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Yayın Construction of a Turkish proposition bank(Tubitak Scientific & Technical Research Council Turkey, 2018) Ak, Koray; Toprak, Cansu; Esgel, Volkan; Yıldız, Olcay TanerThis paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.Yayın Semantic communications in networked systems: a data significance perspective(IEEE, 2022-07-01) Uysal, Elif; Kaya, Onur; Ephremides, Anthony; Gross, James; Codreanu, Marian; Popovski, Petar; Assaad, Mohamad; Liva, Gianluigi; Munari, Andrea; Soret, Beatriz; Soleymani, Touraj; Johansson, Karl HenrikWe present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems. We define semantics of information, not as the meaning of the messages, but as their significance, possibly within a real time constraint, relative to the purpose of the data exchange. We argue that research efforts must focus on laying the theoretical foundations of a redesign of the entire process of information generation, transmission and usage in unison by developing: advanced semantic metrics for communications and control systems; an optimal sampling theory combining signal sparsity and semantics, for real-time prediction, reconstruction and control under communication constraints and delays; semantic compressed sensing techniques for decision making and inference directly in the compressed domain; semantic-aware data generation, channel coding, feedback, multiple and random access schemes that reduce the volume of data and the energy consumption, increasing the number of supportable devices. This paradigm shift targets jointly optimal information gathering, information dissemination, and decision making policies in networked systems.Yayın ComStreamClust: a communicative multi-agent approach to text clustering in streaming data(Springer Science and Business Media Deutschland GmbH, 2023-12) Najafi, Ali; Gholipour-Shilabin, Araz; Dehkharghani, Rahim; Mohammadpur-Fard, Ali; Asgari-Chenaghlu, MeysamTopic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods.Yayın Automating cyber risk assessment with public LLMs: an expert-validated framework and comparative analysis(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Ünal, Nezih Mahmut; Çeliktaş, BarışTraditional cyber risk assessment methodologies face a critical dilemma: they are either quantitative yet static and context-agnostic (e.g., CVSS), or context-aware yet highly labor-intensive and subjective (e.g., NIST SP 800-30). Consequently, organizations struggle to scale risk assessment to match the pace of evolving threats. This paper presents an automated, context-aware risk assessment framework that leverages the reasoning capabilities of publicly available Large Language Models (LLMs) to operationalize expert knowledge. Rather than positioning the LLM as the final decision-maker, the framework decouples semantic interpretation from risk scoring authority through a transparent, deterministic Dynamic Metric Engine. Unlike complex closed box machine learning models, our approach anchors the AI's reasoning to this expert-validated metric schema, with weights derived using the Rank Order Centroid (ROC) method from a survey of 101 cybersecurity professionals. We evaluated the framework through a comparative study involving 15 diverse real-world vulnerability scenarios (C1-C15) and three supplementary sensitivity stress tests (C16-C18). The validation scenarios were independently assessed by a cohort of ten senior human experts and two state-of-the-art LLM agents (GPT-4o and Gemini 2.0 Flash). The results show that the LLM-driven agents achieve scoring consistency closely aligned with the human median (Pearson r ranging from 0.9390 to 0.9717, Spearman ρ from 0.8472 to 0.9276) against a highly reliable expert baseline (Cronbach's α =0.996), while reducing the assessment cycle time by more than 100× (averaging under 4 seconds per case vs. a human average of 6 minutes). Furthermore, a dedicated context sensitivity analysis (C13-C15) indicates that the framework adapts risk scores based on organizational context (e.g., SME vs. Critical Infrastructure) for identical technical vulnerabilities. Importantly, the system is designed not merely to replicate expert intuition, but to enforce bounded, policy-consistent risk evaluation under predefined governance constraints. Overall, these findings suggest that commercially available LLMs, when constrained by expert-validated metric schemas, can support reproducible, transparent, and real-time risk assessments.Yayın Adaptive incident escalation in SOCs via AI-driven skill-aware assignment and tier optimization(Institute of Electrical and Electronics Engineers Inc., 2026-04-15) Abuaziz, Ahmed; Çeliktaş, BarışModern Security Operations Centers (SOCs) face significant operational bottlenecks driven by escalating alert volumes, increasingly sophisticated cyberattack vectors, and chronic imbalances in analyst workloads. Conventional rule-based escalation models frequently fail to account for the multi-dimensional nature of incident characteristics, the nuances of analyst expertise, and fluctuating operational demands. This study proposes a comprehensive AI-driven framework for intelligent incident assignment and workload optimization. The framework introduces five primary contributions: 1) a multi-factor scoring model that integrates severity and complexity metrics with dynamic workload balancing; 2) two novel optimization algorithms, Quantile-Targeted Normality-Regularized Optimization (QT-NRO) and Joint Optimization of Weights and Thresholds (JOWT), to calibrate scoring coefficients against target analyst utilization; 3) a Large Language Model (LLM) engine leveraging Retrieval-Augmented Generation (RAG) for semantic alignment between incident requirements and analyst expertise; 4) an Adaptive Capacity Zoning mechanism for dynamic workload management; and 5) a novel RAG Relevance Score metric—a pre-resolution, semantic alignment indicator that quantifies analyst-incident assignment quality independently of resolution time, addressing a fundamental limitation of traditional temporal metrics such as Mean Time to Resolution (MTTR) and providing a reusable benchmark applicable to any skill-aware assignment system. In addition, the framework incorporates a feedback-based continuous learning mechanism that utilizes historical resolution data to inform future assignments. An experimental evaluation using 10,021 real-world incidents from Microsoft Defender demonstrates that the JOWT algorithm achieves a tier distribution alignment within 0.8% of targets. LLM-enhanced semantic matching yields improvements between 26.7% and 126.8% in skill alignment across both normal-load and high-load evaluations, while simulations indicate a 31.8% reduction in MTTR. These results substantiate the efficacy of AI-driven methodologies in enhancing SOC operational efficiency and response precision.
