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Design and Implementation of a Comprehensive RAG-Driven Dashboard Within the ILO System for Data Visualization and Query Support: A Case Study of the University of Petra

Design and Implementation of a Comprehensive RAG-Driven Dash...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu-Arqoub Abed Alkarim Banna Nuha El-Khalili Mohammad Al-Shaikh Hasan Department of Computer Science University of Petra Amman Jordan Data Science & Artificial Intelligence Dept. University of Petra Amman Jordan Software Engineering Department University of Petra Amman Jordan Computer Science Department Brunel University London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This article presents a comprehensive study on designing and implementing a dashboard for enhanced data visualization and query support at the University of Petra. The system leverages retrieval-augmented generation (RAG) and large language models (LLMs) to support diverse document types, including curricula, course descriptions, and program outcomes, alongside intended learning outcome (ILO)-related files. Our implementation demonstrates significant improvements in data accessibility and query response times while maintaining high accuracy in information retrieval and visualization. Through extensive evaluation, we show that this innovative approach transforms data management processes in higher education by enabling natural language interactions with educational data systems, building upon established business intelligence frameworks while introducing advanced AI capabilities.

关键词： Large language models Retrieval augmented generation Natural languages Data visualization Transforms Educational technology Information retrieval Data systems Business intelligence Time factors

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A novel energy-efficient cross-layer design for scheduling and routing in 6TiSCH networks

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computer Communications 2025年 239卷

作者： Hannachi, Ahlam Jaafar, Wael Bitam, Salim Ouazene, Nabil University of Biskra Dept. Computer Science BP 145 RP Biskra Biskra07000 Algeria Dept. Software and IT Engineering 1100 R. Notre Dame W. MontrealQCH3C 1K3 Canada 53. Constantine Road Batna Fesdis05078 Algeria

The 6TiSCH protocol stack plays a vital role in enabling reliable and energy-efficient communications for the Industrial Internet of Things (IIoT). However, it faces challenges, including prolonged network formation, inefficient parent switching, high control packet overhead, and suboptimal resource utilization. To tackle these issues, we propose in this paper a novel cross-layer optimization framework aiming to enhance the coordination between the Scheduling Function (SF), the Routing Protocol for Low-Power and Lossy Networks (RPL), and queue management. Our solution introduces a slot-aware parent switching mechanism, early slot reservation to mitigate queue overflow, and a refined slot locking strategy to improve slot availability. To reduce control overhead, the proposed method merges 6P cell reservation information into RPL control packets (DIO/DAO), thus minimizing control exchanges during parent switching and node joining. Optimized slot selection further reduces latency and jitter. Through extensive simulations on the 6TiSCH simulator and under varying network densities and traffic loads, we demonstrate significant improvements over the standard 6TiSCH benchmark in terms of traffic load, joining time, latency, and energy efficiency. These enhancements make the proposed solution suitable for time-sensitive IIoT applications. © 2025 Elsevier B.V.

关键词： Industrial internet of things (IIoT)

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Real-Time Traffic Sign Detection for Autonomous Vehicles Using YOLOv11

Real-Time Traffic Sign Detection for Autonomous Vehicles Usi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Shahabub Alam Al Sani Md Tanvir Islam Ayan Kumar Ghosh Avijit Chowdhury Department of Computer Science Universität Potsdam Potsdam Germany Dept. of Industrial & Production Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Software Sungkyunkwan University Suwon Republic of Korea Department of Mechanical Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11’s potential for real-world traffic sign detection tasks, where reliability is critical.

关键词： YOLO Analytical models Adaptation models Electric potential Accuracy Noise Robustness Real-time systems Autonomous vehicles Resilience

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Porous Models for Enhanced Representation of Saturated Curly Hairs: Simulation and Learning

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IEEE Access 2025年 13卷 84517-84540页

作者： Kim, Jong-Hyun Lee, Jung Inha University College of Software and Convergence Dept. of Artificial Intelligence Design Technology 100 Inha-ro Michuhol-gu Incheon 22212 South Korea Hanbat National University Department of Computer Engineering 125 Dongseo-daero Yuseong-gu Daejeon 125 South Korea

Simulating the cohesion, adhesion, stiffness, and exaggeration of curls of wet curly hair or fur, expressed through the saturation-hair interaction in physics-based simulations, remains a challenging problem. Wet hair or fur tends to clump and stiffen at the ends, a common phenomenon observed in wet hair or animal fur. Additionally, while wet hair should exhibit adhesion when in contact with solids, the uneven distribution of forces in wet curly hair, manifested as noise, complicates an accurate representation of adhesion. Research into detailed porous models for wet curly hair, driven by saturation-hair interaction, is not yet extensively explored. Previous methods have manually represented wet hair or used static hairstyles for wet curly hair and fur, maintaining shape but resulting in unnatural movement due to the lack of simulation. This paper proposes methods for representing wet curly hair features: 1) curl exaggeration using locally transformed helices, 2) deformation-based cohesion that remains stable in wet curly hair, 3) level-set-based adhesion for efficiently depicting the sticky and elongated forms of wet hair, 4) dynamic stiffness for improved simulation stability, and 5) collecting a detailed synthetic dataset of curly hairs and extending the solver to represent particle movements in wet strands through learning. Experiments in various scenes demonstrate that our proposed methods more realistically represent the saturation-hair interaction compared to previous wet curly hair simulations. Unlike previous methods in which saturation caused cohesion or curls to tangle, our method stably represents porous flow at the strand level. Additionally, we propose to extend the learning representation solver through both numerical simulation algorithms and AI-based approaches. © 2013 IEEE.

关键词： Adhesion Cohesion Exaggeration of curly hair Local-transformed helix Porous model Saturation Stiffness Wet curly hair

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In-dept. Analysis of Automated Acne Disease Recognition and Classification

arXiv

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arXiv 2025年

作者： Jeny, Afsana Ahsan Junayed, Masum Shah Mia, Md Robel Islam, Md Baharul Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of CSE University of Connecticut StorrsCT06269 United States Department of CSE Daffodil International University Dhaka Bangladesh Dept. of Computing & Software Engineering Florida Gulf Coast University Fort MyersFL33965 United States

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L ∗ a ∗ b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods. © 2025, CC BY.

关键词： Expert systems

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Perspective Transition of Large Language Models for Solving Subjective Tasks

arXiv

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arXiv 2025年

作者： Wang, Xiaolong Zhang, Yuanchi Wang, Ziyue Xu, Yuzhuang Luo, Fuwen Wang, Yile Li, Peng Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Gansu China Harbin Institute of Technology Harbin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as commonsense reasoning and arithmetic question-answering, the performance of LLMs on subjective tasks is still limited, where the perspective on the specific problem plays crucial roles for better interpreting the context and giving proper response. For example, in certain scenarios, LLMs may perform better when answering from an expert role perspective, potentially eliciting their relevant domain knowledge. In contrast, in some scenarios, LLMs may provide more accurate responses when answering from a third-person standpoint, enabling a more comprehensive understanding of the problem and potentially mitigating inherent biases. In this paper, we propose Reasoning through Perspective Transition (RPT), a method based on in-context learning that enables LLMs to dynamically select among direct, role, and third-person perspectives for the best way to solve corresponding subjective problem. Through extensive experiments on totally 12 subjective tasks by using both closed-source and open-source LLMs including GPT-4, GPT-3.5, Llama-3, and Qwen-2, our method outperforms widely used single fixed perspective based methods such as chain-of-thought prompting and expert prompting, highlights the intricate ways that LLMs can adapt their perspectives to provide nuanced and contextually appropriate responses for different problems. © 2025, CC BY.

关键词： Open systems

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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CHSH Violations using Dynamic Circuits

arXiv

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arXiv 2025年

作者： Waring, Jean-Baptiste Pere, Christophe Le Beux, Sébastien Dept. of Electrical & Computer Engineering Concordia University MontréalQC Canada Dept. of Computer Science & Software Engineering INTRIQ Laval University QuébecQC Canada PINQ2 Canada

Scalable quantum computing relies on high-quality, long-range entanglement, a challenge on noisy, near-term devices. The need for practical insights for near-term algorithm design calls for trade-offs exploration in implementing dynamic circuits on current hardware. In this work, we experimentally compare three CNOT implementations for generating Bell states across varying qubit separations on a 127-qubit IBM Quantum Eagle processor (ibm_ quebec): a unitary (SWAP-based) approach, a dynamic approach with mid-circuit measurements and classical feedforward, and a post-processed approach. We use Clauser-Horne-Shimony-Holt (CHSH) inequality violations to quantify entanglement quality. We observe that, beyond 10 qubits, dynamic circuits surpass the unitary approach, demonstrating improved distance-dependent entanglement preservation. The post-processed approach consistently yields the highest CHSH values, reaching |S| > 2 up to 13 qubits. Our results underscore the critical need for faster classical feedforward and higher readout fidelity. © 2025, CC BY-NC-SA.

关键词： Qubits

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Classification System Based on Data Mining Technique for Flat-Colored Textiles

SSRN

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SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

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