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Question-Answering (QA) Model for a Personalized learning Assistant for Arabic Language 1st

Question-Answering (QA) Model for a Personalized Learning As...

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1st international conference on intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Sammoudi, Mohammad Habaybeh, Ahmad Ashqar, Huthaifa I. Elhenawy, Mohammed Department of Natural Engineering and Technology Sciences Arab American University Ramallah240 Palestine Civil Engineering Department Arab American University Palestine and Columbia University JeninNY240 United States CARRS-Q Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783031823763

this paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. the model was particularly fine-tuned on science textbooks in Palestinian curriculum. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. the model's ability to understand and extract pertinent information is improved by fine-tuning it using 11th and 12th grade biology book in Palestinian curriculum. this increases the model's efficacy in producing enlightening responses. Exact match (EM) and F1 score metrics are used to assess the model's performance;the results show an EM score of 20% and an F1 score of 51%. these findings show that the model can comprehend and react to questions in the context of Palestinian science book. the results demonstrate the potential of BERT-based QA models to support learning and understanding Arabic students’ questions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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VaryMorph, a Simple Tool to Automatically Generate Legged Robot Morphologies 11

VaryMorph, a Simple Tool to Automatically Generate Legged Ro...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Jaffal, Oussama Taghizadeh, Zahra Cezayirlioglu, Melike Li, Weipeng Wu, Xuan Melo, Kamilo KM-RoBoTa Renens Switzerland China Nanhu Academy of Electronics and Information Technology Robotics Laboratory Zhejiang Jiaxing China

ISBN: (纸本)9798331509293

Efficient and robust design of legged robots for diverse terrains remains a critical focus in robotics research. To streamline this process we present varyMorph, an open-source tool that programmatically generates diverse legged robot models in both URDF and USDA formats. By randomizing properties such as leg count, morphology, body dimensions, and mass distribution, varyMorph creates hundreds or thousands of valid robot designs in few minutes. Users can seamlessly integrate these models into popular simulators (e.g., Isaac Sim/Lab, MuJoCo, Isaac Gym, RaiSim), accelerating large-scale experimentation in evolutionary robotics and reinforcement learning. Our results show near-linear scalability with respect to complexity and batch size, achieving rapid generation on both high-performance and standard desktop hardware. this high-throughput morphological exploration facilitates data-driven optimization and supports novel research on locomotion control, sim - to- real transfer, and co-evolutionary algorithms. We envision varyMorph as a complementary resource for grammar-based, latent-space, and gradient-driven design frameworks, reducing the barrier to discovering innovative and robust robot morpholoaies. © 2025 IEEE.

关键词： Reinforcement learning

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Development Trends of Big data and Artificial Intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

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14th international conference on Frontier Computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. Big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. the application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. the experimental results indicate that the sample sizes are 100, 200, and 300. the time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Manual Prompt engineering is Not Dead: A Case Study on Large Language Models for Code Vulnerability Detection with DSPy 8

Manual Prompt Engineering is Not Dead: A Case Study on Large...

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8th international conference on data Science and Machine learning Applications, CDMA 2025

作者： Trad, Fouad Chehab, Ali Electrical and Computer Engineering American University of Beirut Beirut Lebanon

ISBN: (纸本)9798331539696

automated prompt engineering tools have recently emerged as a promising solution to simplify the traditional man-ual task of crafting prompts for large language models (LLMs). this study investigates whether such tools can fully replace manual prompt engineering for code vulnerability detection. We leverage the DSPy (Declarative Self-improving Python) frame-work, which uses modular 'signatures' rather than prompts to specify tasks. In DSPy, a signature defines the expected input-output behavior of a task and can optionally include a description to guide the model's objective. this signature is then auto-matically translated into an optimized prompt through DSPy's modules and optimizers. this study compares the performance of GPT-4o-mini on basic and detailed signatures to determine how DSPy optimizations affect model performance in each case. the basic signature prompts the model to classify code as vulnerable or not, while the detailed signature specifies particular vulnerabilities to identify. For each signature, we used DSPy's modules and optimizers to create three prompt configurations: zero-shot (baseline, where the model performs the task without examples), chain-of-thought (where the model shows step-by-step reasoning), and bootstrap few-shot (where the model is guided through a small set of examples). Results show that DSPy's automated optimizations improve performance for both signature types over the zero-shot baseline;however, detection performance increases significantly with detailed signatures. Specifically, the detailed signatures achieved higher F1 scores, with improvements of approximately 23 % for zero-shot, 13 % for chain-of-thought, and 11 % for bootstrap few-shot techniques. these findings indicate that while automated tools enhance prompt efficiency, optimal results are achieved by combining automated techniques with human-crafted signature details, underscoring the ongoing importance of manual refinement in specialized tasks. © 2025 IEEE.

关键词： Code Vulnerability Detection DSPy Large Language Models Prompt engineering

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Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation 11

Predicting Depth Maps from Single RGB Images and Addressing ...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Chaar, Mohamad Mofeed Raiyn, Jamal Weidl, Galia Connected Urban Mobility Faculty of Engineering University of Applied Sciences Aschaffenburg Germany

ISBN: (纸本)9789897587450

Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle’s surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Image segmentation

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

the integration of the Internet of things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. this paves the way for a wide variety of intelligent IoT applications to be developed. this article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. these include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 IEEE.

关键词： Federated learning

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Intrusion Detection Using Convolutional Neural Network: A Color Mapping Approach on NSL-KDD dataset 24

Intrusion Detection Using Convolutional Neural Network: A Co...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Faiaz, Md. Abrar Mitra, Dipankar Prangon, Ranat Das Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

Converting any kind of data to image data can make the dataset suitable for Convolutional Neural Networks (CNNs). In this study, the NSL-KDD dataset was converted to image data using the color mapping technique and using CNN a good accuracy of 98.91% and aggregated f1 score of 0.91 was achieved. Here an image representation of each row was generated using both Hue-Saturation-Value (HSV) and Viridis colormap. though some attack types were misclassified by the model, no attack sample of the validation dataset was classified as normal. For model training, five CNN architectures were evaluated by transfer learning from their pre-trained weights. It was found that ResNet18 performs best among all the five architectures evaluated. ResNet18 uses 1x1 convolution to reduce the number of parameters used. It means that for the classification of the colormap of this dataset complex CNN architectures are not necessary. Fine-tuning of the ResNet18, the best-performing architecture, was done using 50 epochs using an optimal learning rate. However, the accuracy mentioned above was found only after 4 epochs demonstrating good efficiency of the model training. the NSL-KDD dataset contains information about network intrusion in tabular format. Hence, this model can be used for intrusion detection purposes. © 2024 Copyright held by the owner/author(s).

关键词： Network intrusion

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the cross-subject locomotion transition prediction during previous double-stance phases with SHAP feature selection 24

The cross-subject locomotion transition prediction during pr...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Zhang, Guanzheng Ji, Zhimin Wang, Ziyao Meng, Lin Xu, Rui Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

ISBN: (纸本)9798400718274

the recognition of human locomotion modes is crucial for the development of intelligent assistive devices. the high accuracy and early prediction of conversion recognition were of crucial importance for the improvement of the performance of these devices. However, the application of locomotion recognition based on prior knowledge is not a viable option in practical contexts. the objective of this study was to investigate the ability of the data during two double-stance phases (FC-1 and FC-0) for cross-subject locomotion transition recognition under baseline classification strategy and mode-specific classification strategy. Ten healthy participants were recruited and their sEMG and IMU signals were collected while they performed locomotion modes, including three steady-state modes and four transitional modes. the features were extracted from two phases: FC-0 (foot contact before transition) and FC-1 (foot contact before FC-0). A preliminary verification of the baseline classification strategy with 9 channels as inputs indicated an optimal cross-subject recognition accuracy of 84.68%. the mode-specific classification strategy with 26 channels as inputs achieved the best recognition accuracy of 84.32%. Furthermore, there was no unified feature set that could achieve the optimal classification performance. the FC-0 phase data proved to be more suitable for the recognition of the locomotion modes of stair descent. Both FC-0 and FC-1 can achieve high-precision recognition of the transition locomotion modes when going upstairs. In conclusion, the findings of this study indicated that the two phases of data exhibited distinct advantages in recognizing locomotion transitions under two classification strategies. Future research may enhance recognition accuracy by integrating the two types of data or by further optimizing algorithms. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Anticipatory Locomotor Adjustment

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data-Driven Analysis of Bicycle Lane Safety in Mexican Cities: Towards a Real-Time Route Recommendation System for Cyclists 11

Data-Driven Analysis of Bicycle Lane Safety in Mexican Citie...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Domínguez-Báez, Carlos Alberto Mendoza-González, Ricardo Luna-García, Huizilopoztli Tecnológico Nacional de México IT Aguascalientes Av. Adolfo López Mateos 1801 Ote. Aguascalientes Mexico Universidad Autónoma de Zacatecas Ramón López Velarde 801 Zacatecas Mexico

ISBN: (纸本)9789897587450

this study initiated a project to identify urban cycling routes with a focus on cyclist safety in Mexican cities. A data-Driven Analysis approach was implemented to map the riskiest and safest cycling routes by analysing traffic accident data from national, state, and local datasets. the accident hotspots were visually integrated into the urban map of Guadalajara city (Jalisco, Mexico), to identify high-risk zones for cyclists. the integration of diverse data sources and geospatial analysis allowed for an accurate characterization of accident patterns, providing a clear identification of critical areas. Key results from this initial stage of the project included an accurate risk-zones identification, a replicable methodology for data integration, and a first approach to developing algorithms for cyclist accident analysis. these preliminary findings hold promise for enhancing urban cycling safety and supporting urban eco-mobility strategies in Mexican cities. Additionally, the results served as a foundation for future exploration of machine learning techniques to refine data processing and develop a real-time safe bicycle lane recommender prototype aimed at guiding cyclists toward safer alternatives. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Recommender systems

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D-LeDe: A data Leakage Detection Method for Automotive Perception Systems 11

D-LeDe: A Data Leakage Detection Method for Automotive Perce...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Babu, Md Abu Ahammed Pandey, Sushant Kumar Durisic, Darko Koppisetty, Ashok Chaitanya Staron, Miroslaw Research and Development Volvo Car Corporation Gothenburg Sweden Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Department of Computer Science and Engineering University of Gothenburg Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9789897587450

data leakage is a very common problem that is often overlooked during splitting data into train and test sets before training any ML/DL model. the model performance gets artificially inflated with the presence of data leakage during the evaluation phase which often leads the model to erroneous prediction on real-time deployment. However, detecting the presence of such leakage is challenging, particularly in the object detection context of perception systems where the model needs to be supplied with image data for training. In this study, we conduct a computational experiment to develop a method for detecting data leakage. We then conducted an initial evaluation of the method as a first step on a public dataset, "Kitti", which is a popular and widely accepted benchmark dataset in the automotive domain. the evaluation results show that our proposed D-LeDe method are able to successfully detect potential data leakage caused by image similarity. A further validation was also provided to justify the evaluation outcome by conducting pair-wise image similarity analysis using perceptual hash (pHash) distance. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Leak detection

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