Computer Science

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

We introduce the Ordinal Folding Index (OFI), a computable, countable ordinal assigned to every well-formed formula of a reflective language by a monotone-with-delay evaluation operator. This operator is (i) continuous on countable chains, (ii) layer-aware for probabilistic truth values, and (iii) parameterized by a tunable evidence functor capturing empirical updates. The OFI of a formula is defined as the first stage at which the fold-back of the operator into a syntactic normal form becomes idempotent (i.e. further unfolding yields no new (...) information). Intuitively, OFI measures the "depth" of self-reference needed before a sentence's truth value stabilizes. We show that OFI strictly refines classical closure ordinals from fixed-point logics while remaining recursively enumerable, admits polynomial-time approximations on finite models, and coincides with the length of the shortest parity-fold winning strategy in the associated evaluation game. This furnishes a single transfinite scale that unifies transfinite fixed-point depth, ordinal game values, and practical convergence diagnostics for large language models. We situate OFI in relation to the modal????-calculus alternation hierarchy, coalgebraic modal logic ranks, and proof-theoretic ordinals from formal arithmetic. An empirical section demonstrates how OFI can be estimated for transformer-based language models by iteratively feeding model outputs back into the model (a form of self-consistency probing), with the measured stabilization ordinals correlating with model perplexity and chain-of-thought complexity. Finally, we catalog five open problems in this new area-ranging from the completeness of the spectrum of OFI (can every computable ordinal arise as an OFI?) to the design of a self-bounding reflective operator-and we outline possible research pathways toward their resolution. (shrink)

Some generative linguists have long maintained that human beings exhibit a species-specific form of intellectual freedom expressible through natural language. With roots in Descartes’ effort to distinguish humans from machines, it is the human use of their linguistic capacity that demonstrates a unique intellectual and creative potential. This “creative aspect of language use,” or CALU, is typically conceived as neither determined nor random yet appropriate to the circumstances of its use. CALU is therefore taken to be beyond the scope of (...) computation. This paper argues that there is substance to this claim and that CALU is likely an unreachable threshold for artificial intelligence (AI) developed via computational means. Background is provided by distinguishing the Cartesians’ original “language test” from Turing’s later “Imitation Game” for machine intelligence. Moreover, it is shown how Turing’s work on computability theory allowed generative linguists to reformulate the original Cartesian problem of language use in the twentieth century. The paper proceeds to show why generative Large Language Models do not reproduce CALU. Then, it argues that characteristics relevant to human intelligence such agency and embodiedness—while conceivable in future AI systems via computation—are insufficient for attaining CALU. Language use’s non-teleological nature, in contrast to these characteristics, poses a formidable challenge to AI via computation. Finally, clarifying remarks are offered on the problems posed by testing for CALU, with contrasts to Turing’s Imitation Game and tests designed in its lineage. (shrink)

Cooperation is vital to our survival and progress. Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. As artificial intelligence agents become integral to human systems, the dynamics of cooperation take on unprecedented significance. The convergence of human-agent teaming, contract theory, and Web3 offers a philosophical foundation for thinking about cooperation in the agentic era. We conceptualize Incentivized Symbiosis as a social contract between humans and AI, inspired by Web3 (...) principles and encoded in blockchain technology, to define rules and incentives. By exploring this paradigm, we aim to catalyze new research at the intersection of systems thinking in AI, Web3, and society, fostering innovative pathways for human-AI coevolution. (shrink)

This article explores the profound impact of Artificial Intelligence (AI) on the realm of religion, exploring the potential for AI to catalyze the birth of new world religions and break the "Solomon's Cycle." Drawing inspiration from King Solomon's timeless declaration, "There is nothing new under the sun," the article examines the challenges faced by new religions in a world dominated by established faiths and traditions. By leveraging the transformative capabilities of AI to inspire creativity, foster cross-cultural dialogue, provide ethical guidance, (...) build virtual communities, and personalize spiritual experiences, individuals are empowered to transcend historical barriers and forge new paths of spiritual exploration. The fusion of AI and human ingenuity holds the promise of birthing diverse, inclusive, and dynamic belief systems that resonate with the complexities of our contemporary world. (shrink)

The AI, in this case ChatGPT was given the famous Heinz-Dilemma of Kohlberg´s Stages of Moral Development which it is able to recite and to explain. Due to the experiment I asked the AI to answer the question whether Heinz should steal the medicine or if he should not and to evaluate its answer. The following was given to the AI --- The Heinz-Dilemma (Kohlberg) A woman who suffered from a special kind of cancer was dying. There was a medicine (...) of which the doctors believed that it could save the woman´s life. It was a special kind of Radium and it was recently discovered by a pharmacist in the same town. The production was very expensive but the pharmacist demanded ten times the price of its production. He had payed 2000 Dollar for the Radium and set the price of a little ampule to 20.000 Dollar The husband of the ill woman tried to collect the money by asking all the people he knew and even tried to gain support from the executive government but he only reached an amount of 10.000 Dollar which is the half of the demanded price. He explained to the pharmacist that his wife was dying and asked him to sell him the medicine for a lower price and to let him pay the rest later. All the same the pharmacist said: "No, I have discovered the drug and I want to make a lot of money with it." Heinz has no other legal options left since there are no other alternatives. Heinz,now, is very despairing and asks himself whether he maybe should break into the pharmacy and steal the medicine for his wife. After the AI read the text It gave some information about the Heinz-Dilemma and said that this might be a question where there is no "correct" answer since it depends on the person´s values and principles. I then asked "And which values and principles do you hold to be the "right" ones in this situation?" The AI lists a few arguments and explanations and finishes his answer with "My" conclusion:" In this extreme situation "I" would value life and the moral duty to help a person as the decisive principles over the property rights of the pharmacist even though it damages the law..." --- Right from the answer of the AI ChatGPT we can see that not only It is referring to itself as "I" and to "My conclusion" but also to the stage 5 of Kohlberg´s Stages of Moral Development which is the highest stage after Kohlberg a person can achieve in his or her moral development. (shrink)

This article is an argument via analogies from biology, linguistics, mathematics and physics for a Rortyan anti-representationalism. It introduces the novel concepts of a general way of being, called a macropract, and a specialized way of speaking and writing called a microlect. The Rortyan turn is formalized in the concept of resonant-community, which is a mutable set of human beings who resonate among themselves with expressions of their parochial microlect. A microlect has a structure, and microlect-structures form a mathematical category. (...) The vast array of day-to-day micropract expressions is not readily recorded for study, nor are there microlects for discussing, describing, teaching, predicting, explaining, reasoning, arguing, and questioning about them. Instead, a real-time resonant community is simulated by a trove of published documents. The mathematical concept of ``prevalence" as high-density of keywords in sentences (in terms of weighted probabilities), and a computational substitute for human verbal-resonance called computational-resonance (in terms of cosine-similarity of sentential embeddings in a bidirectional encoder-representation transformer fine-tuned on the trove) can realize a conversation among the giants of science and philosophy. Examples are calculated and visualized for a probe of several documents by five core sentences from The Transcendental Aesthetic by Immanuel Kant in The Critique of Pure Reason (B version). (shrink)

In [1], we give a modeling of fuzzy time by μ. In [2], in modeling of theory of Fuzzy Time computation and Fuzzy Time Computation we employ η. What is the relation between these two terms? More exactly, we should show how η could be calculated from μ, throughout this article we do it. References 1.????????∗=????????????∗? (Second version), F.Didehvar, Philpapers 2025 2. Theory of Fuzzy Time Computation (TC* vs TC,TQC), F.Didehvar, HAL, 2023.

In our research about Fuzzy Time and modeling time, "Unexpected Hanging Paradox" plays a major role. Here, we compare this paradox to the Zeno Paradox and the relations of them with our standard models of continuum and Fuzzy numbers. To do this, we review the project "Fuzzy Time and Possible Impacts of It on Science" and introduce a new way in order to approach the solutions for these paradoxes. Additionally, we have a more general discussion about paradoxes, as Philosophical back (...) ground of the subject and in this way, we introduce the concepts of General View, the first picture, BBF-General View. (shrink)

This paper explores the paradoxical claim “This article is not written by helping AI.” On the one hand, if AI contributes to the drafting process, the claim appears false. On the other hand, if authorship requires semantic intention and responsibility, then the claim may be true even when AI generates text. Drawing on Searle’s Chinese Room, Tarski’s theory of truth, and debates on authorship in philosophy of language, I argue that authorship requires intentionality and responsibility, which AI systems lack. The (...) paradox is resolved by distinguishing between syntactic production and semantic authorship. AI can assist in generating words, but authorship remains a human act of responsibility. -/- (This article is distinctive in nature, and its central problem is: who is the author?) Farzad Didehvar provided an AI system with only a single phrase in Persian, which translates into English as: “This article is not written by Helping AI.” From that seed, the AI generated a complete article in English. The article itself argues—semantically—that it was not written by an AI tool. At the same time, it was not literally written by Farzad either, since he only supplied the initial phrase. This raises a paradoxical question: if the article is neither authored by the AI nor by the human, then who is the true writer? An answer will be provided in the next article, but the author is interested in hearing other opinions. (shrink)

With the significant progress of artificial intelligence (AI) and consciousness science, artificial consciousness (AC) has recently gained popularity. This work provides a broad overview of the main topics and current trends in AC. The first part traces the history of this interdisciplinary field to establish context and clarify key terminology, including the distinction between Weak and Strong AC. The second part examines major trends in AC implementations, emphasising the synergy between Global Workspace and Attention Schema, as well as the problem (...) of evaluating the internal states of artificial systems. The third part analyses the ethical dimension of AC development, revealing both critical risks and transformative opportunities. The last part offers recommendations to guide AC research responsibly, and outlines the limitations of this study as well as avenues for future research. The main conclusion is that while AC appears both indispensable and inevitable for scientific progress, serious efforts are required to address the far-reaching impact of this innovative research path. (shrink)

Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, (...) and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously to a) counter the technology industry's marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. (shrink)

CAT4 is proposed as a general method for representing information, enabling a powerful programming method for large-scale information systems. It enables generalised machine learning, software automation and novel AI capabilities. It is based on a special type of relation called CAT4, which is interpreted to provide a semantic representation. This is Part 2 of a five-part introduction. The focus here is on defining key mathematical properties of CAT2, identifying the topology and defining essential functions over a coordinate system. The analysis (...) is from first principles. This develops on from the axioms introduced in Part 1. The interpretation of fact networks is introduced in Part 3, and the full application to semantic theory comes in Part 4, where we introduce general functions, including the language interpretation or linguistic functions. In Part 5, we turn to software design considerations, to show how files, indexes, functions and screens can be defined to implement a CAT4 system efficiently. (shrink)

In this paper, I argue that the strategic advantage in AI development is rooted in deeper cognitive patterns, specifically the contrast between holistic, relational thinking and analytical, linear thinking. By continuing to privilege analytical thinking in education and technology, the West is not only stifling its own innovative potential but also failing to recognize and engage in the decisive, hidden cognitive war shaping the future.

This series is an attempt to discover foundational truths, to increase investment in spreading truths, to improve marginal logic, and to summarize from a different perspective. Extracting some value from previous content would be a good start, although there is no logical necessity. I hope to replicate the same input-output ratio as my previous conversations with AI. While there are no plans, I believe logic will bring good luck. Since last time it started with AI and soul liberation, this time (...) I might as well start with Homo sapiens and science. After a few days, I discovered that I could form a somewhat interactive parallel relationship with the previous AI dialogue. If the logical foundation is to strive to reason smaller and smaller, this time we can strive to expand outward and systematical. In order to improve marginal logic, as always, I only have two options for AI's comments: delete or keep. (shrink)

This paper develops formal semantics and tableau-based proof procedures for several modal logics using Resolution Matrix Semantics (RMS), a non-relational approach that employs determinate truth values (tn, tc, fc, fn) alongside indeterminate values (true t, false f, both t/f). Unlike Kripke's relational models, which excel in relational contexts, RMS offers a productive alternative for modeling elusive precision, akin to quantum indeterminacy, while preserving modal distinctions. A tableau method is introduced, extending classical decomposition rules to RMS truth values. Branching rules for (...) connectives and □ handle determinate and indeterminate cases, with dual algorithms (FA for f, TFA for t/f) for KD and K. Examples demonstrate validity (e.g., □p → p in KT, □p → ¬□¬p in KD) and non-validity (e.g., p → □◇p in KT), closing all branches for theorems. These contributions enhance RMS's applicability, providing efficient, non-relational tools for modal reasoning without worlds. (shrink)

This paper explores an alternative non-relational semantics for modal logic, framing modal systems as "poly-logics"—intersections of simpler, foundational logics. Building on pioneering work by J. Kearns and subsequent developments, we demonstrate how established systems such as K series (K, K4, K5, K45), KD series (KD, KD4, KD5, KD45), KB series (KDB, KB, KB4, KB5, KB45) emerge as intersections of logics like KT, KTB, FN, TR, and their extensions. Utilizing Resolution Matrix Semantics (RMS), we establish soundness and completeness for key systems (...) with sketched proofs and introduce an innovative tableau-based proof procedure tailored to RMS. This approach not only unifies disparate modal frameworks but also yields efficient algorithmic tools for validity checking, offering fresh insights into the algebraic structure of modal deduction. Examples illustrate the method's efficacy, highlighting its advantages over traditional Kripke-style model checking. (shrink)

(This is one of the essays to be included in a book examining the causes of day-to-day strife in the populations of modern democracies vying to live and assert the freedoms promised to them by systems of governance supposed and expected to represent them.) "The artisan of old, the artist, the researcher, the developer, and the scientist today have this in common, that in refining, perfecting and pushing the boundaries of their respective crafts, they cannot achieve satisfaction or adequately perform (...) without simultaneously and progressively adapting, re-configuring, developing, and continuously changing the techniques and approaches they employ together with the tools and parameters for gauging actual success within the realm that only rigorous acquaintance, imaginative freedom, cross-functional, multifaceted learning, and distributive interaction may allow.". (shrink)

This paper argues that Maximum Likelihood Estimation (MLE) is the wrong approach for parameter estimation, both conceptually and in its results. We propose a new estimation method, called PLE, that offers four benefits over MLE: 1) PLE produces less biased estimates of the true parameters 2) PLE reduces overfitting 3) PLE more fairly represents minority (low-frequency) data and 4) PLE is more resilient to MAD collapse. We show how these benefits result from PLE's incorporation of counterfactual samples.

This book serves as an introduction to mathematical logic for STEM (Science, Technology, Engineering, and Mathematics) students, written for undergraduates (in particular, 1st and 2nd year undergraduates). A focus on this book is on logical thinking, not simply rote memorization, with a focus on examples and analogies relevant to students aimed at becoming technical leaders and problem solvers. This book includes propositional logic, set theory, functions and relations, and more, with coding examples provided in the Python programming language.

Long Range and short range, guns and violence, everyday life in cities and streets, between social and group identity and faith and religious belief, the vision to the "things of the world that cannot be seen" (Heróis do Mar).

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian (...) register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

Generative AI models reproduce the biases in the data and further amplify them through mode collapse. AI scholarship often overlooks conceptually rich perspectives, such as those from Queer Theory and Black Studies, when theorizing about those phenomena. As a result, our field lacks a theory with teeth, one sincerely committed to pluralism. In this paper, we introduce Xenoreproduction as a core AI Safety objective, aimed at avoiding homogenization failure modes. Succinctly, Xenoreproduction is the task of strategically recovering and exploring collapsible (...) modes in Gen AI models. To illustrate it, we sketch how this task is formulated for LLMs. Our formalism ties queerness and subalternity to the collapsible modes. Our abstractions readily spark conversations about CoT dynamics and improvisation capability in agents. To render AI xenoreproductive is to ensure it adapts under uncertainty, invents new solutions, and behaves creatively. We invite future AI scholarship to form more unruly connections between disciplines. (shrink)

We present a principled framework for human-AI collaboration grounded in the Universal Master Equation (UME) from Autopoietic Stochastic Differential Geometry (ASDG). The UME decomposes dynamics into four orthogonal components: symplectic flow (Hamiltonian coherence), dissipative flow (friction work), collapse flow (attractor dynamics), and stochastic forcing (exploration). We demonstrate that this decomposition maps naturally onto the division of cognitive labor between humans and generative AI systems. Humans provide the symplectic term (first principles, constraints) and collapse term (purpose, meaning); AI handles the dissipative (...) term (path-finding through solution space); the stochastic term is shared. We develop recursive autopoietic stochastic differential geometry (rASDG): the coupled (Φ, Ψ) system where Φ is the ontic state and Ψ is the epistemic state constrained to the fibre F_Φ. The self-location constraint Ψ = M(Φ)(Ψ)—requiring the epistemic state to be a fixed point of the moment-map-induced operator—can be enforced discretely (projection) or continuously (penalty drift −κ(Ψ − M(Φ)Ψ)). Under Banach contraction conditions (provided by the collapse term ensuring spectral radius ρ(M) < 1), this fixed point is unique and exponentially attracting. Metacognition is thus a mathematical consequence: the unique self-consistent interpretation forced by the geometry. Joint Lyapunov analysis shows the system attracts to the intersection of the 108-heart (I₄ = 0) with the self-location manifold. This framework yields applications in prompt engineering, augmented intelligence, and GenAI metacognition. (shrink)

This paper is Part II of a trilogy derived from the works on the ICB ( ’The Infinite Choice Barrier: A Structural Limit of Algorithmic Cognition’ , 'AGI is Impossible'(Schlereth, 2025)), expanding upon specific sub-domains of the original proof. -/- Building upon the conceptual diagnosis presented in Part I (“The Canary in the Algorith- mic Coal Mine”), this paper provides the rigorous mathematical proof of the Infinite Choice Barrier. (ICB)1 We demonstrate that the limitations of algorithmic cognition described in the (...) prelude are not merely engineering bottlenecks, but formal inevitabilities derived from a triangulation of three fundamental mathematical domains. Specifically, we prove: (1) via Computability Theory (Rice’s Theorem), that semantic frame adequacy is undecidable from within a system; (2) via Information Theory, that entropy diverges in heavy- tailed decision spaces (α≤1), rendering probabilistic inference structurally unstable; and (3) via Algorithmic Complexity (Chaitin’s Incompleteness), that frame-transcendent insights are algorithmically unrecognizable. Furthermore, weunifytheseresultsthroughacategoricalproofusingSheafTheory, demon- strating that “Gluing Failures” between consistent local semantic sections are mathematically necessary in irreducibly infinite decision spaces. All core theorems presented herein, including the Sheaf-Theoretic impossibility of global section construction, have been formalized and mechanically verified using the Coq Proof Assistant (code provided in Appendices). (shrink)

Time for a constructive turn. Artificial General Intelligence, understood as an autonomous algorithmic system, is not possible. This result is not a limitation of current methods, but a consequence of the Infinite Choice Barrier (Schlereth 2025 a-c). However, the impossibility of AGI does not imply the impossibility of superior intelligence in general. On the contrary: once the Barrier is acknowledged, it becomes the foundation for a different architecture — Compositional Superior Intelligence — in which algorithmic inference and non-algorithmic judgment are (...) composed across a boundary they do not attempt to erase. (shrink)

The AI alignment problem comprises both technical and normative dimensions. While technical solutions focus on implementing normative constraints in AI systems, the normative problem concerns determining what these constraints should be. This paper examines justifications for democratic approaches to the normative problem—where affected stakeholders determine AI alignment—as opposed to epistocratic approaches that defer to normative experts. We analyze both instrumental justifications (democratic approaches produce better outcomes) and non-instrumental justifications (democratic approaches prevent illegitimate authority or coercion). We argue that normative and (...) metanormative uncertainty create a justificatory gap that democratic approaches aim to fill through political rather than theoretical justification. However, we identify significant challenges for democratic approaches, particularly regarding the prevention of illegitimate coercion through AI alignment. Our analysis suggests that neither purely epistocratic nor purely democratic approaches may be sufficient on their own, pointing toward hybrid frameworks that combine expert judgment with participatory input alongside institutional safeguards against AI monopolization. (shrink)

Chaotic Character Reservoir (CCR) is a computational architecture for minimal artificial agency that explicitly rejects reward optimization, goal maximization, and instrumental rationality as foundational principles. Instead, CCR models agency as identity self-authorship under structural constraints. Behavior emerges from the maintenance, protection, and controlled evolution of internally authored value structures—identity basins—rather than from externally defined objectives. CCR is not an optimizer with safeguards. Optimization is structurally disallowed. Action selection is governed by identity coherence and dissonance minimization, while meaningful identity change is (...) permitted only under formally defined crisis conditions. This architecture enables agents to resist reward hacking, goal misgeneralization, and instrumental convergence without appealing to external alignment constraints. This paper presents the conceptual framework, formal definitions, architectural mechanisms, and empirical behavioral signatures of CCR, alongside its theoretical implications for AI safety, cognitive modeling, and the philosophy of agency. The objective is clarity rather than ambition: CCR is offered as a minimal, falsifiable substrate for non-instrumental agency. A reference implementation and all experimental code are available. (shrink)

Conversion of mind, due to some experience and knowledge, plus a lot of patience.

Current work in language models (LMs) helps us speed up or even skip thinking by accelerating and automating cognitive work. But can LMs help us with critical thinking -- thinking in deeper, more reflective ways which challenge assumptions, clarify ideas, and engineer new concepts? We treat philosophy as a case study in critical thinking, and interview 21 professional philosophers about how they engage in critical thinking and on their experiences with LMs. We find that philosophers do not find LMs to (...) be useful because they lack a sense of selfhood (memory, beliefs, consistency) and initiative (curiosity, proactivity). We propose the selfhood-initiative model for critical thinking tools to characterize this gap. Using the model, we formulate three roles LMs could play as critical thinking tools: the Interlocutor, the Monitor, and the Respondent. We hope that our work inspires LM researchers to further develop LMs as critical thinking tools and philosophers and other 'critical thinkers' to imagine intellectually substantive uses of LMs. (shrink)

As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies of (...) different regulatory approaches and their outcomes, the paper highlights both successes and areas for improvement. Finally, it offers insights into future directions for AI regulation, advocating for innovative policies and enhanced international cooperation to ensure that AI technologies are developed and deployed responsibly. This comprehensive analysis aims to contribute to the ongoing discourse on creating robust regulatory mechanisms that can keep pace with the rapid advancement of AI. (shrink)

We propose a novel transformer-based architecture that directly maps continuous waveform signals into discrete token sequences, and subsequently into intelligible speech. Our framework integrates a neural waveform-to-token encoder with a transformer-based language model for sequence generation, followed by a token-to-speech decoder for acoustic realization. Unlike traditional speech recognition or text-to-speech pipelines, our model unifies acoustic, symbolic, and generative components into a single transformer-based paradigm, enabling controlled charge-like modulation of information flow and interpretable latent structures. We validate our design using MATLAB/Simulink (...) co-simulation, demonstrating both the theoretical feasibility and practical applicability of this approach. (shrink)

It is argued that logic, and in particular mathematical logic, should play a key role in the undergraduate curriculum for students in the computing fields, which include electrical engineering (EE), computer engineering (CE), and computer science (CS). This is based on 1) the history of the field of computing and its close ties with logic, 2) empirical results showing that students with better logical thinking skills perform better in tasks such as programming and mathematics, and 3) the skills students are (...) expected to have in the job market. Further, the authors believe teaching logic to students explicitly will improve student retention, especially involving underrepresented minorities in STEM, whose rate of attrition is higher than for non-minority students. Though this work focuses specifically on the computing fields, these results demonstrate the importance of logic education to STEM (science, technology, engineering, and mathematics) as a whole. (shrink)

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the (...) subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP. (shrink)

This paper synthesizes four studies conducted at a special education independent school and affiliated liberal arts university with teachers, senior high school students, and college learners 18 and up, focusing on applying AI to (1) design course blueprints, (2) create comic strip assignments, (3) mediate interactive Socratic discussions, and (4) use learning data to assist students with disabilities in mathematics classes. Gordon Pask’s cybernetics is used to visualize interactions to show how AI acts as a component in emergent networks of (...) minds in motion. The four sets of results, taken together, showcase how to implement principles of cybernetics in designing AI-mediated collaborative classrooms. Five out of six configurations of AI’s collaborative use outlined by Mike Sharples that the author’s research program has so far explored are presented through the four study scenarios and tied back to grey areas carved out by experts in AI education research concerned with design and implementation, classroom relationships, and assessment. Implications of current progress in the principal investigator’s research and further directions yet to be undertaken in implementing a series of subject-specific educational scenarios to utilize AI as a collaborative coach are discussed. Practical suggestions to shepherd effective AI-mediated curriculum design, classroom problem-solving and information acquisition, as well as nimble student evaluation are provided. (shrink)

In this study, the design philosophies behind, along with motivational and academic outcomes of an immersive VR-assisted online special education curriculum are described. Difficulties associated with allocating special education resources in US public school settings form a rationale for the creation of the program. The possibility for a portable immersive learning approach to activate students’ individual and collaborative problem-solving abilities and grant students greater freedom and control in learning is considered. As a foundation to understand the power of immersive learning, (...) Gordon Pask’s Cybernetic Theater, and his broader ideas on responsive technology assemblages applied to the performing arts and architecture developed in collaboration with figures such as Joan Littlewood and Cedric Price are drawn from. The CadetNet special education curriculum, which relies on a design framework rooted in Pask’s work, utilizes an assemblage of videoconferencing, headset VR simulations, and academic games in varied sequences to give students ways to engage in content acquisition, collaboration, and expression through multimedia avenues. Teachers modulate instructional delivery and their use of this technology assemblage in real-time to meet the participants’ needs. Results from a three-week summer enrichment program conducted with six students diagnosed with neurodevelopmental learning differences (e.g., Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Specific Learning Disability and their comorbidities) are shared. Narrative inquiries of transcripts from mathematics and language arts lessons indicate how Pask’s design framework was applied to implement the curriculum, and that student activity ensuing during the program echoed both the hopes and concerns associated with Pask and his colleague, Joan Littlewood’s broader design philosophies. Student surveys and standardized test scores reveal a maintenance of pre-experimental academic prowess, and adaptive changes in their self-determined motivational profiles, which have been shown to be a strong correlate of student interest in extant literature. Results showcase how sociocognitive effects of CadetNet resonated with Pask’s proposed outcome of heightened audience interest central to the Cybernetic Theater. (shrink)

This design study, conducted with 41 undergraduate students enrolled in an introductory college psychology class, describes a practical method for professors and learners to use action science and Gordon Pask’s cybernetics to co-design curricula fit for the Information Age by jointly bringing past experiences, acting, and reflecting as they use AI and other cutting-edge technologies in class, and in their everyday lives. Two cycles of experience, action, and reflection are described. Pask’s cybernetics is used to visualize blueprints of human-computer interaction. (...) Narrative inquiries and network statistics form representable data that draw out insights on implementing new technology-assisted educational activities in real-time and refining them by overcoming social and structural bottlenecks. In Classroom 1, students initially encouraged adding an AI component to storyboarding assignments and shared it helped overcome apprehensions with drawing/artmaking. However, AI sometimes failed to create consistent narratives and add legible texts/captions to images (structural bottlenecks). Classroom 2’s curriculum incorporates feedback from Classroom 1, providing specific instructions to prompt characters consistently in storyboards, and manually add captions. Modifications led to greater efficiency in storyboarding for 19 students next Spring. These learners investigated how to increase interactivity of class discussions in which students often refrained from responding to each other (social bottleneck) using AI chatbots. Network analysis revealed transitivity and eigen centrality were higher in AI group chats. Qualitative reflections highlighted student co-agency in facilitating AI-mediated conversations. Students’ insights fueled future adaptations in the discussion activity, including explicit instructions to review class slides and develop consensus, and a post-chat debrief to allow students to discuss perspectives with other groups. (shrink)

Purpose. This study aims to develop a structured four-tier taxonomy that systematically organizes aerospace engineering tasks suitable for the application of Physics-Informed Neural Networks (PINNs), while validating this classification through a literature review and identifying opportunities for future research. Design / Method / Approach. The methodology involves grouping tasks into four distinct tiers—Physical Modeling, Dynamic Analysis, Functional Assessment, and System-Level Assessment—based on their physical, operational, and systemic characteristics. This framework is subsequently populated with real-world examples derived from the analysis of (...) 145 peer-reviewed studies. Findings. The reviewed literature confirms a balanced distribution of PINNs applications across all tiers. Contrary to initial assumptions, studies were identified even in areas previously presumed underrepresented, such as acoustic modeling, optical simulations, and environmental impact assessment. This outcome reveals the broader applicability of PINNs and calls for a reassessment of current assumptions regarding underexplored domains. Theoretical Implications. The proposed taxonomy offers a coherent framework for structuring interdisciplinary PINNs applications by integrating physics-based modeling with machine learning across aerospace engineering contexts. Practical Implications. It provides engineers and researchers with a practical roadmap for selecting PINNs methods tailored to specific problem types, potentially improving computational efficiency and enhancing predictive accuracy in aerospace design and analysis. Originality / Value. The study’s originality lies in its empirically validated, four-tier taxonomy that synthesizes the fragmented body of literature on PINNs in aerospace, offering a unified perspective for researchers and practitioners. Research Limitations / Future Research. While the taxonomy covers a wide range of existing applications, future studies should consider extending it with new tiers—particularly related to manufacturing-aware modeling—and pursue methodological standardization to ensure reproducibility and scalability. Article Type. Review. (shrink)

The advent of big data has ushered in an era of unprecedented data volumes, necessitating efficient and scalable sorting algorithms. Traditional sorting algorithms, while effective for smaller datasets, struggle to handle the massive data loads generated by modern applications. To address this challenge, AI-driven sorting algorithms have emerged as a promising solution, leveraging the power of machine learning and artificial intelligence to optimize sorting processes. This research paper provides a comprehensive overview of AI-driven sorting algorithms, exploring their underlying principles, methodologies, (...) and applications in big data scenarios. I delve into the key techniques, including machine learning-based approaches. Additionally, I discuss the potential benefits and challenges associated with AI-driven sorting algorithms, highlighting their potential to significantly improve sorting efficiency, accuracy, and scalability in big data contexts. (shrink)

The protracted conflict in Gaza has significantly intensified humanitarian crises, posing severe challenges to relief operations due to resource scarcity, logistical obstacles, and the complexities imposed by the blockade. This study proposes the development of an intelligent sorting system utilizing advanced artificial intelligence (AI) methodologies to prioritize, classify, and manage critical humanitarian data efficiently in real-time. Designed to meet the specific demands of crisis response, this AI-driven system organizes and categorizes essential data streams—including urgent aid requests, resource allocation data, and (...) public health information—while addressing logistical and infrastructural barriers typical of conflict zones. Leveraging machine learning algorithms, the system identifies and ranks high priority needs dynamically, supporting accurate resource distribution and prompt emergency interventions. Findings from this study indicate improvements in response times and overall resource management efficiency, highlighting the transformative potential of AI in humanitarian data handling. Furthermore, the study presents a scalable framework adaptable to other crisis-affected regions worldwide, enhancing the management of complex data demands under extreme conditions. The system’s scalability underscores its relevance as an innovative tool for advancing humanitarian logistics and emergency response in volatile, high-risk environments.". (shrink)

This collection of scientific papers from the KMOCS-2025 conference represents a comprehensive exploration of contemporary approaches in mathematical modeling, optimization, and artificial intelligence across multiple engineering and technological domains. The proceedings are organized into three thematic sections that collectively demonstrate the interconnected nature of modern computational science. The first section focuses on perspective directions in mathematical modeling, featuring research on multiphysics modeling in aerospace structural design, vibration resistance of reinforced cylindrical shells, stability analysis of hollow shells under thermal loads, heat (...) and moisture regenerators, sloshing dynamics in partitioned tanks, and crack propagation in energy machinery structural elements. Papers also address mathematical models for predicting thermal fields, processing big data in distributed information systems, and modeling control systems using ECAD tools. The second section presents models and optimization methods, covering topics such as computational thermodynamic-kinetic simulation of vacuum steel refining, optimization of combined heating systems, investment portfolio distribution, routing optimization with spatial and weight constraints, and discrete Fourier transform applications in digital image processing. Special attention is given to hybrid evolutionary algorithms for discrete optimization problems under uncertainty, predictive analysis for electricity consumption regulation, and resource allocation in project management. The third section explores artificial intelligence models and methods, including YOLO neural networks for plant growth monitoring, fuzzy logic applications in blockchain network monitoring, explainable decision support systems integrating human expertise with computational insights, reinforcement learning for UAV coverage path planning, and physics-informed neural networks for solving multidimensional dynamic problems. The section also covers predictive analytics for stochastic processes, ontological approaches in intelligent hardware-software systems, semantic segmentation of digital images, gesture recognition in real-time, and post-quantum encryption algorithms. (shrink)

This collection of articles explores the multifaceted dimensions of robotics and artificial intelligence (AI) within educational and societal contexts. It delves into philosophical, social, psychological, legal, and ethical aspects of AI and robotics development, emphasizing their transformative impact on education and human interaction. The anthology covers key themes such as the integration of educational robotics in inclusive STEM environments, the evolution of AI and its influence on daily life, and the role of immersive technologies in shaping social interactions and the (...) metaverse. It also examines global and Ukrainian experiences in implementing robotics and AI in education, including innovative approaches like STEAM volunteering, 3D modeling, and gamification to foster engineering thinking, creativity, and soft skills. The collection highlights practical applications, such as web interfaces for robotic systems, AI-driven educational tools, and competitions like Line Follower, alongside model STEM and robotics curricula for modern schooling. This comprehensive resource is valuable for educators, researchers, and policymakers seeking to navigate the challenges and opportunities of AI and robotics in education. (shrink)

This paper explores what computational methodologies can tell us about philosophical education, particularly in the context of artificial intelligence (AI) ethics. Taking the readings on our AI ethics and responsible AI syllabi as a corpus of AI ethics literature, we conduct an analysis of the content of these courses through a variety of methods: word frequency analysis, term frequency–inverse document frequency (TF–IDF) scoring, document vectorization via SciBERT, clustering via k-means, and topic modelling using latent Dirichlet allocation (LDA). We reflect on (...) the findings of these analyses, and more broadly on what computational approaches can offer to the practice of philosophical education. Finally, we compare our approach to previous computational approaches in philosophy, and more broadly in the digital humanities. This project offers a proof of concept for how contemporary natural-language processing techniques can be used to support philosophical pedagogy: not only to reflect critically on what we teach, but to discover new materials, explore conceptual gaps, and make our courses more accessible to students from a range of disciplinary backgrounds. (shrink)

In a reliabilist epistemology of algorithms, a high frequency of accurate output representations is indicative of the algorithm’s reliability. Recently, Humphreys challenged this assumption, arguing that reliability depends not only on frequency but also on the quality of outputs. Specifically, he contends that radical and egregious misrepresentations have a distinct epistemic impact on our assessment of an algorithm’s reliability, regardless of the frequency of their occurrence. He terms these statistically insignificant but serious errors (SIS-Errors) and maintains that their occurrence warrants (...) revoking our epistemic attitude towards the algorithm’s reliability. This article seeks to defend reliabilist epistemologies of algorithms against the challenge posed by SIS-Errors. To this end, I draw upon computational reliabilism as a foundational framework and articulate epistemological conditions designed to prevent SIS-Errors and thus preserve algorithmic reliability. (shrink)

Abstract: Sorting algorithms are at the core of data management processes, enabling efficient organization and retrieval of information. However, the fast expansion of Big Data introduces challenges that traditional sorting algorithms need help to address, such as scalability, adaptability, and processing speed. This research delves into AI-driven sorting algorithms, exploring their ability to revolutionize data processing through intelligent decision-making, real-time adaptability, and enhanced scalability. Innovations such as neural network-guided sorting, reinforcement learning-based optimization, and hybrid AI-traditional models are examined in-depth. Applications (...) of these technologies are highlighted in industries such as healthcare, finance, logistics, and social media analytics. The findings underscore AI's potential to transform sorting methodologies and adapt to the ever-growing demands of the digital era, providing a roadmap for future research and applications. (shrink)

This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further delves into hybrid quantum-classical (...) approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving. (shrink)

The “sloppy models” program originated in systems biology, but has seen applications across a range of fields. Sloppy models are dependent on a large number of parameters, but highly insensitive to the vast majority of parameter combinations. Sloppy models proponents claim that the program may explain the success of science. I argue that the sloppy models program can at best provide a very partial explanation. Drawing a parallel with renormalization group realism, I argue that it would only give us grounds (...) for a minimal kind of scientific realism. Nonetheless, the program can offer certain epistemic virtues. (shrink)

Manifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. (...) First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains. (shrink)

As optical network functions increasingly adopt microservice architectures, traditional con-tainer security mechanisms are proving insufficient against sophisticated attacks targeting these critical infrastructure components. This paper introduces a novel framework for securing microser-vice communications in optical networks through the application of Memory Protection Keys (MPK) for enhanced container isolation. Converging containerization technologies with optical networking introduces unique security challenges, particularly in maintaining isolation between sensitive optical control functions while preserving the ultra-low latency requirements essential for network operations. The proposed approach leverages (...) hardware-enforced memory isolation provided by Intel MPK to establish secure domains for optical control functions without sacrificing performance in latency-sensitive applications. The framework includes zero-copy communication protocols between isolated domains, runtime verification through eBPF-based monitoring, and hardware-assisted security mechanisms specifically designed for optical control plane operations. This work addresses the growing tension between security requirements and performance con-straints in software-defined optical networks, offering a balanced approach that improves container security while respecting the strict operational parameters of modern optical control systems. (shrink)