Multi-terminal DC (MTDC) transmission systems based on modular multilevel converters (MMCs) are a key component of the envisioned future energy sector, where sustainability and efficiency are increasingly prioritized. To ensure their reliable operation, MMC currents must be regulated safely and rapidly under a wide range of uncertain operating conditions. Consequently, the design of current controllers faces a fundamental challenge: achieving fast transient response while maintaining robustness against uncertainties. This paper addresses this challenge by proposing a linear matrix inequality (LMI)-based design framework that leverages Lyapunov stability conditions to synthesize a less conservative static state-feedback controller. The proposed design method explicitly accounts for system constraints, including input saturation and overcurrent limits. The proposed method effectiveness is assessed on the CIGRE MT-HVDC benchmark, simulated in RTDS, and compared with existing methods.

This paper investigates how Conflict-free Replicated Data Types (CRDTs) can be used for dynamic software updates of distributed applications. We propose to model application updates as a new App CRDT that stores the application code associated with a semantic version, which defines a total order of the code updates. The App CRDT works with an API-compatible message delivery middleware, which allows applications to continue working with partially updated components in the face of backwards-incompatible software updates. We implemented our approach in AmbientTalk, an ambient-oriented programming language designed for distributed systems. We show how this CRDT can be integrated with existing AmbientTalk applications, requiring minimal changes. We also implemented our approach in LuAT, an ambient-oriented programming framework for Lua. This shows that our approach of using CRDTs to replicate code can be generalised to other programming languages.

When a conversational assistant recommends a brand to a user with no recent observed engagement, that user's same-name Google search rises +4.3 percentage points (pp) [3.1, 5.5], visits to the brand's own site +2.4 pp [1.4, 3.5], and brand-specific retailer-page visits +1.0 pp [0.3, 1.7] over matched backward placebos. Recovering that estimate is the work. The mention creates a brand exposure no web log attributes to the assistant, and the naive all-mention funnel that seems to measure it is confounded: many mentions are incidental references to brands the user already uses ("your Netflix download"), whose downstream visits are that existing customer's own behavior and surface as a brand-specific pre-trend. We measure off-platform response on a panel that joins opt-in clickstream to the same users' ChatGPT, Claude, and Gemini conversations, and isolate the effect with a pre-trend event study, a stance classifier, non-customer conditioning, and a within-response same-category control: incidental name-drops then move behavior far less (+1.8/+1.1/+0.3), and the named brand moves far more than unnamed same-category brands in the same response. The downstream path is mostly search-mediated and reaches both own sites and retailer pages, with a destination mix that tracks baseline brand-directed behavior rather than redirecting toward either. The design is observational and we do not observe transactions, so retail is purchase-adjacent. Standard referrer-based and last-click measurement miss this upstream exposure: assistants move observably-unengaged users into open-web brand navigation along a path attributed elsewhere.

Multimodal large language models (MLLMs) now appear in safety-critical applications, but the visual channel leaves them open to adversarial attacks that predominantly text-oriented safety alignment addresses only in part. Retraining a model for each new vulnerability class is usually too expensive to be practical. We report a comparative empirical evaluation of three inference-time defense methods and their combinations, run on eight models from the InternVL and Qwen-VL families across seven safety benchmarks that span four attack classes and total 9,000 evaluation samples. Every figure below comes from the same unified proxy classifier. Five findings emerge from the evaluation. First, within the evaluated models and benchmarks, no single defense dominates across all settings: what works depends on the model's baseline safety and on the attack type. Second, combining defenses directly drives benign-query over-refusal to 97-100% across all eight evaluated models, and SmoothVLM on its own reaches 99.2-100%. Third, a simple safety prompt keeps utility largely intact (0.0-18.2% over-refusal across all eight models, five of them below 7%, although two exceeded 15%) while still yielding moderate safety gains. Fourth, different attack classes expose different weaknesses across the evaluated setup, which is why multi-benchmark evaluation matters. Fifth, in a preliminary whitebox test on two models (n=20), text-level defenses suppressed a PGD visual attack that had succeeded without any defense: the defenses act at the output stage, where gradient optimization has limited direct leverage in the tested configuration. Read together, these results argue for adaptive defense selection rather than a single fixed defense configuration.

Heat pumps are expected to dominate the heating sector, substantially increasing peak electricity demand. At the same time, building thermal inertia enables operational strategies, providing temporal flexibility in heat pump operation and short-term demand response. However, this dynamic behavior is not yet represented in large-scale energy system optimization models. To address this gap, we present an innovative formulation of building thermal inertia. The resulting temperature variable is integrated into a novel conic temperature-aware heat pump efficiency formulation, enabling a more precise emulation of smart control strategies. In a case study of the European energy system, we show that the approach captures operational heating flexibility while remaining computationally efficient. The results indicate substantial untapped flexibility potential, enabling up to a 22% reduction in heating-related electricity costs. This potential can be realized through a suitable energy market design that incentivizes coordinated heat pump control, individually or via aggregators.

Explainability is increasingly required in AI-enabled software systems to support transparency, user trust, and compliance. Yet, explainability requirements are often written ad hoc, and unguided large language model support can yield vague, inconsistent, or incomplete statements. This paper presents a sequential, guideline-driven workflow for formulating explainability requirements and evaluates its tool-based operationalization. We first elicited candidate quality properties through a structured literature review and developer interviews. We then prioritized these properties in an online survey with practitioners (n=20) and derived a concise guideline of ten core properties with actionable formulation instructions. Next, we operationalized the guideline in a web-based tool that supports an iterative workflow of drafting, property-based checks, and revision. We evaluated the workflow in two complementary studies. In a workshop with requirements engineers (n=6), tool support reduced formulation time by 23.5% on average (Wilcoxon p=0.021). In an independent online study with software developers (n=18), tool-supported and manually written requirements received comparable ratings for implementability and formulation quality, with a descriptive slight preference tendency toward the tool-supported versions. Overall, our results suggest that combining a prioritized quality guideline with lightweight LLM support can reduce formulation effort while producing requirements that are perceived comparably to manually written ones.

As software systems increasingly rely on natural-language explanations to address user-reported explanation needs in requirements communication and support, ensuring that such explanations are consistent, relevant, and well formulated remains a major challenge. Purely automatic large language model (LLM) generation often lacks reliable grounding and controllable output quality. In this paper, we present a guideline-based formulation support tool for software explanations that combines LLM-assisted text generation with an empirically derived quality guideline. The tool structures the writing process into generation, quality checking, and iterative revision, while keeping domain control with developers. We evaluated the approach in a two-phase study consisting of an interview-based developer experiment and a controlled user survey. Six industry practitioners with software development or DevOps experience formulated explanations for real explanation needs in a human-only manual condition and in a human-with-LLM-support condition. In this small-scale evaluation, tool-supported formulation was on average 24.4% faster, although inferential analyses indicated only a trend for efficiency. In a subsequent user study with 17 participants and 204 paired comparisons, tool-supported explanations were rated significantly higher in overall satisfaction than manual explanations (p=0.003, rank-biserial correlation=0.86). Our findings suggest potential efficiency gains and higher perceived formulation quality through guideline-driven LLM assistance. Future work should examine long-term industrial use and integration into existing development workflows.

The rapid advancement of autonomous driving systems (ADS) has introduced significant challenges, particularly in the creation of realistic and complex scenarios for testing and validation. This paper introduces Modular2Simple, a tool designed to address these challenges by simplifying and enhancing the process of creating complex ADS scenarios. Modular2Simple seamlessly integrates with the CARLA simulator and is applicable to any software that supports the OpenSCENARIO format. By leveraging existing simple scenarios in the OpenSCENARIO format, the tool enables developers to create easily customizable modular scenarios through the combination of multiple simple or modular scenarios, significantly simplifying the scenario creation process while maintaining flexibility in scenario design. This approach not only facilitates the development of complex scenarios, reducing both development time and effort, but also promotes scenario reuse and customization, which leads to a significant reduction in code complexity and enhanced efficiency in scenario design and testing compared to traditional scenario development methods.

Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical security concerns, particularly regarding susceptibility to backdoor attacks. Recent studies have uncovered naturally occurring backdoors, referred to as natural backdoors, in normally trained deep learning models. Despite posing threats as serious as those introduced through data poisoning, security implications of natural backdoor vulnerabilities in CodeLMs remain poorly understood. In this paper, we conduct a thorough empirical study of natural backdoor vulnerabilities in CodeLMs across various model architectures and code intelligence tasks. Specifically, we examine potential natural backdoor vulnerabilities across 44 scenarios, demonstrating that natural backdoors are prevalent and intrinsic to CodeLMs. We reveal differences between injected and natural backdoor vulnerabilities at both the model and parameter levels. We then analyze the transferability of natural backdoor vulnerabilities from three perspectives: datasets, model architectures, and shared knowledge. We further investigate the causes of natural backdoors from two aspects: training datasets and the model training procedure. We evaluate existing backdoor defense techniques, including pre-training, in-training, and post-training defenses, in mitigating natural backdoors. Finally, we propose ScanNBT, a novel detection method designed to improve comprehensive detection of natural backdoor vulnerabilities in CodeLMs. We aim for our findings to enhance understanding of these vulnerabilities and provide insights for strengthening CodeLM security against backdoor threats.

Social media has become a key channel for political advertising during election campaigns. However, algorithmic biases in the delivery of these ads may distort the public's exposure to political messaging. This can hinder citizens' ability to make informed choices and undermine equal access to political discourse, raising concerns about the integrity of electoral processes. In this study, we examine gender-based discrimination in the delivery of political ads during the 2024 European Parliament elections. Using a large-scale dataset of over 110000 ads from 453 political parties and 968 candidates that generated over 7 billion impressions across 25 EU countries, we find that men were significantly more likely to be shown ads from populist and far-right parties than women -- even after accounting for ad content, platform-level competition, and targeting strategies. All else equal, ads by populist parties reach, on average, a 6 percentage point higher male share. Such imbalances restrict the ability of parties to reach diverse audiences and prevent voters from engaging equally with the full range of political viewpoints. This pattern is particularly concerning given that far-right and populist ads may reinforce political polarization and widen existing gender gaps in political engagement. Our findings underscore the need for platforms and policymakers to audit algorithmic ad delivery in political campaigns on social media and to implement safeguards that ensure fairness and protect democratic processes.

Research in XR (Extended Reality) has conventionally centred upon concepts such as Presence, Embodiment, Social Presence, and Co-presence. Within these traditions, bodily action, sensory contingencies, synchronous interaction, and possibilities for action have generally been regarded as constitutive conditions for the experience of "being there" and of being with others. XR environments, however, permit the partial separation of conditions that ordinarily co-vary in everyday experience. Bodily co-presence, temporal simultaneity, spatial configuration, and social interaction need not remain inseparable. This paper approaches this possibility as a problem of other-presentness. Other-presentness refers to the conditions under which another individual is experienced as existing "here and now". The contribution of this paper does not lie in arguing that asynchronous others can evoke social responses; such observations have already been addressed within parasocial interaction and social presence research. Rather, the novelty lies in theorising XR as a technological condition capable of separating and operationalising the constitutive elements of other-presentness as design variables. Reconsidering Bodyless Presence as a methodological precedent and drawing upon experimental findings from Immersive Video research, this paper formulates Bodyless Presentness as a condition in which another individual continues to be experienced as presently existing despite attenuated bodily co-presence and weakened real-time simultaneity.

Operator filtering allows for the regularization and compression of dense integral operators, effectively mitigating the memory and computational costs associated with iterative solvers. Previous works introduced filters that leverage the analytical spectral truncation of kernels for operators of the 2D Electric Field Integral Equation (EFIE). In this contribution, we will demonstrate how to obtain filtered kernels in a discrete numerical form within the framework of an Adaptive Integral Method (AIM), yielding results entirely comparable to analytical filters. By operating directly on the discrete operator representations, the proposed strategy ensures a native and robust compatibility with fast solver schemes that analytical formulations often lack. The effectiveness of the proposed approach will be demonstrated through numerical results, including its application to the Calderón preconditioned EFIE.

This paper investigates a viable two-echelon supply chain network design problem with unreliable facilities subject to disruptions. Unlike existing studies that consider supply chain echelons in isolation, the proposed models explicitly capture cross-echelon disruptions and quantify the value of incorporating such interdependencies. Network viability is achieved by jointly integrating resilience (via backup reassignment), agility (via mobile facilities), and environmental impact (via emissions caps) to ensure demand satisfaction across both echelons and support long-term network survival. Two mixed-integer programming formulations are developed: a scenario-based formulation and an implicit formulation, both minimizing expected fixed and service costs. To handle probabilistic service requirements, the implicit formulation incorporates a machine learning-enhanced chance-constrained programming approach, in which intractable capacity chance constraints are replaced by learned linear cuts enforcing a 95% service confidence level. These cuts are trained using several classification methods, including logistic regression, L1-regularized logistic regression, stochastic gradient descent, the perceptron algorithm, and logistic regression with a regularization parameter of 0.1, with the best-performing classifier selected as a surrogate. To further enhance scalability, two fix-and-relax heuristics are developed for the implicit formulation, while a sample average approximation (SAA) method is applied to the scenario-based formulation. Computational experiments demonstrate that the implicit formulation offers a computationally efficient and high-quality alternative to the scenario-based formulation. Moreover, the proposed heuristics and SAA approach effectively address medium- and large-scale instances, delivering high-quality solutions within acceptable computational times.

The ability to take advantage of computationally efficient high-order finite element methods to perform adaptive finite element analysis of complex engineering problems over general 3D domains requires the ability to adapt meshes with curved elements that maintain the level of geometric approximation of the domain boundary required. This paper presents a conforming curved mesh adaptation procedure aimed at effectively supporting automated adaptive analysis of problems for which the domain geometry is defined in a CAD system. The local mesh modification procedures interact with the CAD geometry to curve the mesh edges and faces representing those boundaries to the order of approximation of the high-order elements being used. The curved mesh edges and faces representing the domain boundaries are based on Bézier approximation geometry, which provides more accurate evaluations of surface-related quantities of interest than the commonly used interpolation methods. To attain computational efficiency during mesh adaptation, the interior mesh entities have their geometric order kept as low as possible while still maintaining control of element shapes. The order of curved mesh entities is limited to no higher than cubic, which allows the definition of an effective procedure to define the shape of the limited number of interior mesh entities that must be curved. The procedures, which are fully parallelized, are demonstrated on the adaptive radio-frequency analysis of a magnetically confined fusion system containing a fully represented radio-frequency antenna.

Speech-driven 3D facial animation research has shown promising results, but most methods rely on representations that are not compatible with production pipelines. In this work, we present a deployable system that bridges this gap by enabling speech-driven 3D facial animation directly in Unreal Engine (UE) using ARKit-compatible representations. We construct 3DMEAD-ARKit dataset by converting the MEAD corpus into blendshape sequences using MediaPipe, and retrain FaceDiffuser and ProbTalk3D-X to generate stochastic and emotion controllable animations. We further develop a modular UE plugin with a Python backend that supports model selection, and parameter control. We compare the results to two existing commercial tools: Epic Games' MetaHuman speech-driven animator and Nvidia Audio2Face with a perceptual user study. The results highlight the importance of comparisons among academic and commercial pipelines. We recommend watching the supplementary video. We also plan to do live demonstrations of our work at Siggraph 2026 conference.

This study examines the role of top-tier conference publications in Hungarian computer science research. We show that the national scientometric practice, which is currently journal-oriented, diverges from international norms, creating incentive distortions in researcher evaluation. By linking multiple databases (iCore, DBLP, MTMT, MTA-ATT), we mapped Hungarian-affiliated CORE A* and A conference papers, their temporal and thematic distribution, and author trajectories. Our results indicate that, in theoretical fields, publishing at international conferences became common earlier than in applied fields. At the same time, in applied fields, successful researchers are more likely to continue their careers in foreign institutions or in industry positions. Overall, a substantial share of the already established, internationally most successful researchers are now affiliated with institutions abroad. We recommend recognizing CORE A* papers as equivalent to D1 and CORE A papers as equivalent to Q1 journals in national evaluation systems.

In preparation for potential international agreements on artificial intelligence, the development of verification infrastructure for AI data centres is vital. We propose a method for cryptographically committing all information entering and leaving a data centre: Hashes are computed by network taps placed on all the information-carrying wires between the cluster and the outside world, enabling an auditor to retroactively challenge the preimage data to be sent to a privacy-preserving verification facility performing compliance checks. Our goal is to make it infeasible to covertly exfiltrate the results of undisclosed workloads in the cluster through the tapped wires. To this end, we specify the architecture of a ``Secure Gateway Device'', which handles the erasure of covert channels that post-hoc verification on hashed data cannot address: analogue and timing side-channels, as well as steganography in network protocol headers. The architecture eliminates the need for any processors trusted by both the Prover and the Verifier, leveraging passive optical fibre splitters and coin-flip protocols for random number generation where needed. We expect development costs of a demonstration device to be roughly equivalent to the cost of a small team of engineers for a few months, with a comparatively small bill of materials.

A formal mechanism is presented in which a willing regulator-matchmaker fosters cooperation on resources among participants in the AGI race, collects a Pigouvian tax based on the speed-up it induces, and invests the proceeds into alignment research. The construction is derived in the continuous-time options framework of Tan (2025) in which cooperation is treated as a jump in the underlying asset value of participating players, the Pigouvian component is matched to the marginal effect of increasing expected loss, and the total collected fund endogenizes the rate of learning on safety. It is shown how the framework allows for determining participation and optimal activity levels. Conditions under which it is optimal to enter the market are derived, and it is proven that if the orthogonality condition holds between the supported portfolio and the abilities component, the Suicide Region collapses at finite time, and the upper bound for this time is derived as sum of a deterministic and random term. Finally, if orthogonality is violated, it is proven that enhancing matchmaker capacity does not recover the market's superiority. The construction links research areas including two-sided markets, Pigouvian taxes, self-regulatory organizations, private law enforcement, evolutionary modeling of AI races, real options and option games, measurement of comparative progress and analysis of the Suicide Region.

The agentic web, in which users interact with the internet largely through agents acting on their behalf, is now technically feasible. However, many of the consumer and social benefits that could be realized by online AI agents acting scrupulously in their principals' interest are currently obstructed by outdated laws, terms of service, and other less formal practices which allow online platforms to block and degrade agent access, often in secret. No distinction is currently drawn between "malicious bots" and AI agents acting with the express delegated authority of a user. For the agentic web to realize its promise, it needs not only the technical infrastructure of protocols and interfaces, but the normative infrastructure of a broadly-accepted and socially-beneficial set of laws, norms and practices governing agentic access to online properties. Building that normative infrastructure requires a society-wide conversation. This paper aims to help precipitate that conversation, to identify normative principles that can guide it, and to advocate for policies that enable users' appropriately delegated agents to act online on their behalf, with as few curbs on their doing so as is reasonable given the other legitimate interests at stake.

Traffic generators are essential tools for evaluating the robustness and performance of networked systems. P4TG is an open-source, hardware-accelerated traffic generator implemented in P4 for the Intel Tofino ASIC. It has been adopted by researchers and industry due to its flexibility and multi-terabit generation capability, and its low cost compared to other traffic generators. However, like most existing generators, it primarily produces constant bit rate traffic, which does not reflect the highly time-varying behavior observed in real networks, such as flashcrowds and microbursts. Such patterns are difficult to emulate at scale with current tools. We present a data plane mechanism for P4TG that shapes periodic, time-varying traffic patterns, including patterns representative of DDoS attacks and burst-load scenarios. Pattern shaping in P4TG can be applied to its generated traffic at an aggregate throughput of up to 4 Tbit/s. We evaluate pattern accuracy and analyze scalability across different sampling resolutions and periods. Further, we demonstrate practical use cases, including zero-loss throughput determination and buffer capacity measurement. Finally, we present microburst-based attack scenarios that overload UDP receivers, switch buffers, and degrade TCP throughput on shared links while remaining undetectable to conventional rate monitoring.

Context: Every agentic AI system shipped to production carries two hidden risks: accumulated Technical Debt (TD) and unmonitored runtime energy costs. While functional benchmarking is common, the empirical link between internal structural quality (specifically TD) and dynamic energy consumption during execution remains unexplored, creating a blind spot for practitioners and organizations managing sustainability and operational budgets at scale. Goal: We propose a confirmatory empirical study correlating Self-Admitted Technical Debt (SATD) with hardware-level runtime energy consumption across agentic frameworks, to determine whether code quality can drive energy-aware design decisions. Method: We will evaluate five open-source agentic frameworks by executing a standardized task suite in a strictly controlled environment. SATD will be extracted via automated Python-based comment mining and categorized via LLM-based classification using fine-tuned prompt, while runtime energy will be measured at the hardware level. Our study will investigate three core research questions: (RQ1) the presence of TD within these frameworks; (RQ2) the variance in runtime energy consumption across architectures; and (RQ3) the statistical correlation between a framework's TD and its task-level energy consumption. Conclusion: The findings will establish whether automated source code analysis can serve as a reliable, early-warning proxy for energy-efficient framework selection, thereby advancing both green software engineering and agentic AI quality research.

Attribute-Specific Fashion Retrieval (ASFR) aims to improve fine-grained image retrieval by focusing on specific attributes. However, existing patch-based attention and Transformer methods often misalign with irregular attribute regions and are prone to background noise, limiting their ability to capture subtle, pixel-level microstructures. To tackle these challenges, we propose SuperFashion, the first ASFR framework that adopts superpixel tokens within a Transformer architecture. SuperFashion initially employs an attribute-guided attention mechanism to extract attribute-related features, which in turn guide the cropping of semantically meaningful image regions. Superpixel segmentation is then leveraged on these regions to generate compact, semantically coherent superpixel tokens. By incorporating modality-specific embeddings for both attribute and superpixel tokens, the superpixel token-based Transformer facilitates adaptive interaction and fusion, thereby enhancing attribute localization and discrimination. Extensive experiments on FashionAI, DARN, and DeepFashion demonstrate relative overall MAP improvements of 1.84%, 9.27%, and 9.35% over prior SOTA. SuperFashion offers a new solution for web-based image retrieval.

The last decade of research on the LOCAL model has seen tremendous progress in understanding locally checkable labeling (LCL) problems, culminating in an almost complete classification of the possible complexities LCL problems can exhibit. In particular, on undirected trees, Chang and Pettie showed that there is no LCL problem with complexity between $ω(\log n)$ and $n^{o(1)}$ and Chang showed that, for every positive integer $k$, there is no LCL problem with complexity between $ω(n^{1/(k+1)})$ and $o(n^{1/k})$; additionally, which side of each gap a problem is found on is decidable. While the class of LCL problems - which, roughly speaking, consists of problems for which the correctness of a solution can be described by a finite set of allowed node configurations, which in turn can be locally verified by a constant-time algorithm - includes many important problems, it has one major restriction: problems can be defined only on bounded degree graphs, which consequently restricts all the classification and gap results mentioned above. In this work, we propose a generalization of LCL problems to unbounded degree using Presburger monadic second-order (PMSO) formulas; more specifically, we consider what we call Local PMSO (LPMSO) problems, i.e., problems whose correct solutions are both finitely described by a PMSO formula and locally verifiable by a LOCAL algorithm in constant time - this class contains many of the important problems studied in the LOCAL model but defines them on unbounded degree graphs. As our main result we prove that, on unbounded degree rooted trees, the aforementioned $ω(\log n)$ - $n^{o(1)}$ and $ω(n^{1/(k+1)})$ - $o(n^{1/k})$ complexity gaps (and their decidability) extend to the class of LPMSO problems.

Given a graph $G = (V, E)$, a signed dominating function is a function $f: V \rightarrow \{-1, 1\}$ such that for every vertex $u \in V$, $\sum\limits_{v \in N[u]} f(v) \geq 1$. The weight of $f$ is defined as $\sum\limits_{u \in V} f(u)$. The objective of the \sd{} problem is to compute a signed dominating function $f$ of minimum weight. The problem is known to be NP-complete even when restricted to bipartite, chordal, and planar graphs. In this paper, we extend the known complexity results for the \sd{} problem. Since the problem is NP-complete on chordal graphs, we study its complexity on split graphs, a subclass of chordal graphs, and show that it remains NP-complete. Moreover, as the problem is W[2]-hard parameterized by weight, we investigate its parameterized complexity with respect to structural parameters. We prove that the problem is W[1]-hard when parameterized by feedback vertex set number (and hence by treewidth and clique-width). Motivated by this hardness result, we consider more restrictive parameters, neighbourhood diversity and twin cover number, and present FPT algorithms.

OPC UA has become the dominant open protocol in operational technology. Time-series databases routinely archive OPC UA telemetry but discard the semantic metadata (node hierarchy, engineering units, and type definitions) which gives sensor values their meaning. Recovering this information from a time-series database is non-trivial: namespace indices recorded at the source are session-local and unstable across restarts, and naive merging across multiple source servers results in identifier collisions. We present opcua-ts, an implemented architecture that persists this semantic information alongside its telemetry in a general-purpose time-series database under a lifecycle-stable join key, and that reconstructs the source address space as a live OPC UA endpoint. We characterize the conditions under which the reconstruction is sound across multi-source deployments and validate the approach with a NodeSet2 XML round-trip against the source server. Initial results from a boiler-simulator round-trip indicate that the approach is feasible.

Layered cybersecurity pipelines transform evidence before they decide on it, and the order of those transformations determines which security facts become visible to which layer. This paper gives layer order a finite-state semantics built from a layer-order automaton, deterministic sequential security transducers, evidence markers, and a final decision automaton. The worked case is HTTP request desynchronization: front-end and back-end processors compute incompatible request boundaries, and the same trace is detected or missed according to whether framing evidence reaches the parser-differential layer before it commits. The results separate completed-trace recognition, online editing, decision synthesis, and faithful enforcement; characterize faithful online enforcement as the regular prefix-closed case under causal visibility; and show that regular policies beyond that boundary remain recognizable without becoming deployable enforcers. The framework is monolithically equivalent to finite-output deterministic edit automata, while preserving layer-local invariants such as marker birth, marker survival, and reorder-sensitive visibility. A concrete parser-pair semantics identifies the forbidden marker factor with CL.TE, TE.CL, TE.TE, and HTTP/2-downgrade boundary disagreement under the stated abstraction, and a contextual reorder congruence classifies which component permutations induce the same decision language. The result is an automata-theoretic account of order-sensitive security failures and a compositional vocabulary for auditing, synthesizing, and comparing layered enforcement pipelines.

Three years ago, Philip Zucker posted an attempt to use answer set programming (ASP) for term extraction from e-graphs Although the task is NP-hard and ASP offers a natural modelling of e-graph terms, the initial attempt did not yield convincing results. From the aspect of practical ASP users, we first pinpoint the way to make ASP work and work well on the task of e-graph extraction. The initial results show the naïve ASP encoding is comparable on efficiency to the well-optimised ILP-based exact DAG extraction in the extraction-gym, and find several extra optimal extraction on the complex instances. This leads us to a further agenda: with the "better together of egg+Datalog", is there a better "better together" by having ASP as a more powerful Datalog? We discuss the potential benefit from each other.

Trajectory prediction is a key enabler of autonomous and cooperative driving systems. However, most existing benchmarks are either sensor-centric, geographically constrained, or based on synthetic mobility traces that do not capture real-world V2X communication dynamics. This paper introduces CAMASA, a large-scale infrastructure-based dataset derived from Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) collected within the Modena Automotive Smart Area (MASA). The dataset comprises more than 40 million CAMs and 2 million DENMs recorded under authentic urban traffic conditions over multiple months. We present a rigorous preprocessing pipeline that includes filtering, pseudonym reconciliation to account for ETSI privacy-driven stationID changes, and temporal normalization to 10 Hz trajectories, suitable for motion forecasting and time-series analysis. With over 14,000 km of reconstructed vehicle paths and tens of thousands of unique station IDs, CAMASA provides a statistically significant empirical foundation for research on Cooperative Intelligent Transportation Systems (C-ITS). Beyond trajectory prediction, the dataset enables calibration of microscopic urban traffic simulators (e.g., SUMO) and supports the development of realistic Intelligent Transportation Systems (ITS) Digital Twins by jointly modeling mobility patterns and V2X communication coverage in real deployments.

The rapid growth of digital content has made reliable integrity verification increasingly important. Existing solutions rely either on centralized authorities, which introduce trust dependencies and single points of failure, or on decentralized storage systems that incur prohibitive resource overhead. In this paper, we present snaproot, a lightweight system that implements the hash-anchoring paradigm of Haber and Stornetta on the Solana blockchain to provide efficient, decentralized file integrity verification. snaproot generates a SHA-256 hash of a file and stores it immutably on-chain as a permanent reference record. Verification is performed by recomputing the hash and comparing it to the stored value, yielding a deterministic binary outcome. We describe a four-tier trust architecture comprising three realized tiers and one prospective tier for long-term persistence beyond the lifetime of any single blockchain. We present a formal threat model, a security analysis grounded in the second-preimage resistance of SHA-256, and an empirical evaluation on Solana Devnet across file sizes from 1 KB to 500 MB. A central conceptual contribution is the explicit separation between existence proof, the key-independent claim that a file existed at a given time, and authorship proof, the key-dependent binding between a record and a specific wallet identity. This separation allows existence guarantees to survive key loss while preserving stronger authorship claims where keys are retained. We position snaproot against OpenTimestamps, OriginStamp, and Chainpoint and discuss limitations with respect to pre-registration manipulation and AI-generated content.

End users create IoT automation rules via trigger action programming, but their expressions are often fragmented, capturing device operations rather than high level intents. This gap leads to missing conditions, logical conflicts, and overlooked safety constraints, risking hazardous behaviors. To address this, we propose an intent driven requirements completion approach that reframes rule completion as a dual process: reconstructing intent from fragmented rules, then regenerating rules from that intent, with safety embedded throughout. We introduce a Bidirectional Requirements Traceability Tree, a three layer model linking rules, intents, and quality concerns, and design a multiagent framework that combines LLM reasoning with structured traceability. This enables completions that are both functionally complete and inherently safe, while remaining traceable and explainable. Evaluation shows our method significantly outperforms the baselines, improving the rule completion rate by 43% and reducing logical conflicts by over 21%. By grounding completion in intent understanding, we shift the paradigm from user to system responsibility, and from functional correctness to holistic trustworthiness.