclusively perceptual phenomenon (Prinz, 2007), it is not enough to show that the predictions produced by forward models of planned motor actions are conveyed by mock sensory signals. Rather, it must also be shown that the outputs of “comparator” mechanisms that compare these predictions against (...) actual sensory feedback are also coded in a perceptual representational format. (shrink)

In the presently unfolding deepfake era, recurrent inflationary algorithmic superintelligence (ASI) achievement claims degenerated from being a mere reflection of an exaggerated but candid initial enthusiasm to becoming a convenient tool for misdirection facilitating epistemic perpetuum mobile (EPM) scams. This transdisciplinarily conceived paper compactly analyzes the underlying ASI definition avoidance problem which emerged from interactions between three major epistemic trends in the ASI debate: boomerism, doomerism and pragmatism. Via taking a fourth external perspective entertained by a fictive entity called (...) Cyogenes who volunteers as a philosopher and criticizes contemporary conventions (inspired by the ancient Cynicism lived by early Greek philosophers including Antisthenes and Diogenes of Sinope), the paper highlights key strengths but also particularly pitfalls within those three aforementioned epistemic trends all of which face the ASI definition avoidance problem – clearing the way for global EPM scams. After this philosophical analysis, a deflationary account of ASI (i.e. the epistemic perpetuum mobile) is discussed. Thereafter, practical cybersecurity-oriented threat models and defenses against EPM scams are formulated. In brief, fruitless EPM scams unnecessarily risk intensifying the near-term existential risk of epistemic self-sabotage and reinforcing totalitarian aspirations. (shrink)

Critics have often misunderstood the higher-order theory (HOT) of consciousness. Here we clarify its position on several issues, and distinguish it from other views such as the global The higher-order theory (HOT) of consciousness has often been misunderstood by critics. Here we clarify its position on several issues, and distinguish it from other views such as the global workspace theory (GWT) and early sensory models (e.g. first-order local recurrency theories). For example, HOT has been criticized for over-intellectualizing consciousness. We show (...) that while higher-order states are cognitively assembled, the requirements are actually considerably less than often presumed. In this sense HOT may be viewed as an intermediate position between GWT and early sensory views. Also, we clarify that most proponents of HOT do not stipulate consciousness as equivalent to metacognition or confidence. Further, compared to other existing theories, HOT can arguably account better for complex everyday experiences, such as of emotions and episodic memories. This makes HOT particularly useful as a framework for conceptualizing pathological mental states. (shrink)

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

Archaeological data are shadowy in a number of senses. Not only are they notoriously fragmentary but the conceptual and technical scaffolding on which archaeologists rely to constitute these data as evidence can be as constraining as it is enabling. A recurrent theme in internal archaeological debate is that reliance on sedimented layers of interpretative scaffolding carries the risk that “preunderstandings” configure what archaeologists recognize and record as primary data, and how they interpret it as evidence. The selective and destructive (...) nature of data capture in archeology further suggests that there may be little scope for putting “legacy” data to work in new ways. And yet archaeologists have been strikingly successful in mining old datasets for new insights. I situate these concerns in the broader context of debate about the epistemic standing of the historical sciences, and then consider three strategies by which archaeologists address the challenges posed by legacy data. The first two – secondary retrieval and recontextualization – are a matter of reconfiguring the scaffolding that underpins evidential reasoning. The third turns on redeploying old data in the context of computational models that support the experimental simulation of the cultural systems and contexts under study. (shrink)

In their landmark 2010 paper, “The weirdest people in the world?”, Henrich, Heine, and Norenzayan outlined a serious methodological problem for the psychological and behavioural sciences. Most of the studies produced in the field use people from Western, Educated, Industrialised, Rich and Democratic (WEIRD) societies, yet inferences are often drawn to the species as a whole. In drawing such inferences, researchers implicitly assume that either there is little variation across human populations, or that WEIRD populations are generally representative of the (...) species. Yet neither of these assumptions is justified. In many psychological and behavioural domains, cultural variation begets cognitive variation, and WEIRD samples are recurrently shown to be outliers. In the years since the article was published, attention has focused on the implications this has for research on extant human populations. Here we extend those implications to the study of ancient _H. sapiens_, their hominin forebears, and cousin lineages. We assess a range of characteristic arguments and key studies in the cognitive archaeology literature, identifying issues stemming from the problem of sample diversity. We then look at how worrying the problem is, and consider some conditions under which inferences to ancient populations via cognitive models might be provisionally justified. (shrink)

This article considers an interpretative model for the study of Heraclitus, which was first put forward by Alexander Mourelatos in 1973, and draws upon a related model put forward by Julius Moravcsik beginning in 1983. I further develop this combined model and provide a motivation for an interpretation of Heraclitus. This is also of interest for modern metaphysics due to the recurrence of structurally similar problems, including the ‘colour exclusion’ problem that was faced by Wittgenstein. Further, I (...) employ the model to shed new light on Heraclitus’ image of the river, while relating potential readings to various contemporary metaphysical views. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

Advocates of literary Darwinism, cognitive cultural studies, neuroaesthetics, digital humanities, and other such hybrid fields now seek explicitly to make the aims and methods of one or another humanities discipline approximate more closely the aims and methods of science, and at their most visionary, they urge as well the overall integration of the humanities and natural sciences. This essay indicates some major considerations—historical, conceptual, and pragmatic—that may be useful for assessing these efforts and predicting their future. Arguments promoting integration often (...) involve dubious teleological models of intellectual history and betray limited understandings of the distinctive epistemic orientations and cultural functions of the humanities vis-à-vis the sciences. Recurrent institutional difficulties encountered by scholars and/or scientists in hybrid fields reflect steep prestige differentials between the humanities and sciences, along with significant differences of training, experience, style, and temperament. Meanwhile, both the sciences and humanities are being shaken up by technological and related intellectual developments. Though worrisome, the new disciplinary configurations are thus likely to play out in surprising and, not inconceivably, positive ways. (shrink)

In the exploratory study reported here, we tested the efficacy of an intervention designed to train teenagers with Möbius syndrome (MS) to increase the use of alternative communication strategies (e.g., gestures) to compensate for their lack of facial expressivity. Specifically, we expected the intervention to increase the level of rapport experienced in social interactions by our participants. In addition, we aimed to identify the mechanisms responsible for any such increase in rapport. In the study, five teenagers with MS interacted with (...) three naïve participants without MS before the intervention, and with three different naïve participants without MS after the intervention. Rapport was assessed by self-report and by behavioral coders who rated videos of the interactions. Individual non-verbal behavior was assessed via behavioral coders, whereas verbal behavior was automatically extracted from the sound files. Alignment was assessed using cross recurrence quantification analysis and mixed-effects models. The results showed that observer-coded rapport was greater after the intervention, whereas self-reported rapport did not change significantly. Observer-coded gesture and expressivity increased in participants with and without MS, whereas overall linguistic alignment decreased. Fidgeting and repetitiveness of verbal behavior also decreased in both groups. In sum, the intervention may impact non-verbal and verbal behavior in participants with and without MS, increasing rapport as well as overall gesturing, while decreasing alignment. (shrink)

J. N. (Jitendra Nath) Mohanty (1928–2023). -/- Professor J. N. Mohanty has characterized his life and philosophy as being both “inside” and “outside” East and West, i.e., inside and outside traditions of India and those of the West, living in both India and United States: geographically, culturally, and philosophically; while also traveling the world: Melbourne to Moscow. Most of his academic time was spent teaching at the University of Oklahoma, The New School Graduate Faculty, and finally Temple University. Yet his (...) preeminent work in Husserlian phenomenology developed alongside his eminent work in Indian philosophy: describing his interests as “a fusion of disparate horizons.” -/- J. N. Mohanty was born September 26, 1928, in Cuttack (Odisha, East India). After graduating from high school, he went on to study both Indian and Western philosophy in Calcutta, earning bachelor’s and master’s degrees. There he read Whitehead and Kant’s First Critique; although he wanted to include mathematics in his curriculum, he was led instead to include Indian philosophy and Sanskrit. On the shelves of his teacher, Ras Vihar Das, he came upon a copy of the English translation (Ideas I, 1931) of Edmund Husserl’s classic Ideen I (1913), which presented Husserl’s ground-breaking conception of phenomenology. In 1952–1954 he left India for the first time, reaching Göttingen to study mathematics and philosophy, which earned him a doctorate in mathematics and “philosophy of mathematical sciences” (in his own words). In Göttingen, Mohanty found the powerful mathematical world that Husserl himself had earlier interacted with, where several Husserl students had formed the Göttingen school of phenomenology. During these years Mohanty studied primarily mathematics, alongside Kant and also Vedic Sanskrit. From his friend Günter Patzig, interpreter of Aristotle and Frege, Mohanty was drawn to Frege in relation to mathematical logic. He attended lectures of Heidegger, intrigued by his ontological thinking. Yet, despite the Husserlian legacy, Mohanty was completely self-taught in his studies of Husserl (as he has reported). With a doctorate in mathematics, and ideas from Kant and Frege in his philosophical background, Mohanty set about crafting his own conception of philosophy grounded in phenomenology, drawing on Husserl’s extensive work, critically sifting through Husserl’s texts and their emerging concepts of intentionality, meaning, subject, intersubjectivity, and world. In between he wrote his first book-length study: on phenomenological insights in Nicolai Hartmann and A. N. Whitehead (1958). -/- Over many decades Mohanty formulated and argued, in analytical detail, for a conception of phenomenology and its place in philosophy, later presented in a clear and concise book titled Transcendental Phenomenology: An Analytic Account (1989). Over these very decades the same scholar explored classical and recent Indian philosophy, thinking through kindred ideas of consciousness, self, and knowledge drawn from the Indian philosophical contexts. While writing on Nyāya theory of truth, he also pondered whether the world and finite individual are illusory or real, and whether Marx, Arendt, Gandhi (whom he heard speak in Calcutta), or Vinoba Bhave (with whom he marched across India for the land-grant movement) could best navigate post-Independent India’s social and svarāj or self-rule reforms. The two Mohantys, thinking through a vision of self and world, turned out to be “non-different” or “non-dual” as they each practiced critical phenomenology from both inside and outside the respective philosophical and cultural traditions. Numerous students, fellows, and colleagues or collaborators have benefited immensely from this infusion and unified approach to diversity in philosophical thought. In the 2000s, moving into retirement, Mohanty wrote two long books devoted to his understanding of Husserl and phenomenology and the calling of philosophy itself. This two-volume study shows Mohanty himself thinking through Husserl, critically, in The Philosophy of Edmund Husserl: A Historical Introduction (2008), and Edmund Husserl’s Freiburg Years: 1916–1938. As Mohanty worked on Husserl, he carefully indicated where he agreed and where he rejected or changed ideas, all part of his practice in phenomenology of “description and interpretation.” It was the same pattern he used in addressing the thought of Husserl vis-à-vis Kant or Frege or even Quine. -/- As Mohanty developed his understanding of phenomenology over the years, he wrote books on theory of meaning and the concept of intentionality, developing a model of ideal meaning and its foundation in intentionality, drawing on Husserl’s results. He followed these with the book Husserl and Frege (1982), linking the thought of those foundational figures for the “continental” and “analytic” traditions, respectively, in twentieth-century Western philosophy. Over his long career Mohanty addressed both traditions in his clear and accessible writing style. While developing his views on phenomenology, Mohanty regularly looked to “Husserl and his others,” evaluating views in Heidegger, Sartre, Merleau-Ponty, Ricoeur, and then Derrida and others in the wake of Husserl. With his background in Kant, Mohanty also looked toward Heidegger and Hegel in relation to Husserl’s later work in the Crisis (1935–38). Similarly, he looked to contemporary analytic philosophers, adjudicating his own, oft-wise Husserlian views in relation to Frege, Nagel, and others. Amid his active scholarly career, Mohanty co-founded the journal Husserl Studies, and was editorial advisor to Philosophy & Phenomenological Research, Philosophy East & West, Journal of Indian Philosophy, Sophia, among others. -/- Yet all this while Mohanty was also thinking and writing about Indian philosophy and its relation to phenomenology. In his own retrospective, Indian philosophy is “the permanent background” of his Husserlian thinking, while Kant is the recurrent Western background of his Husserlian phenomenology. Mohanty’s form of “transcendental phenomenology” evolved, in his own perspective, against the background of his studies of Navya-Nyāya on logic and Vedānta on consciousness, in Indian philosophy, and against the background of Kant’s First Critique on the transcendental, in Western philosophy. Accordingly, Mohanty’s study of logical form and of the intentionality of consciousness seeks a fusion of East and West in the conception of transcendental phenomenology. (Cf. Mohanty’s apt response to critics in The Empirical and the Transcendental (2000).) Even in the context of North American Husserl scholarship, Mohanty has exercised an earnest fusion of East and West. For the so-called East Coast and West Coast interpretations of Husserl’s crucial notion of noema both find a sympathetic spirit in J. N. Mohanty’s careful and nuanced interpretation of Husserlian transcendental phenomenology. With Mohanty the two faces of the noema are the logical (Fregean) and the phenomenal (Kantian), and these views of intentional structure join in consciousness—in a way resonant with Indian thought. -/- - David Woodruff Smith (UC Irvine) and Purushottama Bilimoria (SFSU, San Francisco; University of Melbourne) -/- For photo of Prof Mohanty visit APA online memorial_minutes2023 Courtesy of APA Memorial Minutes (& Proceedings) 2023 . (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)

As the prevalence of deepfake videos continues to escalate, there is an urgent need for robust and efficient detection methods to mitigate the potential consequences of misinformation and manipulation. This abstract explores the application of Long Short-Term Memory (LSTM) networks in the realm of deepfake video detection. LSTM, a type of recurrent neural network (RNN), has proven to be adept at capturing temporal dependencies in sequential data, making it a promising candidate for analysing the dynamic nature of videos. The (...) research delves into the intricacies of utilizing LSTM architectures for the detection of deepfake videos, emphasizing the significance of understanding temporal patterns inherent in manipulated content. The proposed methodology involves preprocessing of video data, including the creation of high-quality training datasets and the application of data augmentation techniques to enhance model generalization. The training process and optimization strategies specific to LSTM networks are explored to achieve optimal performance in deepfake detection. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the model’s effectiveness in distinguishing between genuine and manipulated content. The abstract also addresses challenges and limitations inherent in deepfake detection, including mitigating false positives and negatives, and discusses potential avenues for future research to enhance the robustness of LSTM-based detection systems. The findings of this research have implications for real-world applications, particularly in the context of social media platforms and video hosting services, where the integration of LSTM-based deepfake detection can contribute to a safer and more secure online environment. k. (shrink)

Advancing our understanding of one of the most influential 20th-century philosophers, Robert Vinten brings together an international line up of scholars to consider the relevance of Ludwig Wittgenstein's ideas to the cognitive science of religion. Wittgenstein's claims ranged from the rejection of the idea that psychology is a 'young science' in comparison to physics to challenges to scientistic and intellectualist accounts of religion in the work of past anthropologists. Chapters explore whether these remarks about psychology and religion undermine the frameworks (...) and practices of cognitive scientists of religion. Employing philosophical tools as well as drawing on case studies, contributions not only illuminate psychological experiments, anthropological observations and neurophysical research relevant to understanding religious phenomena, they allow cognitive scientists to either heed or clarify their position in relation to Wittgenstein's objections. By developing and responding to his criticisms, Wittgenstein and the Cognitive Science of Religion offers novel perspectives on his philosophy in relation to religion, human nature, and the mind. Contents: Introduction (Robert Vinten); Ch.1: 'Wittgenstein, Concepts, and Human Nature' (Roger Trigg); Ch.2: 'On Truth, Language, and Objectivity' (Florian Franken Figueiredo); Ch.3 'Pascal Boyer's Miscellany of Homunculi: A Wittgensteinian Critique of Religion Explained' (Robert Vinten); Ch.4 'The Brain Perceives/Infers' (Hans van Eyghen); Ch.5 'The Imaginary Inner Inside Cognitive Science of Religion' (Christopher Hoyt); Ch.6 'Cognitive Theories and Wittgenstein: Looking for Convergence not for Divergence' (Olympia Panagiotidou); Ch. 7 'Wittgenstein, Naturalism, and Interpreting Religious Phenomena' (Thomas Carroll); Ch.8 'Natural Thoughts and Unnatural Oughts: Lessing, Wittgenstein, and Contemporary CSR' (Guy Axtell); Ch.9 'Normative Cognition in Cognitive Science of Religion' (Mark Addis); Ch.10 'Brains as the Source of Being: Mind/Brain Focus and the Western Model of Mind in Dominant Cognitive Science Discourse' (Rita McNamara); Ch.11 'On Religious Practices as Multiscale Active Inference: Certainties Emerging From Recurrent Interactions Within and Across Individuals and Groups' (Inês Hipólito and Casper Hesp). (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

Following the inferences of my previous work "Monadic Conditionality", this work further investigates the nature of being-for-itself's transformations and what happens with any being-for-itself in between eternal returns, completing a quasi-principled view on immortality (suggested and started in my previous work). Through mathematical reasoning, this model infers that infinitesimal differences between successive event lines grow gradually across subspaces, until reaching the state of eternal return; the cycle repeats, resulting in each event line having its own eternal return, preserving monadic (...) homeostasis. Thus, "amor fati" is enriched by extending fixed eternal recurrences with all "inter-idem" variations, ultimately reaching a fertile ground for ataraxia. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...) a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and (...) future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

Propounded in relation to a peculiar mode in the view of an oscillating or cyclic universe, the concept of Return of Power, or of ontic recurrence as further increase in ontic Power signifies the determination of the existing entity according to its own selective recurrence as dialectically exceeding a previous status. Based thus upon the assumption that the actual ontological existence of the entity lies in its own potentiated recurrence (for it is maintained that only what is able to return (...) to itself as potentiated signifies actual ontological existence) and that an ontic eternal return can only occur in terms of further Power, this notion embodies the culminating phase within change as an ultimate overcoming of a previous ontic status or situation respecting the category Power as a primary criterion within a constantly increasing Real (as Power, precisely). An ontic selective ultimate event respecting becoming as a general increment of the Real, this concept exceeds both the ontic dispersion in nothingness of the linear time and the identical ontic repeatability of the Eternal Return of the Same, the latter insofar as asserting the perennial return of man's own status of powerlessness, and as rendering accordingly impossible any actual (dialectical) transition to a well-defined superior ontic phase. (shrink)

In the light of the results obtained during the last two decades in analysis of signals by time series, it has become evident that the tools of non linear dynamics have their elective role of application in biological, and, in particular, in neuro-physiological and psycho-physiological studies. The basic concept in non linear analysis of experimental time series is that one of recurrence whose conceptual counterpart is represented from variedness and variability that are the foundations of complexity in dynamic processes. Thus, (...) the recurrence plots and the Recurrence Quantification Analysis (RQA) are discussed. It is shown that RQA represents the most general and correct methodology in investigation of experimental time series. By it we arrive to inspect the inner structure of the time series connected to the signals under investigation. Linked to RQA we prospect also the method CZF, recently introduced by us. It is able to account for a true estimation of variability of signals in time as well as in frequency domain. And, consequently, it may be used in conjunction with classical Fourier analysis, accounting however that it is inappropriate in analysis of non linear and non stationary experimental time series. The use of CZF method in fractal analysis is also considered in addition to standard index as Hurst exponent. A large field of possible applications in neurological as well as in psycho-physiological studies is given. Also, there are given examples of other and (possibly linked) applications as example the analysis of beat-to-beat fluctuations of human heartbeat intervals that is sovereign in psycho-physiological studies. We give applications on some different planes to evidence the particular sensitivity of such methods. We reach the objective to show that the previously exposed methods are also able to predict in advance the advent of ventricular tachycardia and/or of ventricular fibrillation. The RQA analysis gives good results. The CZF method gives the most excellent results showing that it is able to give very significant indexes of prediction. We also apply such methods in investigation of state anxiety, and proposing in detail a quantum like model of such phenomenological status of the mind. (shrink)

We evaluate classical probability in relation to the random generation of a Shakespearean sonnet by a typing monkey and the random generation of universes in a World Ensemble based on various multiverse models involving eternal inflation. We calculate that it would take a monkey roughly 10^942 years to type a Shakespearean sonnet, which pushes the scenario into a World Ensemble. The evaluation of a World Ensemble based on various models of eternal inflation suggests that there is no middle ground between (...) eternal Poincare-Zermelo recurrence and a 0 probability in regards to the natural generation of the initial conditions of the universe. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements (...) of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

One of the most frequently voiced criticisms of free will skepticism is that it is unable to adequately deal with criminal behavior and that the responses it would permit as justified are insufficient for acceptable social policy. This concern is fueled by two factors. The first is that one of the most prominent justifications for punishing criminals, retributivism, is incompatible with free will skepticism. The second concern is that alternative justifications that are not ruled out by the skeptical view per (...) se face significant independent moral objections. Yet despite these concerns, I maintain that free will skepticism leaves intact other ways to respond to criminal behavior—in particular preventive detention, rehabilitation, and alteration of relevant social conditions—and that these methods are both morally justifiable and sufficient for good social policy. The position I defend is similar to Derk Pereboom’s, taking as its starting point his quarantine analogy, but it sets out to develop the quarantine model within a broader justificatory framework drawn from public health ethics. The resulting model—which I call the public health -quarantine model—provides a framework for justifying quarantine and criminal sanctions that is more humane than retributivism and preferable to other non-retributive alternatives. It also provides a broader approach to criminal behavior than Pereboom’s quarantine analogy does on its own. (shrink)

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)

This paper argues that a popular account of intentions can be extended to beliefs. Beliefs are stable all-out states that allow for planning and coordination in a way that is tractable for cognitively limited creatures like human beings. Scepticism is expressed that there is really anything like credences as standardly understood.

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus (...) be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

(Version 2.4) I have argued elsewhere for ascribing an error theory about all normative and evaluative judgements to Nietzsche. Such a nihilism brings with it a puzzle: how could we—or at least the select few of us being addressed by Nietzsche—continue in the face of this nihilism? This is a philosophical puzzle and so, defeasibly, an interpretive puzzle. If there is no theory it would make sense for Nietzsche to have about how the select few could go on, then this (...) is some evidence against the proposed interpretation of him as a nihilist. I defended the interpretation by arguing that Nietzsche’s declarations about creating values point to a practice of generating honest evaluative illusions. Such honest evaluative illusions are tricky things, though, and, precisely because they are honest, one might worry that they lack the motivational power of genuine evaluative belief. Can they truly play the role that evaluative beliefs play in our psychological economies? I suspect that Nietzsche does not want the honest illusions to play exactly the role that evaluative beliefs played. The cheerfulness, the playfulness, the lightness that Nietzsche hopes for are, I have suggested, a function of the shift from belief to pretence, from illusion to honest illusion. The question, nonetheless, is whether the resulting picture is too light. Can I go through life merely acting, as a critic might put it? My suggestion in this essay will be that the thought of eternal recurrence is meant to add weight to the lightness of acting—“acting”, obviously, in both the here relevant senses of the word. (shrink)

The relationship between attention and consciousness is one that is crucial for understanding perception and different types of conscious experience, and we commend this analysis of the topic by Pitts, Lutsyshyna, and Hillyard (2018). We have also examined this relationship closely (e.g., Montemayor & Haladjian, 2015) and would like to point out a few potential contradictions in the Pitts et al. paper that require clarification, particularly in the attempt to reconcile aspects of recurrent processing theory (RPT) with global neuronal (...) workspace theory (GNWT). This commentary addresses these points and introduces other considerations important for this topic. (shrink)

Bartha and DesRoches (Synthese 199(3–4):8701–8740, 2021) and Steel and Bartha (Risk Analysis 43(2):260–268, 2023) argue that we should understand the precautionary principle as the injunction to maximise lexical utilities. They show that the lexical utility model has important pragmatic advantages. Moreover, the model has the theoretical advantage of satisfying all axioms of expected utility theory except continuity. In this paper I raise a trilemma for any attempt at modelling the precautionary principle with lexical utilities: it permits choice cycles (...) or leads to paralysis or implies that the smallest value difference that is possible in a context has extreme axiological implications. (shrink)

There is growing concern that a global economic system fueled predominately by financial incentives may not maximize human flourishing and social welfare externalities. If so, this presents a challenge of how to get economic actors to adopt a more virtuous motivational mindset. Relying on historical, psychological, and philosophical research, we show how such a mindset can be instilled. First, we demonstrate that historically, financial self-interest has never in fact been the only guiding motive behind free markets, but that markets themselves (...) are representations of our individual and collective moral identities. Building on this understanding, we review the research on how economic incentives crowd out virtue-oriented concerns. We then introduce the concept of moral self-awareness (MSA), an evolving mindset informed by reflection on moral identity, namely what one’s actions say about oneself given the impacts on others or society that one’s action may effect. MSA comprises three fundamental aspects of virtue-oriented reasoning: pride, shame, and guilt. Finally, we offer a four-stage model anchored in systems theory, yielding ever more refined motivating strategies for maximizing human flourishing and social welfare externalities. (shrink)

In this paper I offer a model-theoretic interpretation of Autonomy Theory as defended by Moreno, Mossio, Montévil and Bich. I address accusations that Autonomy Theory is excessively liberal, such as those made by Garson (2017), arguing that these misunderstand the role of strategic abstractions and generalizations in theory construction. Conceiving of closure of constraints as a model-building effort that emphasizes generality – in the spirit of Levins (1966) – also clarifies its potential for application in empirical contexts.

Of the various loci of systematic theology that call for sustained philosophical investigation, the doctrine of sanctification stands out as a prime candidate. In response to that call, William Alston developed three models of the sanctifying work of the Holy Spirit: the fiat model, the interpersonal model, and the sharing model. In response to Alston’s argument for the sharing model, this paper offers grounds for a reconsideration of the interpersonal model. We close with a discussion (...) of some of the implications of one’s understanding of the transforming work of the Holy Spirit for practical Christian spirituality. (shrink)

One striking feature of the contemporary modeling practice is its interdisciplinarity: the same function forms and equations, and mathematical and computational methods are being transferred across disciplinary boundaries. Within philosophy of science this interdisciplinary dimension of modeling has been addressed by both analogy and template-based approaches that have proceeded separately from each other. We argue that a more fully-blown account of model transfer needs both perspectives. We examine analogical reasoning and template application through a detailed case study on the (...) transfer of the Ising model from physics into neuroscience. Our account combines the analogy and template-based approaches through the notion of a model template that highlights the conceptual side of model transfer. (shrink)