With the growing importance of Artificial Intelligence (AI) in academic life, it is increasingly necessary to understand the impact of this technology on research activities, yet the quantitative relationships between the use of these tools and research productivity in university settings have not been sufficiently quantified. This study aimed to examine the frequency of AI tool use and the relationship between this use and attitudes towards AI and research productivity among university students and faculty. A cross-sectional survey was carried out with 220 university respondents (undergraduate students, post-graduate students, and Faculty/researchers) across five disciplines. Data were analyzed with descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation, chi-square test and multiple linear regression. Nearly half (49%) of those who responded reported using it regularly ('often'/'always'), and the most popular category of tools used was general-purpose AI assistants (44.1%). The frequency of the use of AI was positively correlated with productivity (r = .48, p < .001) and attitude (r = .37, p < .001), but there were no significant differences between gender (p = .77) or discipline (p = .62). The results indicated that together, usage frequency and attitude accounted for 30% of the variance in productivity. The two factors, together, explained 30% of the productivity (regression) variance. AI adoption is associated with higher research productivity for the institution across different groups of researchers by various disciplines and demographic groups, supporting the case for investing in structured training for AI literacy by institutions.
Research Paper, Asma Atta, Hafiz Kosar et al.· The social science· 0 citations
Urban sustainability assessment is crucial due to rapid urbanisation and environmental stress, particularly in the Kolkata Metropolitan Area (KMA), where land-use changes contribute to ecological degradation. This research develops and assesses the Urban EcoScore Index (UESI) as a method for evaluating biophilic potential and habitat-structural condition in the KMA, integrating six ecological indicators across three dimensions: ecological integrity, anthropogenic pressure, and spatial proximity to natural features. Each indicator is oriented a priori to a common ecological direction before aggregation, and indicator weights are derived through Principal Component Analysis rather than expert judgement. The assessment employs remote sensing and machine learning techniques to model ecological relationships and uses explainable AI methods, such as SHAP, to interpret these models. The UESI assessment showed pronounced spatial disparities across the KMA, with 21.9% (384 km 2 ) of the area classified as Poor, concentrated in urban cores. In contrast, 24.7% (433 km 2 ) fell under the Good category, while 8.6% (151 km 2 ) achieved Excellent conditions (UESI > 0.8), mainly in ecologically sensitive zones. Intermediate Fair (445 km 2 , 25.4%) and Moderate (339 km 2 ) zones indicate transitional areas where restoration could enhance ecological function. SHAP-based decomposition identified potential species richness and human disturbance as the indicators contributing most to the composite's spatial variability, consistent with the broader evidence on biodiversity and land use in urban ecology. UESI serves as a screening-level diagnostic for biophilic potential but does not assess actual human-nature interactions, biodiversity, or distributional equity, as these necessitate field surveys and socio-demographic data beyond the remote-sensing proxies utilized.
Version 6 (Augustus 2026) adds r(21) = e(21) = 231, closing the hardest rung to date after a five-day resistance documented in CHANGELOG_v6.md; the pattern now holds for thirteen consecutive values. -- Version 5 (August 2026) adds r(19) = 207 and r(20) = 220, extending r(n) = e(n) = U(n) to twelve consecutive values; the n=20 census was dual-computed by the established Python pipeline and a gate-validated native kernel with exact agreement. --- Version 4 (August 2026) closes the question left open in v3: r(18) = e(18) = 196, certified circle-inscribed and independently verified. The pattern r(n) = e(n) holds continuously for n = 9 through 18; the apparent separation was a search-capability artifact, documented in CHANGELOG_v4.md. --- Version 3 (August 2026) adds three results: r(17) = e(17) = 185, extending the circle-inscribed series to nine consecutive values meeting the proven combinatorial ceiling. r(18) >= 195, an exact circle-inscribed certificate one below the ceiling of 196. e(18) = 196: the first FREE-PLANAR certificate in this series, consisting of 54 rational point coordinates not on a circle, 196 sides meeting the ceiling, with a new decisive assertion verified in exact integer arithmetic: REGULARITY, i.e. the boundary cycle visits the 54 triangle corners with labels 0..17 repeated exactly three times. Consequently the sequence A375986 extends to a(18) = 196, attained off-circle, while the best known circle configuration at n = 18 has 195 sides: whether r(18) = 195 < e(18), which would be the first separation of the circle-restricted and regular quantities, or r(18) = 196, is open and under active search. All three new certificates passed the same five-tier verification standard as v1/v2 (two independently written exact-arithmetic verifiers, two execution environments, zero floating point in any decisive predicate); the three independent verifiers are included with SHA-256 hashes in CHANGELOG_v3.md. See CHANGELOG_v3.md for details and candid provenance notes. ----- Version 2 (August 2026) extends the results to n = 16: exact certificates for r(13)=137, r(14)=150, r(15)=161, r(16)=172 are added, each verified to the same standard as v1 (two independently written exact-arithmetic verifiers, two environments, zero floating point). See CHANGELOG_v2.md for details. The sequence A375986 now reads 3, 12, 22, 33, 45, 56, 67, 80, 91, 102, 115, 126, 137, 150, 161, 172. Summary This deposit contains explicit, exactly-verifiable configurations answering and extending open questions from: G. Alkauskas, Regular triangle unions with maximal number of sides, arXiv:2510.22584 (v5, April 2026). For n triangles inscribed in the unit circle with their 3n vertices in cyclic arrangement (a regular union, in the paper's sense), r(n) denotes the maximal number of sides of a union that is a simple polygon. The paper proves the combinatorial ceiling e(n) ≤ 12n − 12 − γ(n+1) with γ(n+1) = n + 2 − 2⌊(n+1)/3⌋, poses "prove rigorously that r(9) = 90" as Open Question 2, and asks in Question 3 to improve the bound r(n) ≥ 10n − 7. Main results certified here: r(9) = e(9) = 91 — answering Open Question 2 in the opposite direction to the conjecture; r(10) = e(10) = 102, r(11) = e(11) = 115, r(12) = e(12) = 126 — three new exact values of the sequence e(n) (cf. OEIS A375986: 3, 12, 22, 33, 45, 56, 67, 80, 91, ...), each meeting the proven ceiling; consequent data for Open Questions 6 and 7: the observed increments are 11, 13, 11 (exactly the ceiling increments; no increment of 14), consistent with limsup e(n)/n = 35/3. The certificates Each certificate (certificates/r{n}_exact_certificate.json) is a list of 3n rational numbers t, in increasing order. The corresponding vertex is P(t) = ((1 − t²)/(1 + t²), 2t/(1 + t²)), which lies exactly on the unit circle for rational t. Increasing t corresponds to circular order (wrapping through (−1, 0)); the vertex at position j belongs to triangle j mod n. The claim per certificate: the union of the n closed triangles is a simple polygon with exactly S sides (S = 91, 102, 115, 126), all 3n corners on its boundary in circular order. Verification Two independently written verifiers are included; both use only Python's standard-library fractions.Fraction — no floating point enters any decisive predicate: verifiers/exact_certifier_pipeline.py — the author-side certifier; verifiers/independent_verifier_generalized.py — an independent verifier written from scratch by OpenAI's ChatGPT on request, covering all four certificates. It additionally checks: no coincident vertices, no degenerate triangles, no vertex on a foreign edge, no collinear foreign edges, no endpoint/tangent contacts, no three concurrent edges, boundary graph 2-regular with a single component, no collinear boundary nodes, all corners genuine polygon vertices in circular traversal order, and connectedness of the triangle-interior overlap graph. verifiers/independent_verifier_n9.py is its original n = 9 version. Both verifiers were cross-executed in two separate environments with identical output. To verify yourself: python3 verifiers/independent_verifier_generalized.py (Python ≥ 3.9, no dependencies; runtime seconds to minutes). Method and provenance The configurations were found with substantial help from AI systems (Anthropic's Claude; independent verification code by OpenAI's ChatGPT). Blind numerical search over circle configurations reliably plateaus just below sharp optima (reproducibly 44/45 and 77/80 on the paper's known Pentastar/Octastar values, which may explain the experimental value 90 at n = 9 reported in the paper). The successful approach was combinatorics-first, built on the paper's own triangulation-shift tool: (1) exhaustively enumerate maximal-weight triangulation shifts of the (n+1)-gon; (2) compile each champion into its full boundary word (the compiler reproduces the paper's 79-edge worked example symbol-for-symbol and its Pentastar/Octastar structure); (3) solve the geometric realization on the circle guided by the target word; (4) inflate degeneracy margins, round to rational circle points, and certify exactly. search_code/ contains the complete pipeline. License Code: MIT. Data (certificates) and accompanying text: CC BY 4.0. If you use these certificates or values, please cite this deposit and arXiv:2510.22584.
Reynout Vos· Zenodo (CERN European Organi...· 0 citations
Backdoor attacks pose a critical threat to Deep Neural Networks (DNNs) by embedding hidden behaviors that are activated only under specific trigger conditions, compromising the reliability of Artificial Intelligence (AI) systems. Existing detection approaches often rely on single-method assumptions, limited data access, or controlled environments, limiting their effectiveness against adaptive, real-world attacks. To address these limitations, this study proposes a multi-layer, explainable framework for robust backdoor detection in DNNs. The approach integrates complementary detection mechanisms — activation clustering, spectral-signature analysis, Gaussian Mixture Models, entropy-based evaluation, and input perturbation — within a unified pipeline. Each layer captures distinct indicators of anomalous behavior, enabling comprehensive analysis across structural, statistical, and behavioral dimensions. An explainable component provides interpretable insights into detection decisions. Experimental evaluation on the MNIST and CIFAR-10 datasets demonstrates that the framework achieves 97–99% detection rates with false positive rates (FPRs) below 2%, while reducing attack success rates (ASRs) by over 94% across diverse trigger types. The results confirm that combining multiple detection perspectives significantly improves robustness compared with single-layer defenses. Overall, this work advances AI security by introducing a scalable, practical defense mechanism that operates under limited-knowledge conditions and supports trustworthy deployment in real-world environments.
Ahmed Aljughaiman, Abdulmohsen Saud Albesher, Abdullah Albuali et al.· Figshare· 0 citations
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Artificial intelligence is rapidly becoming embedded in everyday life through an expanding range of applications, services, and products. As its potential to improve diagnosis, personalize treatment, and enhance operational efficiency becomes increasingly evident, healthcare is undergoing profound transformation. However, trust and distrust operate as dual mechanisms shaping technology diffusion: trust facilitates adoption, whereas distrust constrains large-scale deployment. Trust therefore remains a persistent barrier to the widespread use of artificial intelligence in healthcare services. At present, empirical evidence on the pathways linking trust and acceptance of AI medical conversational agents (AIMCAs) remains limited. Grounded in trust theory, this study aimed to develop and validate a multidimensional trust-perception scale for AIMCAs, establish its dimensional structure and psychometric quality, and examine its associations with an external acceptance-related behavioral criterion. Methodologically, the study first used grounded-theory-informed abductive qualitative analysis to identify the structure of public trust perceptions of AIMCAs and generated and screened measurement items through expert Q-sorting; independent samples were then used for exploratory and confirmatory factor analyses, followed by assessments of internal consistency, test-retest reliability and absolute agreement, within-construct indicator convergence, discriminant validity, and criterion-related validity. Parallel analysis and the scree plot jointly supported a five-factor solution. Principal axis factoring with Direct Oblimin oblique rotation yielded a 15-item, five-dimensional structure, with the five common factors explaining 76.07% of the total variance. Confirmatory factor analysis further supported a five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust; model-fit indices, standardized factor loadings, latent-variable correlations, and residual diagnostics collectively provided evidence for its internal structure. The Fornell-Larcker criterion and bootstrap confidence intervals for HTMT jointly provided evidence for internal discriminant validity among the five AIMCA trust dimensions. An ordinal logit model using actual use frequency as an external behavioral criterion was statistically significant overall, likelihood-ratio χ²(5) = 71.963, p < 0.001, McFadden pseudo-R² = 0.125. Interactional trust showed the strongest association with higher use frequency (OR = 3.096, 95% CI [2.257, 4.246]). The resulting scale captures multidimensional public trust perceptions of AIMCAs and provides a structured measurement basis for research on acceptance-related behavior. The findings support a 15-item, five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust. An ordinal logit model using self-reported AIMCA use frequency as an external behavioral criterion provided additional criterion-related evidence. The scale can be used to characterize multidimensional public trust perceptions of AIMCAs and provides a structured measurement foundation for subsequent research on acceptance, use intention, continuance intention, and actual use.
Hemin Du, Wumin Ouyang, Y X Han et al.· Scientific Reports· 0 citations
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This paper introduces a novel approach to explain GNN predictions by learning symbolic representations of the graph structure. We propose a model that maps the GNN's activations to a symbolic representation, enabling the generation of human-readable explanations. This method addresses the interpretability challenge in GNNs, offering a pathway to trust and confidence in their predictions. The core claim is that by leveraging symbolic representation learning, we can transform opaque GNN behavior into understandable insights. The proposed mechanism provides a foundation for building more transparent and reliable GNN-based systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This paper introduces a novel approach to explain GNN predictions by learning symbolic representations of the graph structure. We propose a model that maps the GNN's activations to a symbolic representation, enabling the generation of human-readable explanations. This method addresses the interpretability challenge in GNNs, offering a pathway to trust and confidence in their predictions. The core claim is that by leveraging symbolic representation learning, we can transform opaque GNN behavior into understandable insights. The proposed mechanism provides a foundation for building more transparent and reliable GNN-based systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach to explainable AI (XAI) within reinforcement learning by leveraging causal reasoning. We model the environment and the agent's policy using a causal Bayesian network. By performing inference through this network, we trace the causal chain of events leading to a specific action, providing a transparent explanation. This method moves beyond simply observing the agent's behavior to understanding the underlying reasons for its choices. The core of our approach lies in identifying and representing the causal relationships within the RL system, enabling us to dissect the decision-making process and ultimately build more robust and reliable RL agents. The proposed framework offers a significant step toward interpretable RL and addresses a critical limitation of current techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP) based on continuous AI-guided decision loops integrating enterprise sensing, contextual intelligence, predictive analytics, decision intelligence, prescriptive policies, autonomous execution, learning, and governance. The proposed framework extends the classical Monitor, Analyze, Plan, and Execute model of autonomic computing by incorporating continuous prediction, intervention, evaluation, and adaptation. The study synthesizes research published between 2000 and 2022 on autonomous agents, autonomic computing, self-adaptive systems, predictive process monitoring, reinforcement learning, and prescriptive analytics. Three key studies provide the conceptual foundation: Kephart and Chess on autonomic computing, Metzger et al. on proactive process adaptation using deep learning, and Kubrak et al. on prescriptive process monitoring. The framework distinguishes operational, learning, and governance loops to support continuous enterprise adaptation. It emphasizes the transition from predicting business outcomes to selecting and executing appropriate interventions. The study also examines challenges involving causal reasoning, intervention timing, resource constraints, model drift, explainability, and human oversight. Overall, AI-guided decision loops provide a foundation for adaptive, intelligent, and governed enterprise platforms capable of continuous decision making, organizational learning, and operational optimization.
Shekar Vollem· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence—Neuro-Symbolic Logic Programming with Reinforcement Learning—designed to address the limitations of current AI techniques. The core idea is to integrate the pattern recognition capabilities of neural networks with the reasoning and explainability offered by symbolic logic programming, guided by reinforcement learning. We present a hybrid system where a neural network learns a high-level representation of a task, translating sensory inputs into abstract concepts. This representation is then fed into a symbolic logic engine, which executes predefined rules and generates plans. Reinforcement learning is utilized to optimize the neural network's representation and the logic engine's rule selection, allowing the system to adapt and improve its performance over time. This approach aims to create AI systems that are not only capable of complex behavior but also provide verifiable, logically sound explanations for their actions. The system's architecture and the interaction between its components are detailed, highlighting the potential for robust and explainable AI. We demonstrate a conceptual framework, outlining the key components and their interplay, and discuss potential future research directions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Programming students are no longer only learning to write code; they are also learning in environments where AI tools can explain, debug, and generate code alongside them. This shift creates a tension for programming education: the same tools that can make learning more accessible may also encourage dependence when students use them as substitutes for their own reasoning. Using a conceptual and narrative review of recent literature, this paper examines student dependence on generative AI tools in programming education. Central to this review is an examination of learning outcomes, independent programming ability, self-regulated learning, critical thinking, problem solving, learner characteristics, and instructional design in AI-supported programming environments. Students learning to code are increasingly using AI tools to answer questions, explain concepts, help debug, and make information easier to access, but their use can also create problems. Students who rely heavily on them, especially when instructors provide little instruction, may spend less time thinking through problems on their own or reflecting on their solutions. From the literature, we see that the impact of AI is more dependent on the learner, the learning environment, and the use of the tools than on the technology. Existing studies also have important weaknesses, including heavy use of self-reported measures, small sample sizes, correlational research, and limited evidence about what sustained AI use could mean for independent thinking, problem solving, and programming development over time. More data is needed to understand these longer-term effects.
Kazeem Babatunde Abioye· Journal of Education Learnin...· 0 citations
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate Are the conclusions supported by the data? Somewhat supported Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly Is the preprint likely to advance academic knowledge? Moderately likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, but it needs to be improved Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Yuchen Song· Zenodo (CERN European Organi...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
