The transition to a new technological paradigm is considered through a combination of large-scale digital transformation and the institutionalization of the development of artificial intelligence. The focus is not on individual technologies, but on the formation of a data economy in which control over the infrastructure for collecting, storing and processing information becomes the source of growth and power. It shows how national strategies and projects on AI and digital transformation consolidate a shift in priorities: from the modernization of industries to the construction of sovereign contours of data, computing power and national AI models.Against this background, the mechanisms of global and technological inequality within the country are analyzed due to asymmetric access to digital infrastructure, competencies and markets, as well as the redistribution of resources in favor of actors controlling key platforms and algorithmic systems. Special attention is paid to the use of artificial intelligence as a tool of social control, from the introduction of algorithmic management in public administration to monitoring, profiling and selective access to services and information.In the final perspective, artificial intelligence, being closely linked to the digital infrastructure, acts not as a set of applied solutions, but as a new configuration of power. The defining resource is the ability to create, accumulate, and interpret data. In practice, this is reflected in the redistribution of centers of influence, changing regulatory priorities and consolidating the advantages of those actors who control information flows and manage algorithmic systems.

Many formal security criteria in fields like secure compilation (e.g. full abstraction and robust hyperproperty preservation), cryptography (e.g. security games, reduction proofs, and universal composability), and information flow control share a fundamental underlying structure. This underlying structure and related concepts also appear in broader contexts like denotational semantics and computational expressiveness, yet their connections are often overlooked. This paper identifies and systematizes this structure through robust properties (criteria holding in any context), robust abstractions (ensuring preservation of robust properties from abstract to concrete systems) and the new notion of non-degrading abstractions, which guarantees that the concrete system only exhibits concrete violations that are also present in the corresponding abstract system, thereby ensuring no new violations are introduced by the abstraction. By unifying concepts from different areas into a common framework, we highlight existing but underexplored connections, provide new insights and general results, and introduce a valuable perspective for understanding and advancing formal security.

Beyond structural bias: Improving circRNA-disease association prediction with Multi-Hop Neighborhood Hierarchical Fusion.

The development of the Sokoria Geothermal Power Plant (GPP) in Sokoria Village, East Ndona Subdistrict, Ende Regency is part of Indonesia’s national renewable energy policy. However, this project is not only shaped by technical and economic considerations but also strongly influenced by local socio-political dynamics, particularly the interest relations of local elites. This study aims to analyze the forms of local elite interest relations in the development of the Sokoria GPP, the political, economic, and social interests underlying their involvement, and the impacts on local communities. This research employs a descriptive qualitative approach using in-depth interviews, field observations, and documentation as data collection methods. Data analysis is conducted using C. Wright Mills’ elite theory. The findings reveal that local elites play a dominant role in the decision-making process of the Sokoria GPP development, acting as intermediaries between developers and communities while also controlling information flows and benefit distribution. These interest relations tend to be elitist rather than participatory, resulting in unequal benefit distribution and community resistance. This study concludes that the success of geothermal development depends not only on technical and policy aspects but also on inclusive, transparent, and just socio-political governance.

The article presents an in‑depth examination of the theoretical, methodological, and institutional foundations of protecting the cognitive space under conditions of hybrid threats, intensified semantic influence, and rapidly evolving information environments. Attention is devoted to conceptualizing the architecture of a multi‑level cognitive security system that integrates humanitarian, communicative, and analytical‑technological dimensions. These components are analyzed as interdependent elements that collectively shape societal resilience, influence public trust, and determine the capacity of democratic institutions to withstand manipulative and destabilizing cognitive impacts. The study provides a comprehensive assessment of the interaction among public authorities responsible for strategic communications, counter‑disinformation, and information security. It identifies structural and functional gaps that reduce the coherence of governmental responses to semantic threats and hinder the development of an integrated cognitive security framework. The analysis demonstrates that contemporary public policy must evolve from a paradigm of controlling information flows toward a model of moderating a resilience‑oriented communicative environment. Such a shift requires narrative coherence, transparent communication practices, and the cultivation of public trust as a strategic resource of national security. A conceptual model of the state strategy for protecting the cognitive space is proposed, grounded in the integration of humanitarian, communicative, and technological instruments into a unified cognitive contour. The model emphasizes the transition from fragmented, reactive measures to protocol‑based, anticipatory governance, thereby reducing strategic uncertainty and enhancing institutional adaptability. The article highlights the critical role of strategic communications in safeguarding information sovereignty, strengthening democratic stability, and fostering long‑term societal resilience. Furthermore, the study outlines priority directions for improving state policy, including the epistemic integration of analytical units, the development of a national infrastructure for monitoring cognitive risks, and the enhancement of dromological (speed‑related) stability of public institutions operating in a dynamic semantic environment. These measures are presented as essential prerequisites for constructing a coherent and future‑oriented system of cognitive security. Keywords: cognitive space; semantic threats; strategic communications; information sovereignty; resilience; public administration architecture; cognitive security; analytical‑technological contour.

In the early stages of rumor dissemination, accurately locating the source of transmission is crucial for the management and control of information flow. However, the inherent uncertainty in information dissemination complicates precise source localization. Although incorporating transmission direction can alleviate some of this uncertainty, thereby facilitating source localization, traditional methods still depend on the often inaccurate informed timestamps of observed nodes. To address this limitation, this paper develops a method to infer all rumor sources using a single snapshot of observed nodes at a specific time and the direction of transmission toward these nodes, without requiring prior knowledge of the informed timestamps. First, we conduct a theoretical analysis demonstrating how the network structure can be pruned based on the status and transmission direction of the observers. Subsequently, we propose the Reduce Candidate Source algorithm, which operates within the pruned subgraph to identify the node with the minimum sum of shortest paths to the informed observers as the potential source of propagation. Additionally, we propose the Reduce Candidate Source by Deleting All algorithm for propagation models characterized by high certainty, retaining only the informed observers and those nodes indicating the transmission direction to further narrow the candidate source range. Finally, extensive experiments confirm that our two proposed methods align with the theoretical analysis, effectively identifying the sources of rumor dissemination in the early stages, even under conditions where the propagation model and informed are unknown.

INFFLOW-RT: A real-rime, adaptive methodology for risk-based information flow control in IIoT

Enterprise identity management and privacy infrastructure have emerged as foundational elements for compliance-driven distributed systems operating under stringent data protection regulations. Modern digital platforms face unprecedented challenges when antitrust authorities integrate data protection principles into competition law enforcement, requiring comprehensive reforms to cross-platform data processing practices. Compliance infrastructure addresses these challenges through metadata-driven enforcement mechanisms that label data assets with sensitivity classifications and permitted usage contexts. Privacy solutions implement logical segmentation boundaries mapping data lineages and controlling information flows between sources and sinks. Machine learning models automate data classification by analyzing schemas, content patterns, and usage contexts to assign appropriate privacy labels. Anomaly detection algorithms continuously monitor data flows for unexpected patterns indicating potential policy violations or system misconfigurations. Federated engineering coordination distributes compliance responsibilities across organizational teams through cross-functional pods aligned with policy domains. Automated tooling simplifies integration complexity through simplified APIs, template-based policy definitions, and continuous validation frameworks. Applications extend to artificial intelligence systems where privacy checks validate training data sources against usage policies before model development begins. The architectural patterns establish replicable frameworks for organizations navigating complex regulatory landscapes while maintaining innovation capacity. Extensible designs accommodate jurisdiction-specific requirements without fundamental redesign, supporting multinational operations under diverse regulatory regimes through flexible configuration management.

The increasing reliance on cloud storage has highlighted significant security concerns, particularly in data confidentiality and unauthorised access. Traditional encryption methods, such as the Elgamal cryptosystem, are insufficient to address the evolving security demands and the need for integrated access control. This paper suggests a new method that uses proxy re-encryption, access control and enhanced encryption to increase the security of cloud storage. It is divided into the following three major stages: (1) Secured Cloud storage (2) Access Control and (3) Proxy re-encryption. Secure cloud storage is where data is stored, and it’s critical to guard against damaging external attacks as well as internal data leaks. The data will be encrypted to lessen this issue. An enhanced Elgamal technique is used to safely store the customers’ data on the cloud. The central authority is in charge of gaining access to the data that is kept in the cloud. To access the data through an obfuscation process, an enhanced information flow control system is suggested. This procedure applies additional encryption utilising the Improved Light Weight Key Management (ILWKM) technology, which encrypts the data by adopting a session key through a tent map. Initially, the user and the central authority authenticate each other. Only the authenticated user will have access to the cloud’s data when a proxy key is used. The proxy re-encryption process uses the session key as a proxy key. The proxy uses the session key to re-encrypt the data before sending it to the authorised user. The user uses an inverted tent map to decrypt the data. Finally, the performance of the improved Elgamal approach is evaluated over conventional approaches with regard to different types of attacks. The improved Elgamal scheme achieves lesser ratings of 0.024 in CCA attacks as well as achieved the shortest encryption time, at just 0.0001 s when compared to other traditional schemes.

The metaverse, a virtual world that simulates reality, is developing quickly and is about to become widely incorporated into human existence in a number of areas, including healthcare, education, and transportation. Utilizing the core principles of traditional classroom training, its online counterpart offers increased flexibility, accessibility, inclusivity, and cost-efficiency. Advancements in technology and educational tools automate data collection, enabling precise assessment of knowledge, tailored learning experiences, and targeted faculty interventions to accommodate diverse learner needs. The onset of global technological advancements accelerated the adoption of virtual learning solutions, prompting a significant transformation in teaching methods. This paper proposes a novel avatar-authenticated metaverse environment, leveraging cloud computing benefits and incorporating techniques such as Improved Key Management (IKM), Enhanced Information Flow Control (EIFC), with an obfuscation process. The EIFC method, coupled with obfuscation, introduces the Red Fox-Adapted Tuna Swarm Optimization algorithm (RFATSO) to optimally choose an improved Blowfish key from a series of sub-keys in the improved Blowfish algorithm. Experimental evaluation against established techniques showcases the effectiveness of the proposed approach in revolutionizing educational practices.

Enhancing the robustness of cyber–physical power systems: From the perspective of information flow control

Smart contract vulnerability detection has attracted increasing attention due to billions of economic losses caused by vulnerabilities. Existing smart contract vulnerability detection methods have high false negative and high false positive rates. To address these issues, we present ByteEye, a bytecode level smart contract vulnerability detection framework with Graph Neural Networks (GNNs). ByteEye first constructs an edge-enhanced Control Flow Graph (CFG) to maintain rich information from the low-level bytecode with low latency. ByteEye also designs and incorporates both general information and vulnerability-specific information into its detection method as bytecode level features. Furthermore, ByteEye flexibly supports machine/deep learning models, especially with graph neural networks, which can facilitate vulnerability detection precisely. The extensive experimental results highlight that ByteEye outperforms the state-of-the-art approaches on all three types of vulnerability detection. ByteEye can achieve an average of 35.29%, 43.95%, and 6.38% higher on F1 than the bytecode level best-performed baseline on reentrancy vulnerability, timestamp dependency vulnerability, and integer overflow/underflow vulnerability, respectively. Moreover, ByteEye can detect 361 new vulnerabilities in real-world smart contracts, which are reported for the first time. ByteEye enhances control flow information, designs general bytecode-level features with expert knowledge, and flexibly supports deep learning models, particularly GNNs, thus achieving high detection effectiveness.

The End–Edge–Cloud (EEC) paradigm hierarchically orchestrates Internet of Things (IoT) devices, edge nodes, and cloud, optimizing system performance for both delay-sensitive data and compute-intensive processing tasks. Securing IoT data sharing in the EEC-driven paradigm while maintaining data traceability poses critical challenges. In this paper we propose STDSM, a symmetry-enhanced secure and traceable data sharing model for the EEC-driven data sharing paradigm. STDSM enables IoT data owners to share data securely by attaching symmetric security labels (for secrecy and integrity) to their data. This mechanism symmetrically controls both data outflow and inflow. Furthermore, STDSM can also track data user identity. Subsequently, the security properties of STDSM, including data confidentiality, integrity, and identity traceability, are formally verified; the verification takes 280 ms, using a novel approach that combines High-Level Petri Net modeling with the satisfiability modulo theories library and the Z3 solver. In addition, our experimental results show that STDSM reduces time overhead by up to 15% while providing enhanced traceability.

Confidential computing (CC), designed for security-critical scenarios, uses remote attestation to guarantee code integrity on cloud servers. However, CC alone cannot provide assurance of high-level security properties (e.g., no data leak) on the code. In this paper, we introduce a novel framework, Agora, scrupulously designed to provide a trustworthy and open verification platform for CC. To prompt trustworthiness, we observe that certain verification tasks can be delegated to untrusted entities, while the corresponding (smaller) validators are securely housed within the trusted computing base (TCB). Moreover, through a novel blockchain-based bounty task manager, it also utilizes crowdsourcing to remove trust in complex theorem provers. These synergistic techniques successfully ameliorate the TCB size burden associated with two procedures: binary analysis and theorem proving. To prompt openness, Agora supports a versatile assertion language that allows verification of various security policies. Moreover, the design of Agora enables untrusted parties to participate in any complex processes out of Agora’s TCB. By implementing verification workflows for software-based fault isolation, information flow control, and side-channel mitigation policies, our evaluation demonstrates the efficacy of Agora.

A dynamic information flow control framework for hyperchaos in complex-variable systems and FPGA-based applications

Political movements are often bound together by mobilizing narratives about social threat. In devoted activists, this triggers moral motivations to protect the narrative from criticism and nuance. Speech repression phenomena include public shaming on social media, the "deplatforming" and "canceling" of controversial speakers, and the intimidation of dissidents. Speech repression phenomena are most puzzling when the narratives activists try to protect are simplistic and inaccurate, which is often the case in politics. Here, I argue that speech repression derives from at least three main sociocognitive motivations. First, hypersensitive dispositions to detect threat, from hostile outgroups in particular. Second, motivations to try to keep people committed to moral causes and mobilized against dangerous groups by controlling information flows and beliefs. Third, motivations to signal personal devotion to causes and ingroups to gain status. Members of most political groups engage in speech repression, even those ostensibly committed to freedom. Political activists and leaders only need to believe that speech restriction will bring about desired effects to engage in it. While speech repression can derive from sincere convictions, insincere self-censorship and sanctioning are widespread.

Software defect prediction (SDP) plays a pivotal role in ensuring high-quality software development by aiding in the early identification of potential defects. This practice has gained substantial attention in the field of software engineering over the years. Recent advancements in deep learning have primarily focused on extracting general syntactic features from abstract syntax trees (ASTs) for SDP. However, AST-based neural network models might overlook important structural information related to control flows embedded within the source code. Given that software defects are often influenced by control flow patterns, this article proposes a novel SDP approach called control flow graph and graph attention (CFG2AT) network-based SDP. CFG2AT is specifically designed to automatically identify software defects and contains a graph-structured attention unit to effectively capture control flow information. To evaluate the effectiveness of CFG2AT, we carried out extensive experiments using data from 15 versions of six different open-source software projects under both within-project and cross-project defect prediction settings. Experimental results demonstrate that our proposed CFG2AT approach generally outperforms a range of competing methods for defect prediction. The improvement is 7.09%–12.80% in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i>, 1.30%–4.15% in area under curve (AUC), and 6.78%–17.54% in Matthews correlation coefficient (MCC) under within-project defect prediction, and 23.76%–44.79% in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i>, 8.93%–13.27% in AUC, and 36.92%–94.89% in MCC under CPDP, respectively.

Modulation of synaptic transmission in the deep cerebellar nuclei, a major output region of the cerebellum, is essential for regulating motor and non-motor functions by controlling information flow from the cerebellar cortex. In this study, we aimed to investigate the effects of dopamine (DA) and noradrenaline (NA) on glutamatergic synaptic transmission using cerebellar slices from both male and female Wistar rats. Stimulation-evoked excitatory postsynaptic currents (eEPSCs) were recorded from deep cerebellar nuclei neurons using whole-cell patch-clamp technique. Bath application of DA or NA decreased the eEPSC amplitude. Pharmacological analysis revealed presynaptic D2-like receptors (D2R) and α2-adrenergic receptors (α2-AdR) as mediators of the inhibitory effects induced by DA and NA, respectively. While DA decreased eEPSC amplitude in all tested synapses, the selective D2R agonist, quinpirole showed no effect in approximately 30% of synapses. By contrast, NA and α2-AdR-selective agonists (clonidine and dexmedetomidine) inhibited synaptic transmission in all tested synapses. Notably, both DA and NA maintained their inhibitory effects even when their respective receptor antagonists (sulpiride and RS79948), were present. This observation suggests cross-receptor interactions: DA acted through α2-AdRs, while NA operated via D2Rs. These findings reveal novel cross-talk of catecholamines within cerebellar networks, providing new insights into mechanisms underlying synaptic modulation.

The rapid development of social networks has significantly accelerated information dissemination, but it has also intensified the risk of large-scale rumor propagation in areas of public concern, such as healthcare. To address this issue, this paper proposes an intelligent rumor governance system that integrates text recognition, diffusion simulation, and reinforcement learning control. The system constructs a dataset containing factual information and potential rumors, and employs TF-IDF features and a Random Forest model to achieve high-accuracy rumor detection. For information diffusion modeling, the system simulates rumor spread on an ErdsRnyi network and designs a Q-learning-based controller to intervene in the process. Experimental results show that the Random Forest-based rumor detection model achieves an accuracy of over 85%, demonstrating strong classification performance. The reinforcement learning control strategy effectively curbs information diffusion, reducing the number of infected nodes by 40%60%, and achieves the best overall performance among multiple strategies, particularly in balancing containment effectiveness, implementation cost, and user experience, highlighting its strong potential for real-world applications.

The article proposes to consider information security as a threecomponent object, which includes the protection of useful information from external influence, the availability of reliable information to ensure the normal functioning of state institutions and the life of citizens, and the protection of a person from aggressive or destructive information impact. The main attention is paid to the last aspect. There are two mechanisms of protection: a personal information filter based on the correct understanding of the value hierarchy and forced restriction of information flows by the state. Neither mechanism is currently fully operational: the subjective value hierarchy of a large number of people does not correspond to the objective picture of the world, and the state has not yet learned to either control or limit information flows. The information environment in Russia is very heterogeneous, a huge amount of destructive information is in the public domain. The possibility of generating and disseminating such information is almost unlimited. But awareness of the problem has already appeared and attempts to build information barriers both in the direction of educating the correct moral guidelines and in the direction of control and regulation of information flows in Russia are being undertaken.