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Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

Intelligent Automation & Soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh S.Kannimuthu Computer Science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

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A Combined Approach of Deep Reinforcement Learning and Simulated Annealing for Task offloading in Edge Computing 24

A Combined Approach of Deep Reinforcement Learning and Simul...

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4th International Conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Lin, Ke Liu, Yanggai Zhu, Zilu Li, Lin School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9798400710049

Edge computing paradigms shifts data processing tasks from remote cloud servers to the network’s edge, closer to data origination points. This approach reduces data transmission delays, enhances processing efficiency [1]. To further minimize latency, an effective policy for task offloading onto finite Edge Computing is crucial for maintaining the internet’s high-performance capabilities. However, traditional techniques often fail to achieve equitable task offloading and can converge on suboptimal solutions, leading to increased time and energy consumption. To address these limitations, we propose a novel resource allocation algorithm that leverages the strengths of Deep Reinforcement Learning (DRL) combined with the evolutionary capabilities of the Simulated Annealing (SA). Through simulation experiments, we evaluate the proposed strategy across various metrics, including cost, convergence speed, and reward, demonstrating its significant ability to improve overall system efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Synthetic Collision-Prone Trajectory Data Generation Using CTGAN for Connected Autonomous Vehicles 11

Synthetic Collision-Prone Trajectory Data Generation Using C...

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11th International Symposium on Telecommunication, IST 2024

作者： Samanipour, Ali Javidan, Reza Bushehrian, Omid Shiraz University of Technology Department of Computer Engineering and Information Technology Shiraz Iran

ISBN: (纸本)9798350356250

The safety and reliability of connected autonomous vehicles (CAVs) hinge on their ability to navigate complex and unpredictable driving environments. Traditional testing methods, relying on real-world data and simulations, often fall short of providing the diverse and comprehensive set of collision scenarios needed for rigorous evaluation. On the other hand, mobility trajectory data acquisition is challenging due to privacy concerns, commercial considerations, missing values, and expensive deployment costs. This paper presents a novel approach to generating synthetic collision-prone trajectories using the Conditional Tabular Generative Adversarial Network (CTGAN). By leveraging CTGAN's ability to handle imbalanced and mixed data types, we create a rich synthetic dataset that enhances the validation and testing processes for CAVs. Our methodology includes the preparation of real-world trajectory data, the configuration and training of the CTGAN model, and the generation of a diverse set of collision-prone scenarios. We test our approach using two large-scale real-world trajectory datasets and comparative analyses with other trajectory generation methods, including TimeGAN and SocialGAN, to underscore the superiority of our approach in generating realistic and varied scenarios. This research contributes to the field of autonomous vehicle testing by offering a robust and scalable solution for scenario generation. The synthetic dataset not only improves safety and reliability metrics but also provides a valuable resource for future research and development. Our findings suggest that integrating CTGAN-generated data into CAV testing frameworks, not only in terms of statistical and quality metrics, is better than the other approaches but also can significantly enhance the preparedness of autonomous vehicles, ultimately contributing to safer and more efficient transportation systems. © 2024 IEEE.

关键词： Ability testing

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning

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Chinese Journal of Electronics 2022年第2期31卷 354-366页

作者： LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23%compared with TCA+ algorithm on AEEEM datasets,and an average improvement of 5% on ReLink datasets.

关键词： feature engineering cross-project defect prediction software defect prediction project management feature selection learning (artificial intelligence) software maintenance WCM-WTrA distance-weight transfer learning software quality distance-weight instance transfer multisource model MultiWCM-WTrA

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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A Cascaded Dynamic Attention-Based Deep Learning Model Segments Brain Tumors 12

A Cascaded Dynamic Attention-Based Deep Learning Model Segme...

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12th International Conference on Current Trends in Advanced Computing, ICCTAC 2024

作者： Choudhary, Kartik Singh, Kaptan Saxena, Amit Computer Science & Engineering Truba Institute of Engineering & Information Technology Bhopal India

ISBN: (纸本)9798350395808

Brain tumor diagnosis and treatment need segmentation, Precision automated segmentation of brain tumors is difficult due to their size, shape, unexpected placements, and fuzzy boundaries. U-Net is a popular medical picture segmentation model due to its simple architecture and high performance. Underutilized contextual information, limited local receptive fields, and lost spatial information are issues. For brain tumor segmentation, CDAU-Net, a novel model with Dynamic Convolution and Non-Local Attention Mechanism, addresses these issues. Two-stage cascaded three-dimensional U-Nets rebuild brain tumor spatial information at better resolution. The cascaded network's lateral connections receive maximal attention, improving its ability to collect long-distance interconnections and use tumor context information. Finally, dynamic convolutions with local adaptive capabilities replace normal convolutions in the cascaded network to increase local features capture. Extensive experiments were conducted on publicly available BraTS 2019 and 2020 datasets, comparing the proposed method with other representative approaches. Proposed method segments brain tumors. Brain tumor segmentation dice values of 0.897/0.903 for whole tumor, 0.826/0.828 for tumor core, and 0.781/0.786 for enhanced tumor segmentation worked in the BraTS 2019/2020 validation set. © 2024 IEEE.

关键词： Convolution

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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