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        <title>Frontiers in Computer Science | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science</link>
        <description>RSS Feed for Frontiers in Computer Science | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-11-15T15:42:09.987+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1953285</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1953285</link>
        <title><![CDATA[A hybrid deep learning and ensemble framework for thermal image-based metal detection and classification system]]></title>
        <pubdate>2026-11-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siva Rajesh Chiluveru</author><author>Usha Kumari</author><author>Surfarazhussain S. Halkarni</author><author>Shuvendu Rana</author>
        <description><![CDATA[Accurate metal identification and classification is essential for industrial inspection, material sorting, and quality control applications. Conventional metal identification and classification techniques typically rely on laboratory-based analytical methods that require expensive equipment, manual intervention, and significant processing time, thereby limiting their scalability and real-time applicability. To overcome these challenges, this study proposes a novel thermal image–based metal identification and classification framework integrating automated temperature extraction with hybrid deep learning models. Thermal images of different metals were first captured under controlled experimental conditions. Subsequently, temperature values for each metal category were recorded at fixed time intervals using the proposed automated temperature extraction algorithm. The thermal behavior of metals is analyzed by considering their heat conductivity and specific heat characteristics. Based on the collected temperature data, a dedicated and structured dataset was created to facilitate accurate metal classification. For classification, a hybrid architecture combining TabNet representation learning, Autoencoder-based feature extraction, and XGBoost classification was implemented to enhance discriminative capability. Additionally, a hybrid framework combining of Deep Neural Network and Random Forest hybrid model. Experimental results demonstrate that, the two proposed hybrid models achieved high classification accuracy of 99%, outperforming traditional machine learning approaches. The stable convergence behavior and minimal generalization gap further confirm the robustness and reliability of the framework. Overall, the proposed system offers a fast, low-cost, and accurate solution for automated thermal image–based metal identification and classification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1948353</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1948353</link>
        <title><![CDATA[Traffic density-aware multi-objective edge server deployment for vehicular edge computing]]></title>
        <pubdate>2026-11-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Atla Lakshmi Parvathi</author><author>Satish Anamalamudi</author><author>Anil Carie Chettupally</author><author>Murali Krishna Enduri</author>
        <description><![CDATA[Vehicular edge computing (VEC) has been introduced as a promising paradigm to support latency-sensitive and computation-intensive vehicular applications. However, the dynamic mobility of vehicles and uneven traffic distribution bring great challenges in efficient edge server deployment. Inappropriate positioning of the edge servers may increase the communication latency, energy consumption, service interruptions, and handover frequency and thus degrade the overall network performance. To address these challenges, this paper proposes a traffic density-aware multi-objective edge server deployment framework for VEC environments based on the traffic density-aware non-dominated sorting genetic algorithm II (TDA-NSGA-II). The proposed framework exploits realistic simulation of urban mobility (SUMO)-generated vehicular mobility traces to identify high-density traffic regions and optimize edge server locations accordingly. The optimization process jointly minimizes communication latency and energy consumption while improving coverage and reducing handovers. In addition, the proposed scheme implements mobility-aware vehicle-to-server association to provide stable connectivity under dynamic vehicular movement. Experimental evaluations are conducted based on realistic traffic mobility scenarios and compared with conventional optimization approaches such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Simulation results presented in this paper demonstrate that the proposed TDA-NSGA-II framework achieves substantially lower latency and energy consumption than GA, PSO and ACO, with coverage comparable to the best baseline, while handover counts remain comparable rather than consistently reduced. The results illustrate that traffic-density-aware edge server deployment can significantly improve the efficiency and reliability of VEC networks. The proposed framework provides an effective solution for adaptive edge server deployment in dynamic vehicular edge computing environments and can support latency-sensitive intelligent transportation applications through efficient resource utilization and improved communication reliability.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.2030100</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.2030100</link>
        <title><![CDATA[Editorial: Securing the edge: cybersecurity for IoT and distributed systems]]></title>
        <pubdate>2026-11-12T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Carlos Cambra</author><author>Ruben Ruiz-Gonzalez</author><author>Rogério Dionísio</author><author>Fernando Ribeiro</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1936389</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1936389</link>
        <title><![CDATA[Real-time insider threat detection using adaptive user behavior analytics and file-system monitoring]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Osama A. Khashan</author>
        <description><![CDATA[Abnormal user activity and insider misuse are still serious risks in modern computing systems. Many practical solutions do not operate in real time as data volumes increase and infrastructures become more complicated, which slows response times and weakens containment. Reliance on manual processes or third-party solutions diminishes the user experience and limits long-term protection. This paper introduces a system that integrates machine learning with the Linux Filesystem in Userspace (FUSE) to provide real-time, continuous anomaly detection along with targeted security measures. The design uses Isolation Forest to learn baseline behavior and identify deviations from data-driven norms, instead of relying on prior attack signatures. Temporal and spatial profiling incorporate recent behavioral context, while adaptive thresholds track changes in observed deviation scores. This improves sensitivity to minor variations as the system adapts to evolving user behavior and new threats. Detected events are classified according to severity as benign, suspicious, and critical. Furthermore, thresholds are adjusted to minimize disruption to legitimate activity while maintaining protection. Operating at the filesystem layer provides direct visibility into file activity and supports automated detection and response without requiring user intervention. Experiments across the four evaluation sets demonstrate detection accuracy of up to 98.7% and an F-measure of 95.5%. Under normal operating conditions, average read and write times were 36.8 and 44.1 ms, respectively. The results demonstrate adaptive anomaly detection and severity-dependent response under the evaluated file-system workloads.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1972293</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1972293</link>
        <title><![CDATA[Are explanations faithful under fire? Evaluating XAI reliability on live honeypot intrusion-detection telemetry]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Govardhan Sridhar</author><author>J. B. Simha</author><author>Rashmi Agarwal</author>
        <description><![CDATA[One way to validate explanations from Explainable Artificial Intelligence (XAI) is to use operational data rather than pre-existing datasets. Using this approach, we deployed ten honeypots on Google Cloud Platform for 21 days. During this time, we received 10,900 sessions from 4,817 distinct IP addresses. We manually tagged each session via a human-in-the-loop process (operational annotations; Cohen’s κ = 0.91) and then trained six distinct types of models (Random Forest, XGBoost, LightGBM, MLP, LSTM, and Transformer) with 13 features. Each type of model was evaluated with three protocols – record independent, temporal (hour of day holdout), and source IP group. We evaluated how well each model explained its predictions using frozen checkpoints of a holdout set with two metrics: AUDAC (area under the descriptive-accuracy curve) and flip rate after perturbation. The tree ensembles (Random Forest, XGBoost and LightGBM) had lower AUDAC (indicating more faithful attributions) than the MLP and LSTM models. For instance, LightGBM had an AUDAC of 0.346, XGBoost had an AUDAC of 0.348, and Random Forest had an AUDAC of 0.351. There was consistency among the three seed runs for Random Forest. For LightGBM, the average AUDAC for the three seeds was consistent but the AUDAC for individual seeds varied. For XGBoost, there was no consistency. When we masked the input of the top 10 SHAP features for each model, we found that all correct malicious sessions changed their classification for all three tree-based ensembles (Random Forest, XGBoost and LightGBM). Approximately 98.8 percent of correct malicious sessions were changed for MLP. Under random masking, by contrast, only about 61% of correct malicious sessions flipped. The benign sessions were not flipped by the tree-based ensemble models for Seed 42, and only 2.2% of them were flipped by MLP. Across three seeds, the benign flip rate for Random Forest and LightGBM remained near zero, while the malicious flip rate for XGBoost ranged from 71.8% to 100%. The ranking of SHAP and LIME showed strong correlation (rho = 0.85–0.93) across all five models, although there was less agreement between instances. Due to the transformer’s inability to converge (F1 score of 0.00 on two seeds; produced identical outputs on the third), it was removed from ranking of faithfulness. Ablation studies on service-proxy and Leave One Service Out did not provide any evidence that the tested services relied on service-identity shortcuts. Furthermore, removal of the top three behavioural features used by honeypot signatures (command frequency, authentication failure ratio, and number of unique commands) resulted in relatively small reductions in F1 scores ranging from 3.3% to 3.8% points (approximately 84%–85%). Removal of a fourth feature, suspicious keyword hits, resulted in larger reductions in F1 scores ranging from 4.9–5.7 percentage points. These high levels of performance suggest that the models are using greater amounts of contextual information, rather than simply replicating signature-based rules.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947682</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947682</link>
        <title><![CDATA[AutoSwin: learned downsampling for adapting high-resolution chest X-rays to fixed-resolution foundation models]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joshua Bruton</author><author>Sean Terespolsky</author><author>Warrick Sive</author><author>Rodney Ehrlich</author><author>Annalee Yassi</author><author>Hairong Wang</author><author>Richard Klein</author>
        <description><![CDATA[Medical foundation models commonly operate on low-resolution inputs (e.g., 224 × 224), but the adequacy of this resolution depends on the spatial scale of the target pathology. Using chest radiographs from the VinDr ChestX dataset and the South African Medical Bureau for Occupational Diseases (MBOD), we find preliminary evidence that input resolution is a pathology-dependent bottleneck. In controlled resolution experiments, silicosis performance is strongly correlated with input resolution, improving up to 640 × 640, whereas tuberculosis performance peaks at 224 × 224 and shows no consistent trend with resolution. This descriptive divergence suggests that aggregate benchmarks can conceal the loss of fine-grained evidence needed for under-represented pathologies such as silicosis. We further evaluate AutoSwin, a trainable Hierarchical Shifted Window Transformer-based downsampling frontend that adapts high-resolution radiographs to a fixed-resolution foundation model. AutoSwin increases macro-averaged AUC from 0.5527 to 0.7437 on MBOD and from 0.8505 to 0.8873 on VinDr. These findings motivate pathology-specific resolution assessment as an important part of foundation-model design and show that learned downsampling can recover task-relevant information when retraining a high-resolution foundation model is impractical.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940035</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940035</link>
        <title><![CDATA[Personality without a person: measuring personality in generative AI]]></title>
        <pubdate>2026-11-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jason L. Harman</author><author>Jaelle Scheuerman</author><author>Nithish Raja Karuppiah Loganathan</author>
        <description><![CDATA[Personality describes relatively stable styles of thinking, feeling, and acting when interacting with the physical and social environment. The stability and reliability of these individual styles plays an important role in our daily lives as we team with various individuals at work and home. Generative AI agents (LLMs) are new additions to our lives, quickly assuming roles that might otherwise have been given to humans though there is little known about the stability and reliability of LLM “thinking” styles or actions. We have been investigating whether LLMs can be thought of as having personality out of the box along with the creation and measurement of AI agents with distinct and reliable personalities. In this study four LLMs were assessed with both self-report and newer gamified behavioral measures of personality under an Out-of-the-Box condition and with three prompt-based personas. Contrary to previous studies, LLMs showed very high test-retest reliability. Personality scores varied predictably following persona prompting, while agreement between assessment types varied across models and persona conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1932390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1932390</link>
        <title><![CDATA[Lost in space: uncovering fundamental limits of large language models in complex, spatial problem solving]]></title>
        <pubdate>2026-11-09T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Roussel Rahman</author><author>Catherine Sibert</author>
        <description><![CDATA[As agents based on Large Language Models (LLMs) are rapidly becoming assistive tools in the real world, it is important to understand the limits of their ability to reason and navigate in complex physical spaces. While LLMs have demonstrated impressive performance on many reasoning tasks, their spatial reasoning remains underexplored. We evaluated agents powered by five state-of-the-art open-source models on two complex spatial problems that humans excel at: the Tower of Hanoi (ToH) puzzle and the Tetris game. In contrast to humans, who are able to learn these tasks with ease and are able to interact with the environment with very little training, we find that the LLMs are badly lost in space. In the ToH task, the agents started well at low complexity levels but failed to find general, complexity-independent solution strategies that humans discover with little practice, culminating in performance collapse as task complexity increases. In Tetris, the models rarely produced legal placements, even in drastically simplified variants of the game. In both tasks, evaluating their paths in space relative to optimal or acceptable ones reflects a fundamental lack of understanding of physical space and its laws. Both tasks are fully describable in text, giving LLMs the best conditions to succeed; however, the agents still failed to follow the rules of the problems, diverged toward impossible paths, struggled to maintain objects and problem states, and did not demonstrate any understanding of gravity, rigid bodies, and solid boundaries. Altogether, these results highlight key differences between human and LLM problem-solving approaches and suggest an uphill battle in scaling purely language-based architectures into general problem solvers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.2010006</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.2010006</link>
        <title><![CDATA[Digital forensics in cybercrime investigation: a comparative legal and institutional analysis of the United States and India]]></title>
        <pubdate>2026-11-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rasheed Tahir</author><author>Sangar Dawood Mohammed</author><author>Kawar Mohammed Mousa</author>
        <description><![CDATA[Digital forensics has become indispensable to cybercrime investigations, yet the reliability and admissibility of digital evidence depend on legal rules, institutional capacity, and procedural safeguards as well as technical competence. This study conducts a qualitative comparative doctrinal and institutional analysis of digital forensics in the United States and India. It examines legislation, judicial decisions, evidentiary rules, official institutional materials, and relevant scholarly literature through five analytical dimensions: legal authority, procedural safeguards, institutional capacity, cross-border cooperation, and fundamental rights. The analysis identifies a specific divergence in the procedural role of electronic-evidence certification. In the United States, Federal Rules of Evidence 901 and 902(13)–(14) provide a plural authentication framework in which certification operates as an optional self-authentication pathway for qualifying electronic evidence, while Rule 702 separately governs expert testimony. In India, Section 63 of the Bharatiya Sakshya Adhiniyam 2023 integrates certification more directly into the statutory admissibility pathway for qualifying computer output. Both jurisdictions face challenges involving cloud-based evidence, encryption, institutional disparities, cross-border access, privacy, and due process. The study argues that effective digital forensic governance requires coordination among legal authorization, technical integrity, institutional competence, judicial oversight, and international cooperation. It recommends modernized evidentiary rules, standardized forensic procedures, professional training, stronger institutional capacity, and rights-protective mechanisms for obtaining and using electronic evidence.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1916336</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1916336</link>
        <title><![CDATA[Computer networking as infrastructure for clinical information exchange in nursing practice: a narrative review from network architecture to nursing outcomes]]></title>
        <pubdate>2026-11-06T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Alia Mohammed AlSulaimi</author>
        <description><![CDATA[Computer networks in clinical environments are commonly treated as passive data conduits. This narrative review examines them instead as technical infrastructure that conditions how nurses exchange clinical information, communicate across disciplines, and access professional knowledge. Six bibliographic databases and the primary standards corpora of HL7, IHE, IETF, IEEE, ISO/IEC and NIST were consulted for English-language sources published between January 2015 and February 2026, together with regulatory instruments in force in the United States and the European Union. Sources were identified purposively, through topic-based searching, citation chasing, and targeted retrieval of standards and regulatory documents from issuing bodies, rather than through a protocol-driven search; 83 reference entries representing 82 distinct works were retained, comprising 33 empirical clinical studies, 36 conceptual or architectural sources, 12 normative specifications, and 2 regulatory instruments. Retained sources were assigned to four evidence classes, each appraised against class-specific criteria, and every substantive claim is labelled as empirically demonstrated (E), architecturally inferred (A), or proposed (P). Three findings follow, and their evidentiary status differs. First, interoperability maturity, availability, and security architecture are architecturally implicated in the completeness and timeliness of nursing information at the point of care (A); direct empirical evidence isolating network properties from co-implemented application and workflow changes was not identified, and the available clinical evidence concerns composite interventions concentrated in high-income settings (E). Second, reported latency, bandwidth, and jitter figures are application-dependent design targets rather than clinical requirements, and no normative source for the figures most frequently cited was identified (S). Third, capabilities in 5G, edge computing, and distributed ledgers remain predominantly pre-deployment in nursing contexts (P). The review accordingly does not conclude that network architecture determines nursing or patient outcomes; it concludes that the architectural case is substantially stronger than the empirical one. A three-stage analytical model, with candidate measurable indicators, is proposed as a framework requiring empirical validation rather than as a demonstrated causal account.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1897185</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1897185</link>
        <title><![CDATA[Adversarial example detection for image captioning models via SPC-verify]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dehai Zhang</author><author>Jiaqi Mao</author><author>Jiajun Zhu</author>
        <description><![CDATA[Image captioning models are vulnerable to adversarial examples that can induce descriptions inconsistent with visual evidence, commonsense knowledge, or physical rules. Existing detection methods mainly rely on confidence anomalies, feature-space deviations, or reconstruction errors, making them less suitable for cross-modal generative tasks. To address this limitation, we propose SPC-Verify, a Semantic-Phenomenon Consistency Verification framework for adversarial example detection in image captioning. The framework combines Visual-Semantic Consistency Verification (VSCV), which assesses whether token-level visual evidence supports the corresponding generated concepts, with Phenomenon Plausibility Verification (PPV), which evaluates the consistency of complete captions with commonsense knowledge and physical constraints. To improve the reliability of local visual evidence, we introduce SCAR-SAT, a post-hoc heatmap refinement method for Show-Attend-and-Tell-based models that integrates semantic consistency, lightweight counterfactual support, spatial continuity, and temporal stability. We further propose Zero-Shot Cross-Model Threshold Transfer (ZS-CMTT), which learns shared decision thresholds from the joint score distributions of SAT and BLIP and transfers them to unseen captioning models without requiring target-model samples. Experiments on MSCOCO and Flickr30K across SAT and BLIP, under multiple attack settings, demonstrate the effectiveness of the proposed framework. Ablation studies show that the Grounded Category Vocabulary improves token-level verification, while PPV provides complementary sentence-level plausibility assessment. Further evaluations indicate that SCAR-SAT improves token-level visual grounding and that ZS-CMTT facilitates threshold transfer across captioning models. Overall, SPC-Verify provides an interpretable and transferable approach to adversarial example detection by jointly evaluating local visual-semantic consistency and global caption plausibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1942828</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1942828</link>
        <title><![CDATA[FABRIC: a standards-aligned orchestration platform for compute continuum operations in 5G networks and beyond]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gorka Nieto</author><author>Maite Puerta-Beldarrain</author><author>Begoña Blanco</author><author>Endika Aldecoa</author><author>Iker Costa</author><author>Unai Lopez-Novoa</author><author>Fidel Liberal</author>
        <description><![CDATA[IntroductionThe compute continuum, together with the high-speed and reliable connectivity provided by advanced communication technologies such as 5G or beyond 5G (B5G) networks, enables the development of new types of applications and services. However, deciding when application components should be re-instantiated and selecting the most appropriate destination node according to dynamic execution conditions remains a challenge. Although artificial intelligence (AI), among other techniques, has emerged as a promising approach to address these challenges, the integration and evaluation of such orchestrators remain complex due to the lack of modular and standardized architectures.MethodsIn this work, we present FABRIC, an orchestration platform that aims to evolve current 5G networks by enabling the re-instantiation of computational tasks whenever required through continuous computing and advanced task orchestration across distributed resources, all this whilst being aligned with current 3GPP and ETSI standards. This architecture bridges the gap between distributed computing paradigms and modern mobile network architectures, anticipating the computational and operational demands of next-generation mobile networks thanks to its standards-based nature. This modular design facilitates the implementation of sophisticated re-instantiation strategies while promoting interoperability and extensibility, fostering the adoption of more advanced orchestration mechanisms for future compute continuum environments supported by 5G/B5G networks.ResultsFABRIC is assessed with two use case scenarios, one with a CPU-intensive task and another one re-instantiating an application with user traffic.DiscussionThe results demonstrate that FABRIC supports interoperable service re-instantiation across the compute continuum, while facilitating service orchestration adapted to the requirements of each use case.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947684</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947684</link>
        <title><![CDATA[A post-quantum lightweight mutual authentication and encryption scheme for secure UAV communications]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alharith A. Abdullah</author><author>Shahad A. Hussien</author>
        <description><![CDATA[The unmanned aerial vehicle (UAV) field is rapidly evolving, increasing the need for effective security mechanisms to protect data in transit in resource-constrained environments. Although many solutions have been proposed to achieve the required security objectives, these traditional solutions either suffer from high computational complexity or are vulnerable to quantum computing threats, an open challenge. However, this research presents a lightweight security scheme that uses the Kyber 512 algorithm for a quantum-resistant key-exchange algorithm, followed by a Wi-Fi Protected Access 2 (WPA2)-inspired mechanism for mutual authentication. Furthermore, an Ascon Authenticated Encryption with Associated Data (ASCON-AEAD)-based lightweight algorithm is utilized for data encryption and verification. Additionally, the scheme is implemented in a simulation-based UAV environment to demonstrate its performance in protecting the confidentiality and integrity of MAVLink messages transferred between aircraft and the ground control station (CGS), as well as the communicated party's authenticity. Finally, the randomness of both authentication and encryption keys was evaluated using the National Institute of Standards and Technology (NIST) tests across 50 independent sessions, with a significance level of 0.01. All tests showed average p-values higher than the accepted significance level, with pass rates ranging from 96 to 100%, indicating good statistical randomness for the generated keys and supporting their security quality within the proposed protocol. In contrast, it also demonstrated a successful transfer of encrypted Micro Air Vehicle Link (MAVLink) messages. It ensured their validity only after verification of the identities of both communicating parties (i.e., mutual authentication). Furthermore, the achieved processing time averaged approximately 1.45 ms, ranging from 0.91 to 1.89 ms, which was within acceptable limits for such an environment, making this proposed solution efficient and effective.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1918269</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1918269</link>
        <title><![CDATA[Secure symmetric encryption using coloring of multipartite graphs]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>D Gomathi</author><author>Sivakumar Nagarajan</author>
        <description><![CDATA[IntroductionLightweight cryptographic schemes are needed for resource-constrained environments such as Internet of Things systems, wireless sensor networks, and embedded platforms. This paper presents a symmetric encryption framework based on the coloring of complete n-partite graphs, in which the keystream is derived from partition sizes and multi-character color labels assigned to graph partitions.MethodsA cyclic-padding convention extends all color labels to a common length m = max(ℓ1, ℓ2, …, ℓn) before summation, making the keystream period exactly m by construction. Plaintext characters are encoded using ASCII values and encrypted by symbol-wise modular addition over ℤ127, giving a repeated-key additive cipher with time complexity O(L+n·m) and space complexity O(L+n·m), where L is the message length. A formal threat model distinguishes four adversary categories (ciphertext-only, known-plaintext, chosen-plaintext, and active network adversaries) and sets security thresholds in terms of m.ResultsEncryption and decryption time scale linearly with message length. Empirical ciphertext entropy approaches 6.88 bits per symbol at n = 8 partitions. The scheme provides meaningful confidentiality against ciphertext-only adversaries when L ≤ m.DiscussionThe scheme is vulnerable to known-plaintext attacks, a fundamental limitation shared by all repeated-key additive ciphers. It is therefore positioned as a graph-structured, session-bound, repeated-key additive cipher framework, not a replacement for standardized cryptographic primitives. Its low computational cost supports use in lightweight and resource-constrained settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.2029696</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.2029696</link>
        <title><![CDATA[Editorial: Data privacy and cybersecurity challenges in digital business transformation]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Saqib Saeed</author><author>Noor Zaman Jhanjhi</author><author>Muhammad Adnan Khan</author><author>Dileep Kumar Yadav</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1964763</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1964763</link>
        <title><![CDATA[A preliminary design framework for human-friendly AI interaction: grounded in anthropomorphism]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siu Jung</author><author>Yoori Koo</author>
        <description><![CDATA[People converse with AI as they would with friends in daily life, collaborate with it, and form personalized relationships. In response to these emerging forms of interaction, this study clarifies the concept of Human-Friendly Interaction with conversational AI and develops the Human-Friendly AI Interaction Design Framework (HFAI-DF). This study positions anthropomorphism, a foundational phenomenon in human–AI interaction, as a theoretical bridge and reviews literature on anthropomorphism theory and related research on human cognition. On this basis, this study interpretively derives and organizes how anthropomorphism-inducing stimuli can be applied to AI agents, systematizing them into 30 AI behavioral items that enable human-friendly interaction. Subsequently, a survey was conducted and exploratory factor analysis performed to identify six dimensions as a preliminary structure for a design framework for AI’s human-friendly interaction. These dimensions are: Self-Aware Interaction, User-Adaptive Responsiveness Interaction, Context-Aware Reasoning Interaction, Socio-Emotional Interaction, Creative Collaboration Interaction, and Proactive Assistance Interaction. Within the evolving AI research landscape focused on interaction, this study extends the human-centered AI concept toward human-friendly interaction that operates in an organic manner comparable to human–human conversation, grounded in an understanding of human cognitive functioning. The proposed framework provides both AI providers and designers with an initial structure for shaping and improving the quality of human-friendly interaction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1909806</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1909806</link>
        <title><![CDATA[Global classrooms: a tri-national study on AI integration by higher education students]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Claudia Finkbeiner</author><author>Miri Shonfeld</author><author>Orit Avidov-Ungar</author><author>Zara Mamont</author><author>Emese Nagy</author><author>Yvonne Hesse</author>
        <description><![CDATA[Collaborative cross-cultural learning expands higher education students’ (HES’) intercultural and language awareness, enhances their intercultural competence, has an impact on attitudes towards technology and AI as well as towards the course design and general satisfaction with the collaborative experience in the course. Furthermore, their self-perceived role in a digital setting and their self-perceived English proficiency were measured. The CoGIH Project encourages HES to explore the educational potential of artificial intelligence (AI) for learning and teaching in the school setting. A special focus is on the impact AI might have on the language learning process in English as a lingua franca in a multilingual setting. N = 113 HES, including both pre-service and in-service teachers as well as students preparing for educational administration, from five institutions in Israel, Germany, and Hungary collaborated in 14 international groups, representing five higher education institutions; n = 91 answered the questionnaires. Each group investigated the educational potential of a specific AI application and developed an AI-supported, results-based webpage. The study employed a multi-method research design. Questionnaires were administered to the HES to investigate their experiences at the beginning and at the end of the course. While quantitative data were used to investigate the research through the lens of descriptive statistics and correlations between factors, qualitative data from the open questions were analysed to enable a deeper understanding of HES’ perceptions, experiences and meanings, as well as their critical thinking. The study highlighted strengths and challenges in using AI for educational purposes. The HES valued clear structures, logical navigation, and creative, motivating tasks. Simultaneously, recurring issues included incomplete translations, limited depth of critical reflection, and weaknesses in visual design. In sum, even though AI can support creativity and language learning and teaching, individual learners’ reflections and authentic teacher guidance remain indispensable for meaningful learning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1969691</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1969691</link>
        <title><![CDATA[Combined experimental and simulation evaluation of interference-resilient and energy-efficient wireless sensor networks using ESP32, TSCH, and multi-criteria decision analysis]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yersin Kozhanzharov</author><author>Andras Molnar</author><author>Svetlana Grigorieva</author><author>Assel Naizabayeva</author><author>Karlygash Alibekkyzy</author><author>Madina Bazarova</author><author>Talshyn Keribayeva</author><author>Saule Belginova</author>
        <description><![CDATA[IntroductionWireless sensor networks (WSNs) used in industrial monitoring and Internet of Things (IoT) applications must simultaneously maintain reliable data transmission, low communication delay, and efficient energy consumption under resource and interference constraints.MethodsThis study combines an ESP32-based hardware-assisted interference-emulation platform with large-scale OMNeT++ network modeling and multi-criteria evaluation. A 300-node WSN comprising 240 end devices and 60 routers was evaluated using Star, Tree, conventional Mesh, and Mesh + Time Slotted Channel Hopping (TSCH) configurations. Performance was assessed using Packet Delivery Ratio (PDR), mean transmission delay, energy consumption, and network-lifetime indicators. An additional reconstructed stochastic analysis used 20 independent runs per configuration, and scalability was evaluated for network populations from 50 to 1,000 nodes.ResultsIn the archived scenario-level analysis, Mesh + TSCH increased PDR from 93% to 99%, reduced mean delay from 150 ms to 61 ms, reduced per-node energy consumption from 6.42 J to 2.14 J, and increased the reported LND from 30 to 120 days relative to conventional Mesh. In the reconstructed analysis, Mesh + TSCH maintained higher PDR (79.26 ± 0.83% vs. 51.46 ± 2.49%) and lower per-node energy consumption (59.02 ± 0.65 J vs. 125.96 ± 0.82 J), but showed higher mean end-to-end delay (97.18 ± 5.56 ms vs. 57.84 ± 1.44 ms).DiscussionThe results indicate that TSCH-enabled coordination provides robust reliability and energy-efficiency advantages, whereas its latency effect depends on scheduling and implementation conditions. The combined findings demonstrate an application–dependent reliability–energy—latency trade-off and support multi-criteria evaluation for WSN architecture selection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940983</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940983</link>
        <title><![CDATA[PestFG-RTDETR: an efficient pest detector for small targets in dense trap-captured scenes]]></title>
        <pubdate>2026-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meng Zhang</author><author>Xianliang Jiang</author><author>Zijian Le</author><author>Guang Jin</author>
        <description><![CDATA[Agricultural pests pose a persistent threat to crop yield and quality, making accurate and efficient detection essential for modern crop protection. Although trap-based systems are widely used for automatic pest monitoring, pest detection in captured images remains challenging due to tiny pest bodies, clustered distributions, diverse capture states, and inter-class similarity. To address these challenges, we propose PestFG-RTDETR, a detector with three key components: frequency-guided adaptive feature interaction, dynamic upsampling, and a lightweight backbone. We also introduce a biologically inspired Copy-Paste augmentation strategy to enrich minority classes and generate realistic dense pest layouts on the dataset. When evaluated at an input resolution of 416 × 416 pixels, PestFG-RTDETR achieves an mAP@0.5 of 61.64% and an mAP@0.5:0.95 of 37.27%, outperforming RT-DETR-R18 by 4.67 and 6.08 percentage points, respectively, while using 1.55M fewer parameters. Extensive comparisons with representative two-stage, one-stage, and transformer-based detectors show that PestFG-RTDETR achieves competitive performance, indicating the effectiveness of the proposed design. Independent training and evaluation on a self-collected rice pest dataset further indicate that the proposed architecture can be applied to another trap-based pest detection scenario.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1823339</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1823339</link>
        <title><![CDATA[Automated creation of source code variants of a cryptographic hash function implementation using generative pre-trained transformer models]]></title>
        <pubdate>2026-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elijah Pelofske</author><author>Vincent Urias</author><author>Lorie M. Liebrock</author>
        <description><![CDATA[Generative pre-trained transformers (GPTs) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. However, GPT model outputs in general are stochastic and not always correct. For programming languages, the exact specification of the computer code, syntactically and algorithmically, is strictly required in order to ensure the security of computing systems and applications. Therefore, using GPT models to generate computer code poses an important security risk—while at the same time allowing for potential innovation in how computer code is generated. This study examines GPT model outputs of novel and correct, and notably very insecure, versions of implementations of the cryptographic hash function SHA-1. The GPT models Llama-2-70b-chat-hf, Mistral-7B-Instruct-v0.1, and zephyr-7b-alpha are used. The GPT models are prompted to re-write each function using a modified version of the localGPT framework to provide word embedding context of the full source code and header files to the model, resulting in over 150, 000 function re-write GPT output text blocks, approximately 50, 000 of which were able to be parsed as C code and subsequently compiled. The generated code is analyzed for being compilable, correctness of the algorithm, memory leaks, compiler optimization stability, and character distance to the reference implementation. Remarkably, several generated function variants have a high implementation security risk of being correct for some test vectors, but incorrect for other test vectors. Additionally, many function implementations were not correct to the reference algorithm of SHA-1, but produced hashes that have some of the basic characteristics of hash functions. Many of the function re-writes contained serious flaws such as memory leaks, integer overflows, out of bounds accesses, use of uninitialized values, and compiler optimization instability. Compiler optimization settings and SHA-256 hash checksums of the compiled binaries are used to cluster implementations that are equivalent but may not have identical syntax—using this clustering, over 100, 000 distinct and correct (up to input-output validation checking) versions of the SHA-1 codebase were generated, by permuting between versions of the four component functions, where each component C function of the reference implementation is different from the original code.]]></description>
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