Search Results (120)

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article

This paper develops a belief-based dynamic optimisation framework to explain career continuation decisions in settings characterised by monopsonistic employment and asymmetric performance evaluation. Extending Holmström’s career concerns model, we consider agents who must decide whether to continue or exit their vocation based on subjective beliefs updated from noisy signals. Unlike the original framework, our model assumes a single institutional employer and limited feedback transparency, turning the agent’s decision into an optimal stopping problem governed by evolving belief thresholds. Analytical results demonstrate how greater signal noise, higher effort costs, and more attractive outside options raise the probability of exit. To validate the framework, we confront belief-based dropout decisions using original survey data from over 8000 football referees in Europe, showing that threats, unmet development expectations, and perceived stagnation significantly predict dropout. The results offer practical insights for institutions, such as sports federations, academic bodies, and civil services, on how to improve retention through increased transparency and better support structures. This study contributes to the literature by integrating optimal stopping theory and dynamic labor models in a novel context of constrained career environments. Full article

In Italy, several changes to academic and professional standards and rules in kinesiology and sport have recently occurred. On the university side, no data collection has started regarding these changes and effects on specific scholars. The aim of this study was to evaluate the opinions of Italian university scholars in Exercise and Sport Sciences regarding recent disciplinary reclassifications, the emergence of the kinesiologist as a formal profession, and related curricular updates. Specifically, this study aimed to measure scholars’ views on the usefulness of unification, hybridization with other fields of knowledge, interdisciplinarity with pedagogy, the distinctiveness of undergraduate education in light of the new kinesiologist profile, and the inclusion of Technical and Laboratory Activities (TLA) credited through the European Credit Transfer System (ECTS). These aspects were explored through an eight-question survey offering three multiple-choice answers. An exploratory survey was distributed to a defined population of 261 Italian scholars (48 full professors, 137 associate professors, and 76 researchers). A total of 83 responses were collected: 14 full professors, 45 associate professors, and 24 researchers (response rate: 31.8%). Descriptive statistics and inferential analyses (Chi-Square tests, Cramér’s V, and Pearson/Spearman correlations) were conducted. Results indicated that 72.3% perceived overlap between pedagogical and medical disciplinary groups, and 85.5% considered practical/laboratory activities essential to the kinesiologist’s role. Significant differences in keyword-sharing perceptions across academic ranks emerged (p = 0.012; V = 0.3), and a near-significant trend was found regarding the importance of discipline-aligned research (p = 0.058; V = 0.3). Full agreement was found on the use of updated scientific evidence in lectures (100%), and 81.9% supported standardized education for the kinesiologist profession (Q6). Positive correlations were observed between support for keyword sharing and belief in its usefulness for promoting interdisciplinarity among full professors (r = 0.58, p = 0.02), associate professors (r = 0.68, p < 0.01), and researchers (r = 0.83, p < 0.01). Conversely, negative correlations emerged between the importance placed on practical activities and support for interdisciplinarity among associate professors and researchers, with values ranging from r = −0.31 to −0.46. The results are significant and tended toward autonomy from pedagogy, training aligned with the bachelor’s and master’s degree kinesiologist, and interdisciplinarity inherent in typical Exercise and Sport Sciences (ESS) keywords. This study should be replicated to increase the sample and to expand the ad hoc questionnaire to other issues. These findings highlight the need for greater alignment between academic training, disciplinary definitions, and professional practice through shared epistemological frameworks and updated descriptors that reflect scientific and labor market developments. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Accessibility in web systems is essential to ensure everyone can obtain information equally. Based on the Web Content Accessibility Guidelines (WCAGs), the Electronic Government Accessibility Model (eMAG) was established in Brazil to guide the accessibility of federal government web systems. Based on these guidelines, this research sought to understand the reasons behind the persistent gaps in web accessibility in Brazil, even after 20 years of eMAG. To this end, the accessibility of the Integrated Academic Activities Management System (SIGAA), used by 39 higher education institutions in Brazil, was evaluated. The living lab methodology was used to carry out accessibility and usability tests based on students’ experiences with visual impairments during interaction with the system. Furthermore, IT professionals’ knowledge of eMAG/WCAG guidelines, the use of accessibility tools, and their beliefs about accessible systems were investigated through an online questionnaire. Additionally, the syllabuses of training courses for IT professionals at 20 universities were analyzed through document analysis. The research confirmed non-compliance with the guidelines in the software researched, gaps in the knowledge of IT professionals regarding software accessibility practices, and inadequacy of accessibility content within training courses. It is concluded, therefore, that universities should incorporate mandatory courses related to software accessibility into the training programs for IT professionals and that organizations should provide continuous training for IT professionals in software accessibility practices. Furthermore, the current accessibility legislation should be updated, and its compliance should be required within all organizations, whether public or private. Full article

Introduction. Mesotherapy is a widely used technique around the world. However, there is currently no internationally recognized, evidence-based standard for its various clinical applications. To address this gap, we have reviewed the current state of the art, critically evaluated its clinical benefits and limitations, and proposed a set of standards including procedural steps, recommended actions, and practical instructions in the form of protocols, guidelines, and expert recommendations. Methods. A team of researchers conducted a comprehensive literature review, selecting studies published between 1976 and 2023. Drawing on the available evidence and the needs expressed by patient associations, 23 clinical questions were developed and presented to a panel of experts. Through multiple rounds of evaluation, evidence-based recommendations were formulated and subsequently submitted for structured evaluation and voting by a broad, multidisciplinary panel of international experts, representing numerous national and international scientific societies. Results. The recommendations outlined in this guideline support the use of mesotherapy across diverse clinical and organizational settings, providing a standardized framework that ensures both efficacy and patient safety in osteoarticular pain, rehabilitation, and dermatological fields. Conclusions. The era of mesotherapy based on personal beliefs now gives way to evidence-based practice. The limitations underscore the need for continued high-quality research and scheduled guideline updates. Full article

Striking belief distortions may accompany various disorders of awareness that are predominantly associated with right hemispheric cerebral dysfunction. Distortions may range on a continuum of pathological severity, from the unawareness of paralysis in anosognosia for hemiplegia, to a more startling disturbance in denial of paralysis where belief may starkly conflict with reality. The patients’ beliefs about their limitations typically represent attempts to make sense of limitations or to impart meaning to incongruous facts. These beliefs are often couched in recollections from past memories or previous experience, and are hard to modify even given new information. Various explanations of unawareness have been suggested, including sensory, cognitive, monitoring and feedback operations, feedforward mechanisms, disconnection theories, and hemispheric asymmetry hypotheses, along with psychological denial, to account for the curious lack of awareness in anosognosia and other awareness disorders. This paper addresses these varying explanations of the puzzling beliefs regarding hemiparesis in anosognosia. Furthermore, using the multi-dimensional nature of unawareness in anosognosia as a model, some startling belief distortions in other right-hemisphere associated clinical syndromes are also explored. Other neurobehavioral disturbances, though perhaps less common, reflect marked psychopathological distortions. Startling disorders of belief are notable in somatic illusions, non-recognition or delusional misattribution of limb ownership (asomatognosia, somatoparaphrenia), or delusional identity (Capgras syndrome) and misidentification phenomena. Difficulty in updating beliefs as a source of unawareness in anosognosia and other awareness disorders has been proposed. Processes of belief development are considered to be patterns of thought, memories, and experience, which coalesce in a sense of the bodily and personal self. A common consequence of such disorders seems to be an altered representation of the self, self-parts, or the external world. Astonishing nonveridical beliefs about the body, about space, or about the self, continue to invite exploration and to stimulate fascination. Full article

Belief change is a core component of intelligent reasoning, enabling agents to adapt their beliefs in response to new information. A prominent form of belief change is belief revision, which involves altering an agent’s beliefs about a static (unchanging) world in light of new evidence. A foundational framework for modeling rational belief revision was introduced by Alchourrón, Gärdenfors, and Makinson (AGM), who formalized revision functions based on total preorders over possible worlds—that is, orderings that encode the relative plausibility of alternative states of affairs. Building on this, Peppas and Williams later characterized AGM-style revision functions using weaker preference structures known as semiorders, which, unlike total preorders, permit intransitive indifference between alternatives. In this article, we extend the framework of Peppas and Williams to the context of belief update. In contrast to belief revision, belief update concerns maintaining coherent beliefs in response to actual changes in a dynamic, evolving environment. We provide both axiomatic and semantic characterizations of update functions derived from semiorders, establishing corresponding representation theorems. These results essentially generalize the classical belief-update framework of Katsuno and Mendelzon, which relies on total preorders, thereby offering a broader and more flexible perspective. The intransitivity of indifference inherent in semiorders plays a central role in our framework, enabling the representation of nuanced plausibility distinctions between possible states of affairs—an essential feature for realistically modeling belief dynamics. Full article

Religion played a pivotal role in shaping the political and cultural landscape of early China, particularly through the practice of relocating capitals (遷都). The relocation of capitals is an outstanding theme in early Chinese historiography, setting it apart from many other world traditions. In particular, this practice contrasts sharply with the early Mediterranean context, where the city of Rome transitioned from a modest city-state to a world empire and was celebrated as the “eternal city.” By contrast, early Chinese capitals were deliberately transient, their impermanence rooted in strong religious sentiments and pragmatic considerations. Religious and ideological justifications were central to these relocations. The relocation was not merely a logistical or political exercise; it was imbued with symbolic meaning that reinforced the ruler’s legitimacy and divine mandate. Equally important was the way rulers communicated these decisions to the populace. The ability to garner mass support for such monumental undertakings reveals the intricate relationship between political authority and religious practice in early China. These critical moments of migration offer profound insights into the evolving religious landscape of early China, shedding light on how religion shaped early governance and public persuasion. “Capital relocation” served as a means to rearticulate belief, reaffirm the centrality of worship, and restore faith in the ruling order. Drawing on recent archeological discoveries and updated textual and inscriptional scholarship related to the events of Pan Geng and the Zhou relocation to Luoyi, this article re-examines the motif of “capital relocation” as both a historical and historiographical phenomenon unique to early China. Full article

Bayesian nonparametric methods, particularly the Dirichlet process (DP), have gained increasing popularity in both theoretical and applied research, driven by advances in computing power. Traditional Bayesian estimation, which often relies on Gaussian priors, struggles to dynamically integrate evolving prior beliefs into the posterior distribution for decision-making in finance. This study addresses that limitation by modeling daily security price fluctuations using a Dirichlet process mixture (DPM) model. Our results demonstrate the DPM’s effectiveness in identifying the optimal number of clusters within time series data, leading to more accurate density estimation. Unlike kernel methods, the DPM continuously updates the prior density based on observed data, enabling it to better capture the dynamic nature of security prices. This adaptive feature positions the DPM as a superior estimation technique for time series data with complex, multimodal distributions. Full article

This paper develops a novel probabilistic theory of belief formation in social networks, departing from classical opinion dynamics models in both interpretation and structure. Rather than treating agent states as abstract scalar opinions, we model them as belief-adoption probabilities with clear decision-theoretic meaning. Our approach replaces iterative update rules with a fixed-point formulation that reflects rapid local convergence within social neighborhoods, followed by slower global diffusion. We derive a matrix logistic equation describing uncorrelated belief propagation and analyze its solutions in terms of mean learning time (MLT), enabling us to distinguish between fast local consensus and structurally delayed global agreement. In contrast to memory-driven models, where convergence is slow and unbounded, uncorrelated influence produces finite, quantifiable belief shifts. Our results yield closed-form theorems on propaganda efficiency, saturation depth in hierarchical trees, and structural limits of ideological manipulation. By combining probabilistic semantics, nonlinear dynamics, and network topology, this framework provides a rigorous and expressive model for understanding belief diffusion, opinion cascades, and the temporal structure of social conformity under modern influence regimes. Full article

Background/Objectives: Obsessive–compulsive disorder (OCD) is a complex psychiatric condition often associated with alterations in cognitive processes, including memory. Although memory dysfunction has been proposed as a contributing factor to the onset and maintenance of OCD symptoms, it remains debated whether these deficits reflect genuine cognitive impairments or maladaptive metacognitive processes, such as pathological doubt and memory distrust. This review aims to synthesize current findings on memory functioning in OCD, focusing on distinct memory systems and the role of metacognition. Methods: A comprehensive literature search was conducted across five databases (PubMed, Scopus, Embase, PsycINFO, and Google Scholar), covering studies up to April 2025. Search terms included “Obsessive-compulsive disorder”; “OCD”; “Memory dysfunction”; “Episodic memory”; “Working memory impairment”; “Prospective memory deficits”; “Checking compulsions”; “Memory confidence”; “Cognitive biases”. Results: Short-term memory appears generally preserved in OCD. Working memory deficits are consistently reported, especially in the visuospatial domain, and they are associated with difficulties in updating and clearing irrelevant information. Episodic memory impairments are common and often linked to inefficient encoding strategies and heightened cognitive self-consciousness. Prospective memory is frequently compromised under neutral conditions. Individuals with checking symptoms tend to show intact objective memory performance, despite reporting low memory confidence, supporting the concept of memory distrust. Conclusions: Memory dysfunction in OCD is multifaceted, involving both cognitive and metacognitive alterations. The evidence supports a model in which executive dysfunctions and memory-related beliefs contribute to compulsive behaviors more than objective memory failure. These insights highlight the need for integrative assessment protocols and personalized interventions targeting both cognitive performance and metacognitive appraisals. Full article

The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, real-time decision-making in uncertain environments. Due to dynamic and unpredictable surroundings, autonomous navigation is highly challenged in uncertainty quantification and adaptive parameter tuning. By leveraging PGMs, the framework can first determine probabilistic dependencies between critical variables, i.e., nodes and edges, such as vehicle speed, obstacle proximity, and environmental factors, to create a robust foundation for situational awareness. Then, Bayesian inference is obtained using MCMC: the system updates its real-time beliefs as new sensor data become available. The inference layer allows adaptation to unexpected obstacles by revising trajectories or controlling a vehicle’s speed while improving safety and reliability. Finally, Bayesian optimization fine-tunes key parameters within the system, such as sensor thresholds and control variables, maximizing efficiency without exhaustive manual tuning of these parameters. Using a multi-sensor data source with images, LiDAR, radar, and annotated environmental features, the Lyft Level 5 Perception Dataset tested real-world navigation scenarios against the framework. This proposed framework’s accuracy was around 99.01% and signified good decision-making capabilities for an autonomous vehicle navigating through complex environments with reliable performance. The autonomous vehicle system is also intended to provide improved safety and flexibility in complex environments, promising the development of more resilient and dependable AI-driven solutions for navigation. Full article

In the oil and gas IIoT environment, fire detection systems heavily depend on fire sensor data, which can be prone to inaccuracies due to faulty or unreliable sensors. These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire predictions, posing significant safety and operational risks in the oil and gas industrial IoT environment. This paper presents an approach for handling faulty sensors in edge servers within an IIoT environment to enhance the reliability and accuracy of fire prediction through multi-sensor fusion preprocessing, machine learning (ML)-driven probabilistic model adjustment, and uncertainty handling. First, a real-time anomaly detection and statistical assessment mechanism is employed to preprocess sensor data, filtering out faulty readings and normalizing data from multiple sensor types using dynamic thresholding, which adapts to sensor behavior in real-time. The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. A belief mass assignment mechanism is introduced, giving more weight to reliable sensors to ensure they have a stronger influence on fire detection. Simultaneously, a dynamic belief update strategy continuously adjusts sensor trust levels, reducing the impact of faulty readings over time. Additionally, uncertainty measurements using Hellinger and Deng entropy, along with Dempster–Shafer Theory, enable the integration of conflicting sensor inputs and enhance decision-making in fire detection. This approach improves decision-making by managing sensor discrepancies and provides a reliable solution for real-time fire predictions, even in the presence of faulty sensor readings, thereby mitigating the fire risks in IIoT environments. Full article

Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement. Full article