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Towards Ensuring AI data Access control in RDBMS

Towards Ensuring AI Data Access Control in RDBMS

4th International conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-Annual

作者： Kandolo, William Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9783031834318;9783031834325

Relational database Management systems (RDBMS) are foundational for structured data management across various domains, making their security paramount, especially in the era of Artificial Intelligence (AI). This paper proposes an AI-driven Access control (AI-DAC) framework that integrates machine learning (ML), reinforcement learning (RL), and metaheuristic algorithms to enhance data access control in RDBMS. The framework is designed to dynamically adapt to evolving security threats, providing a robust, multi-layered approach that includes diagnostic, corrective, and generative loops. The AI-DAC framework's effectiveness is validated through comprehensive experimental studies and real-world case analyses, demonstrating its capability to optimize access control policies, reduce unauthorized access, and ensure high security in RDBMS environments.

关键词： AI-driven Access control RDBMS Security Machine learning Reinforcement learning Metaheuristic Algorithms

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Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging systems

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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ieee TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 333-348页

作者： Gao, Yao Jiang, Qi Gao, Shaohua Sun, Lei Yang, Kailun Wang, Kaiwei Zhejiang Univ State Key Lab Extreme Photon & Instrumentat Hangzhou 310027 Peoples R China Hunan Univ Sch Robot Changsha 410012 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China

Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over traditional separate design approaches. However, current joint design approaches heavily rely on the manual identification of initial lenses, posing challenges and limitations, particularly for compound lens systems with multiple potential starting points. In this work, we present Quasi-Global Search Optics (QGSO) to automatically design compound lens based computational imaging systems through two parts: (i) Fused Optimization Method for Automatic Optical Design (OptiFusion), which searches for diverse initial optical systems under certain design specifications;and (ii) Efficient Physic-aware Joint Optimization (EPJO), which conducts parallel joint optimization of initial optical systems and image reconstruction networks with the consideration of physical constraints, culminating in the selection of the optimal solution in all search results. Extensive experimental results illustrate that QGSO serves as a transformative end-to-end lens design paradigm for superior global search ability, which automatically provides compound lens based computational imaging systems with higher imaging quality compared to existing paradigms.

关键词： Optical design Optical imaging Lenses Optical diffraction Optimization Adaptive optics Optical refraction Image reconstruction Computational modeling Compounds Computational imaging end-to-end lens design global optimization image reconstruction

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data-driven Decision Making in Smart Health and Emergency Management

Data-Driven Decision Making in Smart Health and Emergency Ma...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sudharson, K. Selvi, K. Ramu, V. Monika, V. Sureshkumar, A. Nagarajan, S. R.M.D. Engineering College Department of Aiml Chennai India S.A. Engineering College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba Chennai India Vel Tech Ranga Sanku Arts College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba India

ISBN: (纸本)9798331529833

Phenomena such as smart health and emergency management systems when embedded with data-driven decision-making have become a game-changer in the field of healthcare and an emergency response. In this paper, we investigate the use of advanced data analytics, machine learning algorithms and real-time data processing in improving decision-making activities in these domains. Using data from a diverse set of large datasets such as EHRs, wearables, emergency response systems etc., we hope to build predictive models and decision support systems which can help save lives by predicting the epidemic before it peaks saving valuable time when every minute counts. The research also deals with issues such as data privacy, interoperability and real-time data integration. Early findings suggest large efficiencies and effectiveness gains in health and emergency management, showing the game-changing potential of datadriven solutions in their domains. © 2025 ieee.

关键词： Electronic health record

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Quality Evaluation of College English Classroom Teaching based on K-Means Clustering Algorithm 1

Quality Evaluation of College English Classroom Teaching bas...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Zhang, Di The Open University of Lu'an Lu'an China

ISBN: (纸本)9798331529246

The quality of college English classroom teaching is a critical factor in shaping students' linguistic proficiency and academic growth. Traditional evaluation methods often suffer from subjectivity and inconsistency, making it difficult to accurately assess teaching effectiveness. To overcome these limitations, this study introduced a data-driven approach using the K-Means clustering algorithm, which objectively categorizes teaching quality based on various instructional parameters. By analyzing factors such as student engagement, teaching methodologies, assessment strategies, and classroom interaction metrics, the proposed model successfully segmented teaching sessions into meaningful clusters. The results demonstrated a classification accuracy of 99%, outperforming conventional assessment techniques. Key evaluation metrics, including precision, recall, F1-score further validated by the recommended clustering approach. These findings highlight the potential of machine learning in educational assessment, offering a structured and unbiased evaluation framework. The application of K-Means clustering provides institutions with valuable insights into teaching performance, enabling educators to refine their strategies and enhance student learning experiences. By identifying strengths and areas for improvement, this approach ensures continuous pedagogical development. Moreover, integrating AI-driven analytics into institutional assessment frameworks can help enhance academic quality and facilitate data-driven decision-making in curriculum planning. This research underscores the transformative role of machine learning in optimizing teaching effectiveness and fostering an improved learning environment. © 2025 ieee.

关键词： Adversarial machine learning

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Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced data Processing 11

Coordination for Complex Road Conditions at Unsignalized Int...

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11th International conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Chen, Ruo Zhu, Yang Su, Hongye Ningbo Innovation Center Zhejiang University Ningbo315100 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789897587450

In this paper, we use deep reinforcement learning to enable connected and automated vehicles (CAVs) to drive in a intersection with human-driven vehicles. The multi-agent deep deterministic policy gradient (MADDPG) algorithm is improved to be more efficient for data processing, so that it can solve the problem of learning bottlenecks in complex environments, and use sliding control to execute control strategies. Finally, the feasibility of the method is verified in the simulation environment of CARLA. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： data reduction

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RAMPA: Robotic Augmented Reality for Machine Programming by DemonstrAtion

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ieee ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3795-3802页

作者： Dogangun, Fatih Bahar, Serdar Yildirim, Yigit Temir, Bora Toprak Ugur, Emre Dogan, Mustafa Doga Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkiye Adobe Res CH-4052 Basel Switzerland

This letter introduces Robotic Augmented Reality for Machine Programming by Demonstration (Rampa), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of Programming by Demonstration (PbD) approaches on industrial robotic arms, e.g., Universal Robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. Rampa addresses critical challenges of PbD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.

关键词： Robots Trajectory Training Hands Robot programming data models Real-time systems Augmented reality User experience Human-robot interaction Virtual reality and interfaces learning from demonstration augmented reality (AR) extended reality (XR)

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IoT based Line of control Monitoring System 2

IoT based Line of Control Monitoring System

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 ieee.

关键词： Network security

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Adaptive Digital Twin-Assisted 3C Management for QoE-driven MSVS: A GAI-Based DRL Approach

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ieee TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 858-872页

作者： Huang, Xinyu Qin, Xue Li, Mushu Huang, Cheng Shen, Xuemin Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18015 USA Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Communication, computing, and buffer control (3C) management is essential to enhance quality-of-experience (QoE) in multicast short video streaming (MSVS). The existing 3C management schemes mainly rely on static data processing methods and a general QoE model, which may not efficiently improve QoE when users' swipe behaviors exhibit distinct spatiotemporal differences. In this paper, we propose an adaptive digital twin (DT)-assisted 3C management scheme to enhance QoE in MSVS. Particularly, DTs consist of user status data and data-based models, which can update multicast groups and abstract users' swipe features. An adaptive DT management mechanism is developed to adapt to users' swipe behavior dynamics. Then, a fine-grained QoE model is established by considering the impact of resource constraints and DT model accuracy, leading to accurate buffer control. Finally, a joint optimization problem of 3C management is formulated to maximize long-term QoE. To efficiently solve this problem, a diffusion-based deep reinforcement learning (DRL) algorithm is proposed, which utilizes the denoising technique to improve the action exploration capabilities of DRL. Simulation results based on a real-world dataset demonstrate that the proposed DT-assisted 3C management scheme outperforms benchmark schemes in terms of QoE, with improvements of 18.4% and 20.5% under low and high user dynamics, respectively.

关键词： Streaming media Quality of experience Computational modeling Resource management Adaptation models Accuracy Web sites Video recording Real-time systems Quality assessment Digital twin 3C management multicast transmission short video QoE diffusion-based DRL

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Human-Centric Context and Self-Uncertainty-driven Multi-Modal Large Language Model for Training-Free Vision-Based Driver State Recognition

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Hu, Chuanfei Li, Xinde Southeast Univ Sch Automat Key Lab Measurement & Control CSE Nanjing 210096 Peoples R China Nanjing Ctr Appl Math Nanjing 211135 Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China

A vision-driven driver monitoring system plays a vital role to guarantee the driving safety. Recent advances focus on modeling a learning-based method to realize the driver monitoring system, benefiting from the powerful capability of data-driven feature extraction. Although the acceptable performances of these methods are achieved, the training procedure with massive data would significantly increase the labor costs. Thus, it is intuitive to explore a training-free vision-based driver state recognition in the era of large language model (LLM)/multi-modal large language model (MLLM). There are two issues should be considered. First, the general prompt might not guide MLLM to focus on the human-centric visual appearances, resulting in the insufficient understanding of MLLM for the contextual cues of driver. Second, the inherent uncertainty of MLLM might impact the reasoning precision, which is not considered comprehensively for the MLLM-based driver state recognition. In this paper, we focus on a vision-based driver state monitoring method, where a novel training-free driver state recognition method via human-centric context and self-uncertainty-driven MLLM (HSUM). Specifically, a human-centric context generator (HCG) is first proposed based on a context-specific prompt. MLLM is guided to capture the human-centric contextual cues as a scene graph. The contextual interaction of objects with their surroundings can be represented effectively. Then, a self-uncertainty response enumerator (SRE) is proposed to exploit the uncertainty of MLLM. The potential reasoning responses are enumerated repeatedly based on the assembly of the human-centric context and uncertainty-specific prompt. Furthermore, to reveal the precise reasoning result from the enumerated responses, we introduce the Dempster-Shafer evidence theory (DST)-based combination rule to conduct an evidence-aware fusion (EAF). The precise answer could be gathered based on DST-based combination rule theoretically. Ex

关键词： Driver monitoring system multi-modal large language model driver state recognition driver state recognition driver distraction and emotion recognition driver distraction and emotion recognition driver distraction and emotion recognition

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Integrating AI and data Mining Tools into Information systems Courses-A Case Study of Successes, Challenges, and Student Outcomes 14

Integrating AI and Data Mining Tools into Information System...

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14th International conference on Educational and Information Technology, ICEIT 2025

作者： Olugboja, Adedeji Business and Economics York College of the City University of New York New York United States

ISBN: (纸本)9798331540883

This paper examines the integration of artificial intelligence (AI) and data mining tools into an undergraduate information systems course, assessing its impact on student engagement, comprehension, and practical skill development. With industries increasingly prioritizing data-centric roles, IT education must evolve to equip students with hands-on, technology-integrated skills relevant to workforce demands. Quantitative and qualitative data were collected using a mixedmethods approach to evaluate learning outcomes and instructional efficacy. Key findings revealed that students exhibited a 20 % increase in project scores and a 15 % improvement in quiz performance, with high engagement levels during hands-on and collaborative activities. Challenges included technical difficulties and varying programming skill levels, suggesting additional support resources were needed. The case study highlights best practices such as incremental skill-building, real-world data usage, and collaborative learning, which foster adaptability and analytical thinking. These results underscore the im- portance of AI-integrated learning approaches in IT education, bridging theoretical knowledge and industry needs and preparing students for data-driven roles. © 2025 ieee.

关键词： Students

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