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8th International Joint Conference on Rules and Reasoning, RuleML+RR-Companion 2024

作者： Paduraru, Ciprian Cristea, Rares Stefanescu, Alin University of Bucharest Romania Institute for Logic and Data Science Romania

We present an overview of the AI techniques used in Horizon Project No. 101070455 DYNABIC (Dynamic business continuity and response of critical systems against advanced cyber-physical threats). The project, which started in December 2022 and spans three years, aims to enhance European critical services’ resilience and business continuity against cyber-physical threats. The project focuses on real-time prediction, assessment, and mitigation of threats, as well as automated optimization and orchestration strategies for response and prevention. Demonstrations are conducted in four industry-based case studies, simulating attacks on intelligent transportation services, electric vehicle charging stations, 5G telecommunication infrastructure, and healthcare services. Each infrastructure is modeled using a digital twin, which simulates business processes and allows user input for control. The paper focuses on one of the uses of AI within DYNABIC involving large-language models fine-tuned to assist cybersecurity experts. © 2024 Copyright for this paper by its authors.

关键词： 5G mobile communication systems

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LLM-based methods for the creation of unit tests in game development 28th

LLM-based methods for the creation of unit tests in game dev...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Paduraru, Ciprian Staicu, Adelina Stefanescu, Alin University of Bucharest Department of Computer Science Romania Institute of Logic and Data Science Romania

Problems related to the quality of games, whether on the initial release or after updates, can lead to player dissatisfaction, media attention, and potential financial setbacks. These issues can stem from software bugs, performance bottlenecks, or security vulnerabilities. Despite these challenges, game developers often rely on manual playtesting, highlighting the need for more robust and automated processes in game development. This research explores the application of Large Language Models (LLMs) to automate the creation of unit tests in game development, focusing on strongly typed programming languages such as C++ and C#, which are widely used in the industry. The study focuses on fine-tuning Code Llama, an advanced code generation model, to address common scenarios in game development, including game engines and specific APIs or backends. Although the prototyping and evaluations primarily took place within the Unity game engine, the proposed methods can be adapted to other internal or publicly available solutions. The evaluation results demonstrate these methods' effectiveness in improving existing unit test suites or automatically generating new tests based on natural language descriptions of class contexts and targeted methods. © 2024 The Authors.

关键词： C++ (programming language)

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CyberGuardian 2: Integrating LLMs and Agentic AI Assistants for Securing Distributed Networks 20

CyberGuardian 2: Integrating LLMs and Agentic AI Assistants ...

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20th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2025

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587429

Robust cybersecurity measures are essential to protect complex information systems from a variety of cyber threats, which requires sophisticated security solutions. This paper explores the integration of Large Language Models (LLMs) to improve cybersecurity operations within Security Operations Centers (SOCs). The proposed framework has a modular plugin architecture where Agentic AI controls the information flow, includes Retrieval Augmented Generation (RAG), protection methods for human-chatbot interactions and tools for managing tasks such as database interactions, code generation and execution. By utilizing these techniques, the framework aims to streamline the workflows of SOC analysts, allowing them to focus on critical tasks rather than redundant activities. The study also explores the dynamic customization of LLMs based on client data, user experience, potential risks and language preferences to ensure a user-centric approach. The results show improvements in efficiency and effectiveness and highlight the potential of LLMs in cybersecurity applications. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Cyber attacks

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Intuitionistic Propositional logic in Lean 8

Intuitionistic Propositional Logic in Lean

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8th Symposium on Working Formal Methods, FROM 2024

作者： Trufas, Dafina LOS Faculty of Mathematics and Computer Science University of Bucharest Institute for Logic and Data Science Bucharest Romania

In this paper we present a formalization of Intuitionistic Propositional logic in the Lean proof assistant. Our approach focuses on verifying two completeness proofs for the studied logical system, as well as exploring the relation between the two analyzed semantical paradigms - Kripke and algebraic. In addition, we prove a large number of theorems and derived deduction rules. © Dafina Trufas.

关键词： Computer circuits

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CyberGuardian: An Interactive Assistant for Cybersecurity Specialists Using Large Language Models

CyberGuardian: An Interactive Assistant for Cybersecurity Sp...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

Cybersecurity plays an important role in protecting people and critical infrastructure. Sectors such as energy, defense and healthcare are increasingly at risk from cyber threats. To address these challenges, dedicated Security Operations Centers (SOCs) continuously monitor threats and respond to critical issues. Our research focuses on the use of Large Language Models (LLMs) to optimize the tasks of SOCs and to support security professionals. In this work, we propose a framework, which we call CyberGuardian, whose main goal is to provide a chatbot application along with a set of tools to support SOC analysts in real-time cybersecurity tasks. We use state-of-the-art LLM techniques and start from a Llama 2 model, then fine-tune the base model using a new dataset containing mainly cybersecurity topics. The CyberGuardian framework has a plugin architecture that integrates processes such as Retrieval Augmented Generation (RAG), safeguard methods for interaction between human user and chatbot, integration with tools to manage tasks such as database interactions, code generation and execution, and plotting graphs just by specifying the task in a natural language. The work, along with the dataset we collected and reusable code to update or customize, is made available to the cybersecurity community as open source. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Critical infrastructures

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RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning

RLHR: A Framework for Driving Dynamically Adaptable Question...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

In today’s corporate landscape, the creation of questionnaires, surveys or evaluation forms for employees is a widespread practice. These tools are regularly used to check various aspects such as motivation, opportunities for improvement, satisfaction levels and even potential cybersecurity risks. A common limitation lies in their generic nature: they often lack personalization and rely on predetermined questions. Our research focuses on improving this process by introducing AI agents based on reinforcement learning. These agents dynamically adapt the content of surveys to each person based on their unique personality traits. Our framework is open source and can be seamlessly integrated into various use cases in different industries or academic research. To evaluate the effectiveness of the approach, we tackle a real-life scenario: the detection of potentially inappropriate behavior in the workplace. In this context, the reinforcement learning-based AI agents function like human recruiters and create personalized surveys. The results are encouraging, as they show that our decision algorithms for content selection are very similar to those of recruiters. The open-source framework also includes tools for detailed post-analysis for further decision making and explanation of the results. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Reinforcement learning

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Enhancing User Experience in Games with Large Language Models

Enhancing User Experience in Games with Large Language Model...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Cernat, Marina Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

This paper explores the application of state-of-the-art natural language processing (NLP) technologies to improve the user experience in games. Our motivation stems from the realization that a virtual assistant’s input during games or simulation applications can significantly assist the user in real-time problem solving, suggestion generation, and dynamic adjustments. We propose a novel framework that seamlessly integrates large-scale language models (LLMs) into game environments and enables intelligent assistants to take the form of physical 3D characters or virtual background entities within the player narrative. Our evaluation considers computational requirements, latency and quality of results using techniques such as synthetic dataset generation, fine-tuning, Retrieval Augmented Generation (RAG) and security mechanisms. Quantitative and qualitative evaluations, including real user feedback, confirm the effectiveness of our approach. The framework is implemented as an open-source plugin for the Unreal Engine and has already been successfully used in a game demo. The presented methods can be extended to simulation applications and serious games in general. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Natural language processing systems

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Improving Legal Judgement Prediction in Romanian with Long Text Encoders 3

Improving Legal Judgement Prediction in Romanian with Long T...

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3rd Annual Meeting of the ELRA-ISCA Special Interest Group on Under-Resourced Languages, SIGUL 2024

作者： Masala, Mihai Rebedea, Traian Velicu, Horia Institute for Logic and Data Science Romania University Politehnica of Bucharest Romania BRD Groupe Societe Generale Romania

ISBN: (纸本)9782493814296

In recent years,the entire field of Natural Language Processing (NLP) has enjoyed amazing novel results achieving almost human-like performance on a variety of tasks. Legal NLP domain has also been part of this process, as it has seen an impressive growth. However, general-purpose models are not readily applicable for legal domain. Due to the nature of the domain (e.g. specialized vocabulary, long documents) specific models and methods are often needed for Legal NLP. In this work we investigate both specialized and general models for predicting the final ruling of a legal case, task known as Legal Judgment Prediction (LJP). We particularly focus on methods to extend to sequence length of Transformer-based models to better understand the long documents present in legal corpora. Extensive experiments on 4 LJP datasets in Romanian, originating from 2 sources with significantly different sizes and document lengths, show that specialized models and handling long texts are critical for a good performance. © 2024 ELRA Language Resource Association.

关键词： Forecasting

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Impact of Task Adapting on Transformer Models for Targeted Sentiment Analysis in Croatian Headlines 30

Impact of Task Adapting on Transformer Models for Targeted S...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lee, Sofia Bloem, Jelke Vrije Universiteit Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands Data Science Centre University of Amsterdam Netherlands

ISBN: (纸本)9782493814104

Transformer models, such as BERT, are often taken off-the-shelf and then fine-tuned on a downstream task. Although this is sufficient for many tasks, low-resource settings require special attention. We demonstrate an approach of performing an extra stage of self-supervised task-adaptive pre-training to a number of Croatian-supporting Transformer models. In particular, we focus on approaches to language, domain, and task adaptation. The task in question is targeted sentiment analysis for Croatian news headlines. We produce new state-of-the-art results (F1= 0.781), but the highest performing model still struggles with irony and implicature. Overall, we find that task-adaptive pre-training benefits massively multilingual models but not Croatian-dominant models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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LLM-based methods for the creation of unit tests in game development

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Procedia Computer science 2024年 246卷 2459-2468页

作者： Ciprian Paduraru Adelina Staicu Alin Stefanescu University of Bucharest Department of Computer Science Romania Institute of Logic and Data Science Romania

Problems related to the quality of games, whether on the initial release or after updates, can lead to player dissatisfaction, media attention, and potential financial setbacks. These issues can stem from software bugs, performance bottlenecks, or security vulnerabilities. Despite these challenges, game developers often rely on manual playtesting, highlighting the need for more robust and automated processes in game development. This research explores the application of Large Language Models (LLMs) to automate the creation of unit tests in game development, focusing on strongly typed programming languages such as C++ and C#, which are widely used in the industry. The study focuses on fine-tuning Code Llama, an advanced code generation model, to address common scenarios in game development, including game engines and specific APIs or backends. Although the prototyping and evaluations primarily took place within the Unity game engine, the proposed methods can be adapted to other internal or publicly available solutions. The evaluation results demonstrate these methods’ effectiveness in improving existing unit test suites or automatically generating new tests based on natural language descriptions of class contexts and targeted methods.

关键词： Large language models fine-tuning game development unit tests

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