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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Safae, Belkhyr Jaouad, Boumhidi Nikolov Nikola, S. University Sidi Mohamed Ben LISAC Laboratory Computer Science Departement Faculty of Science Dhar EI Mahraz Abdellah Fez Morocco University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9798350351200

Deep Reinforcement Learning (DRL) has emerged as a pivotal technology in modern transportation systems, offering considerable potential to enhance driving safety and optimize performance. This study underscores the significance of DRL in autonomous driving (AD) and introduces the Advantage Actor-Critic (A2C) approach for lane change decision-making. A2C, representing policy-based methods, is compared with the Prioritized Replay Deep Q-Network (PRDQN), a value-based approach previously used in similar scenarios. Both approaches were evaluated using the CARLA (Car Learning to Act) simulation environment, focusing on static obstacle scenarios. The comparative analysis aims to assess the performance differences between A2C and PRDQN, highlighting the effectiveness of A2C in improving decision-making accuracy, safety, and adaptability. The results demonstrate the superior performance of A2C in improving safety and decision-making within autonomous driving systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Delegable zk-SNARKs with proxies

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Frontiers of computer science 2024年第5期18卷 201-217页

作者： Jinrui SHA Shengli LIU School of Cyber Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Cryptology Beijing 100878China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

In this paper,we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge(zk-SNARKs).The delegable zk-SNARKKis parameterized by(u,k,k',k").The delegable property of zk-SNARKs allows the prover to delegate its proving ability toμ*** k honest proxies are able to generate the correct proof for a statement,but the collusion of less than k proxies does not obtain information about the witness of the *** also define k'-soundness and k"-zero knowledge by taking into consider of *** propose a construction of(μ,2t+1,t,t)-delegable zk-SNARK for the NPC language of arithmetic circuit *** delegable zk-SNARK stems from Groth's zk-SNARK scheme(Groth16).We take advantage of the additive and multiplicative properties of polynomial-based secret sharing schemes to achieve delegation for *** secret sharing scheme works well with the pairing groups so that the nice succinct properties of Groth's zk-SNARK scheme are preserved,while augmenting the delegable property and keeping soundness and zero-knowledge in the scenario of multi-proxies.

关键词： szk-SNARKs secret sharing delegation bilinear groups

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COVID-19 Patients’ Health Monitoring System using Fuzzy Ontology and Internet of Things

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 191-203页

作者： Hadjadj, Abdelhalim Halimi, Khaled Deptartment of Computer Science LabSTIC Laboratory University 08 Mai 1945 Guelma Algeria Department of Information LabSTIC Laboratory University 08 Mai 1945 Guelma Algeria

COVID-19 is a very dangerous pandemic attacking the respiratory organs of humans. It is characterized by its contagious speed, especially with its last versions. Effectiveness of confrontation resides in a strategy based on the speed of intervention, early detection, and appropriate and quick treatment. However, this strategy requires more effort and enormous human, material, and financial resources. The latter situation requires a much more efficient solution based on using new technologies. Through the integration of the internet of things in healthcare, the quality of the latter will be improved. This integration requires suitable architecture represent the foundation for the system that handles the data generated by this technology. To concretize this strategy, we propose an approach based on IoT architecture inspired by the organization of the Algerian health structure for fighting COVID-19. The architecture allows the organization to manage resources and ensure adequate resources. The approach also consists of a semantic web-based system to handle the heterogeneity of data sources and exchange it with different applications. The system is based on a proposed fuzzy ontology that helps to treat vague and imprecise data that characterize the medical domain. The fuzzy ontology is developed by reusing a standard IoT ontology for allowing the sharing and reuse of IoT data. It also uses the NEWS2 score system for defining membership functions. We conducted an experimental study, and the results of the proposed approach were compared with those obtained by physician assessment. The results show that the approach is effective, and even if COVID-19 disappears soon according to the World Health Organization indications, the proposed approach will still be valid for any other epidemic that may occur in the future or for any other disease. © 2023 Iraqi Journal for computer science and Mathematics. All rights reserved.

关键词： COVID-19

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Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach

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SN computer science 2024年第1期5卷 1-12页

作者： El Hlouli, Fatima Zohra Riffi, Jamal Mahraz, Mohamed Adnane Yahyaouy, Ali El Fazazy, Khalid Tairi, Hamid LISAC Laboratory Department of Computer Science Faculty of Sciences University Sidi Mohammed Ben Abdellah Atlas Fes 30050 Morocco

Credit card fraud detection plays a crucial role in safeguarding the financial security of individuals and organizations. However, imbalanced datasets pose significant challenges to accurately identifying fraudulent transactions. In this research paper, we propose a novel approach that combines autoencoder (AE) and fully connected deep networks (FCDN) models to address this issue. The process involves three phases: training an AE on fraudulent transactions, utilizing another AE for dimensionality reduction, and using the encoded representations as input for FCDN classification. To further enhance the model’s performance, we introduce an additional FCDN trained on the preprocessed data using the synthetic minority oversampling technique (SMOTE). The predictions from both AE, AE–FCDN, and the FCDN are combined using a majority voting approach. We evaluate the proposed method using standard performance metrics, including accuracy, precision, recall, and F1-score. Our experimental results demonstrate the effectiveness and robustness of the integrated model architecture in accurately detecting credit card fraud. These findings provide valuable insights for improving financial security measures and mitigating potential losses associated with credit card fraud. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AE Credit card fraud detection Dimensionality reduction FCDN Majority voting Reconstruction error SMOTE

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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MDT-Net: A Mask Decoder Tuning Strategy for CLIP-Based Zero-Shot 3D Classification 31st

MDT-Net: A Mask Decoder Tuning Strategy for CLIP-Based Zero...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Yan, Hao Bai, Jing The School of Computer Science and Engineering North Minzu University Ningxia Yinchuan750021 China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission Ningxia Yinchuan750021 China

ISBN: (纸本)9789819620609

PointCLIP shows that transferring 2D knowledge to zero-shot 3D shape classification tasks can be effective, even without incorporating 3D-specific knowledge. However, the absence of 3D knowledge limits the model’s performance, leading to efforts focused on improving 3D-to-2D projection algorithms. This work advances previous methods by introducing a learnable fine-tuning strategy, called MDT-Net, for Contrastive Language-Image Pre-training (CLIP) on 3D datasets. MDT-Net freezes the CLIP encoder to keep view features aligned with semantic information and incorporates a shape decoder with masked view consistency and attention-guided fusion, enabling more effective fine-tuning of CLIP for 3D tasks. The decoder receives both raw view features and masked view features. In the shallow layers, a mask consistency constraint is utilized between two types of view features to enhance the model’s generalization and robustness. In the deeper layers, attention-guided fusion is introduced to better capture the contextual information between views and compact all view features into one. MDT-Net is evaluated on different benchmarks of 5 datasets for zero-shot classification, achieving a competitive classification accuracy on ModelNet10, ModelNet40, McGill, and ZS3D datasets. These experiment results demonstrate the effectiveness of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Local Texture Patterns Fusion For Content Based Image Retrieval 9th

Local Texture Patterns Fusion For Content Based Image Retrie...

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9th International Conference on Smart City Applications, SCA 2024

作者： Zekkouri, Hassan Zarouit, Yousra Aksasse, Brahim Ouanan, Mohammed Ouhda, Mohamed Department of Computer Science Faculty of Science and Techniques Errachidia Moulay Ismail University Errachidia Morocco Department of Computer Science Faculty of Sciences and Technics Moulay Ismail University Errachidia Morocco Department of Computer Science Faculty of Science Meknes Moulay Ismail University Errachidia Morocco Department of Computer and Mathematics TIAD Laboratory Higher School of Technology Sultan Moulay Slimane University Khenifra Morocco

ISBN: (纸本)9783031886522

Content-based image retrieval (CBIR) is a method of developing image search engines. It involves searching for images that are similar to a given query image from a large collection of images. This is done by analyzing the content of each image pixel. CBIR is considered a substitute for traditional text-based image retrieval (TBIR), which depends on text annotation for conducting image retrieval. Although TBIR is capable of conducting image retrieval, it is limited by its use of a restricted vocabulary for annotating images, which may not encompass all aspects of the image’s content. This limitation can lead to ambiguity and various interpretations. This results in the imprecise retrieval of images via TBIR. To address these limitations and overcome additional obstacles, instead of using a textual description, CBIR (Content-Based Image Retrieval) utilizes the content of each image to generate a vector description by analyzing the digital image properties such as color, texture, and shape. This work explores the utilization of specific local texture patterns for image vector description. In addition, we employ various similarity/dissimilarity metrics to evaluate the performance of each technique in comparison to the Corel-1k dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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Automatic Face Recognition System Based on Data Augmentation and Transfer Learning 3

Automatic Face Recognition System Based on Data Augmentation...

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3rd International Conference on Theoretical and Applicative Aspects of computer science, ICTAACS 2023

作者： Abdessalam, Hattab Ali, Behloul Batna 2 University LaSTIC Laboratory Computer Science Department Batna Algeria

ISBN: (纸本)9798350385854

In an era marked by the ubiquity of digital identity verification, the realm of biometrics, especially facial recognition, has been a focal point of research. While it has exhibited impressive performance under controlled conditions, its efficacy degrades under uncontrolled environments. To address this challenge, we present a robust face recognition system inspired by Xception model. We harnessed the power of Data Augmentation and Transfer Learning techniques to proficiently train our proposed Deep CNN model on limited face datasets. These dual techniques not only led to heightened recognition accuracy but also served as robust safeguards against overfitting. Through rigorous testing using the Georgia Tech and ORL benchmark datasets, employing a two-fold cross-validation protocol, our system consistently surpasses contemporary state-of-the-art face recognition methods, achieving an outstanding 100% accuracy rate on both datasats. © 2023 IEEE.

关键词： Face recognition

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PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity 30

PAD: A Robustness Enhancement Ensemble Method via Promoting ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Yuting Huang, Pei Ma, Feifei Cao, Juan Li, Jintao China University of Chinese Academy of Sciences China Department of Computer Science Stanford University United States State Key Laboratory of Computer Science Institute of Software CAS China Laboratory of Parallel Software and Computational Science Institute of Software CAS China

ISBN: (纸本)9782493814104

Deep neural networks can be vulnerable to adversarial attacks, even for the mainstream Transformer-based models. Although several robustness enhancement approaches have been proposed, they usually focus on some certain type of perturbation. As the types of attack can be various and unpredictable in practical scenarios, a general and strong defense method is urgently in require. We notice that most well-trained models can be weakly robust in the perturbation space, i.e., only a small ratio of adversarial examples exist. Inspired by the weak robust property, this paper presents a novel ensemble method for enhancing robustness. We propose a lightweight framework PAD to save computational resources in realizing an ensemble. Instead of training multiple models, a plugin module is designed to perturb the parameters of a base model which can achieve the effect of multiple models. Then, to diversify adversarial example distributions among different models, we promote each model to have different attention patterns via optimizing a diversity measure we defined. Experiments on various widely-used datasets and target models show that PAD can consistently improve the defense ability against many types of adversarial attacks while maintaining accuracy on clean data. Besides, PAD also presents good interpretability via visualizing diverse attention patterns. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Deep neural networks

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Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition

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IEEE Transactions on Image Processing 2023年 32卷 2719-2733页

作者： Liu, Wenxuan Zhong, Xian Zhou, Zhuo Jiang, Kui Wang, Zheng Lin, Chia-Wen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan430070 China Peking University School of Electronics Engineering and Computer Science Beijing100091 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University School of Computer Science Wuhan430072 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://***/51cloud/DRDN. © 1992-2012 IEEE.

关键词： Economic and social effects

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