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42nd International Symposium on Theoretical Aspects of computer Science, STACS 2025

作者： Agrawal, Akanksha Lokshtanov, Daniel Panolan, Fahad Saurabh, Saket Verma, Shaily Indian Institute of Technology Madras India University of California Santa BarbaraCA United States School of Computer Science University of Leeds United Kingdom The Institute of Mathematical Sciences HBNI Chennai India Department of Informatics University of Bergen Norway Algorithm Engineering Group Hasso Plattner Institute Potsdam Germany

ISBN: (纸本)9783959773652

The classic greedy coloring algorithm considers the vertices of an input graph G in a given order and assigns the first available color to each vertex v in G. In the Grundy Coloring problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the Partial Grundy Coloring, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex v is a color previously used for another vertex if such a color is available. Whether Grundy Coloring and Partial Grundy Coloring admit fixed-parameter tractable (FPT) algorithms, algorithms with running time f(k)nO(1), where k is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for Grundy Coloring in the negative by showing that the problem is W[1]-hard. For Partial Grundy Coloring, they obtain an FPT algorithm on graphs that do not contain Ki,j as a subgraph (a.k.a. Ki,j-free graphs). Aboulker et al. re-iterate the question of whether there exists an FPT algorithm for Partial Grundy Coloring on general graphs and also asks whether Grundy Coloring admits an FPT algorithm on Ki,j-free graphs. We give FPT algorithms for Partial Grundy Coloring on general graphs and for Grundy Coloring on Ki,j-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and "representative-family" like sets for Ki,j-free graphs that we use in obtaining our results may have wider algorithmic applications. © Akanksha Agrawal, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, and Shaily Verma.

关键词： Graph algorithms

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Imitation Game for Adversarial Disillusion with Multimodal Generative Chain-of-Thought Role-Play

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Chen, Fan-Yun Gu, Shih-Hong Gao, Kai Wang, Hanrui Echizen, Isao Information and Society Research Division National Institute of Informatics Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviours. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalises and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. © 2025, CC BY.

关键词： Adversarial machine learning

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AI-Powered Behaviour Analysis in Financial Services

AI-Powered Behaviour Analysis in Financial Services

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Thalapathi Rajasekaran Muthu Selvam Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India College of Computing and Informatics University of North Carolina at Charlotte Charlotte North Carolina USA

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This research investigates the application of state-of-the-art machine learning strategies, including deep learning, clustering, and reinforcement learning, to better understand user behavior in the banking sector. This strategy enriches personalisation, boosts customer engagement, and enhances operational efficiencies. Advanced neural network designs, including Recurrent Neural Networks (RNNs) and transformer architectures, are deployed to interpret transactional and communicative data. Various clustering methods, such as K-Means, DBSCAN, and Hierarchical Clustering, are utilised to classify users by their behavioral characteristics. Moreover, techniques like Deep Q-learning and multi-agent reinforcement learning are employed to adapt dynamically to user actions, thereby maximizing satisfaction. The research further highlights essential ethical issues concerning bias minimization, privacy protection, and the interpretability of models, ensuring the ethical deployment of machine learning practices. Through empirical validation, the suggested framework shows notable improvements in predicting defaults, refining marketing approaches, and increasing adoption rates. These findings underscore the revolutionary influence of machine learning in profiling user behavior, hinting at future developments in federated learning and quantum computing.

关键词： Industries Deep learning Ethics Adaptation models Q-learning Recurrent neural networks Computational modeling Refining Banking Transformers

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Structural Properties of Non-Linear Cellular Automata: Permutivity, Surjectivity and Reversibility

arXiv

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arXiv 2025年

作者： Ramdhane, Firas Ben Dennunzio, Alberto Margara, Luciano Menara, Giuliamaria Department of Informatics Systems and Communications University of Milano-Bicocca Italy Department of Computer Science and Engineering University of Bologna Cesena Italy University of Sfax Faculty of Sciences of Sfax Tunisia

This paper explores the algebraic conditions under which a cellular automaton with a non-linear local rule exhibits surjectivity and reversibility. We also analyze the role of permutivity as a key factor influencing these properties and provide conditions that determine whether a non-linear CA is (bi)permutive. Through theoretical results and illustrative examples, we characterize the relationships between these fundamental properties, offering new insights into the dynamical behavior of non-linear CA. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear algebra

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Enhancing User Interaction in Digital Financial Systems Using AI-Driven Front-End Technologies: A Personalised Response Approach

Enhancing User Interaction in Digital Financial Systems Usin...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study highlights the rapid advancements in artificial intelligence (AI) and machine learning (ML), which have transformed the financial sector by enhancing personalised user interactions and customer engagement. An innovative AI-driven framework was proposed to deliver real-time, context-aware, and highly personalised responses for Digital Banking users. The system architecture employs advanced ML models, natural language processing (NLP) techniques, and modern front-end technologies such as React and GraphQL to address the challenges related to data silos, computational demand, and data privacy. ML models such as user segmentation, recommendation, and NLP models work together to analyse user behaviour, predict needs, and generate human-like responses. Utilising React, GraphQL subscriptions, and Edge AI, the front-end layer ensures a seamless and responsive user experience. The experimental results showed a 120% increase in click-through rates, a 180% increase in follow-up actions, and a 70% reduction in average response time, demonstrating significant improvements in user engagement. User satisfaction, measured by the Net Promoter Score, was significantly higher for the personalised system (8.5) than for the traditional system (5.2). The proposed methodology lays the foundation for an agile trust-based financial environment. Future enhancements will focus on explainable AI, multimodal interactions, and blockchain integration to boost transparency, engagement, and data privacy.

关键词： Data privacy Analytical models Computational modeling Systems architecture Banking Predictive models Natural language processing Real-time systems User experience Time factors

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Metaverse for Education: Developments, Challenges, and Future Direction

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computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients Data Heterogeneity

Towards Robust Federated Learning: Investigating Poisoning A...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdenour Soubih Seyyid Ahmed Lahmer Mohammed Abuhamad Tamer Abuhmed College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago USA

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions.

关键词： Training Toxicology Federated learning Computational modeling Data models Information management Security Reliability Faces Resilience

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An Adaptable Methodology for Generating Realistic Annotated Synthetic Data with 3d Gaussian Splatting and Modeling Software

SSRN

引用

SSRN 2025年

作者： Losada del Olmo, Juan Jesús López-De-Teruel, Pedro Enrique Ruiz, Alberto Department of Computer Engineering and Technology University of Murcia Murcia30100 Spain Department of Informatics and Systems University of Murcia Murcia30100 Spain Bleecker Technologies Murcia30100 Spain

The increasing demand for large annotated datasets in computer vision underscores the need for scalable synthetic data generation methods, as traditional approaches often lack adaptability or offer limited annotations. To address these challenges, this work introduces a comprehensive methodology that leverages 3D Gaussian splatting and Blender to generate photorealistic synthetic datasets with automatic annotations, suitable for training deep learning computer vision models in multiple tasks, including object detection, pose estimation, semantic and instance segmentation, depth estimation, and surface normal estimation. The proposed pipeline can model any scenario by reconstructing high-fidelity 3D environments, integrating synthetic objects through diverse modeling techniques, and applying procedural generation to vary lighting, camera angles, and object placements. With all annotations generated automatically, this approach facilitates the efficient creation of extensive annotated datasets, thereby eliminating manual labeling efforts. To validate the effectiveness of our synthetic data, we train computer vision models exclusively on these datasets and evaluate their performance in real applications, demonstrating their generalization capability and practical relevance. The results confirm that our methodology provides a scalable, flexible, and high-quality alternative to traditional data collection, bridging the gap between synthetic and real-world datasets while offering a powerful tool for both research and industrial applications. Our code is publicly accessible at https://***/juanjesus-ldo/scene_generator. © 2025, The Authors. All rights reserved.

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Analysis of the Performance of Transformers and Ensemble Schemes for Twitter US Airlines Sentiment Analysis 24

Analysis of the Performance of Transformers and Ensemble Sch...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Konstantinos Kyritsis Isidoros Perikos Charalampos M. Liapis Vaggelis Kapoulas Michael Paraskevas Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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A Convolutionaly-Informed XLM-RoBERTa for Classifying Short Text Inquires in Greek School Network 24

A Convolutionaly-Informed XLM-RoBERTa for Classifying Short ...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Charalampos M. Liapis Isidoros Perikos Konstantinos Kyritsis Vaggelis Kapoulas Michael Paraskevas Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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