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检索条件"机构=School of Computer Science& Software Engineering"

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Hands and Palms Recognition by Transfer Learning for Forensics: A Comparative Study 23

Hands and Palms Recognition by Transfer Learning for Forensi...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Fkiri, Obeb Mejri, Mohamed Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuebecQCG1V 0A6 Canada Department of Computer Science National School of Engineers of Carthage University of Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

In the realm of forensic science, precise identification of individuals holds paramount importance in both investigative procedures and legal proceedings. Hands and palms recognition has emerged as a valuable biometric modality within forensic applications, owing to the distinct and intricate features inherent to these anatomical regions. The elaborate patterns of veins, creases, and ridges present on palms and fingers serve as rich sources of biometric data, crucial for accurate identification purposes. Furthermore, given the frequent involvement of hands and palms in criminal activities such as theft and assault, their recognition becomes imperative for establishing links between suspects and crime scenes. However, developing robust recognition systems tailored for forensic applications poses notable challenges, including variations in hand poses, lighting conditions, and image quality. To address these hurdles, sophisticated deep learning techniques, notably transfer learning, have been employed. By harnessing pre-trained deep learning models namely NasNetLarge, NasNetMobile, and EfficientNet, initially trained on expansive datasets for general image recognition tasks, we can adapt these models to the specific task of hands and palms recognition in forensic contexts. Our findings reveal that all three models consistently achieved over 92% accuracy across all metrics evaluated, demonstrating their efficacy as strong contenders for the hands-and-palms recognition task. Notably, the EfficientNet model exhibited superior performance compared to its counterparts, boasting more than 95.8% accuracy, precision, F1-score and recall, along with more than 98.6% specificity and 99.4% AUC. © 2024 IOS Press. All rights reserved.

关键词： Palmprint recognition

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An Intelligent Defect Detection Algorithm for PCB based on Deep Learning 6

An Intelligent Defect Detection Algorithm for PCB based on D...

引用

6th International Conference on Advances in Image Processing, ICAIP 2022

作者： Zhu, Xiangyuan Xiao, Xiuchun Lan, Zhiming Hong, Qiuhong Hou, Miao School of Computer Science and Software Zhaoqing University China College of Electronics and Information Engineering Guangdong Ocean University China

ISBN: (纸本)9781450397155

As an essential component of modern machines, printed circuit board (PCB) is widely used in various electronic products. Its quality significantly affects the quality of products. However, the production process of PCB is often accompanied with defects. In this paper, a defect detection algorithm is proposed. Data augmentation such as flipping, shifting, brightness adjustment, rotation, and Guass noise are carried out to diversify the dataset. You only look once (YOLO) v5s is then introduced to detect the PCB defects. Through parameter tuning and optimization, a trained detection model is achieved. F1-score and mean average precision (mAP) are used to assess the performance of the model. The experiment results show that the mAP and F1-score are 99.3% and 99.0%, respectively. The model developed based on YOLO-v5s algorithm can achieve superior performance, which is competent to detect the defects of PCBs. © 2022 ACM.

关键词： Printed circuit boards

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A Multi-objective Evolutionary Algorithm Based on Piecewise Parallel Decomposition for Portfolio Optimization 12th

A Multi-objective Evolutionary Algorithm Based on Piecewise ...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Wu, Yu Wei, Jianle Ying, Weiqin Cui, Zhen Pan, Xuanda Wang, Zhenyu School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9789811941085

Due to the nature of multiple objectives, the portfolio optimization problems based on the Mean-Variance model are very suitable to be solved by multi-objective evolutionary algorithms (MOEAs). However, most of the existing MOEAs do not consider the preferences of decision-makers. Meanwhile, there are certain limitations to the traditional crossover and mutation strategy in the exploration of population diversity. To overcome these disadvantages, this paper proposes a multi-objective evolutionary algorithm based on piecewise parallel decomposition (MOEA-PPD). The algorithm takes the largest Sharpe ratio as the piecewise marker of parallel decomposition for portfolio optimization problems. In addition, a neuro-evolution strategy is applied to better approximate a global non-dominated front in the early stage. Then a crossover and mutation strategy is used to enhance the convergence of the obtained non-dominated front in the later stage. Experimental results show that the proposed algorithm is competitive in terms of diversity and convergence for portfolio optimization. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Multi-objective Evolutionary Architecture Search of U-Net with Diamond Atrous Convolution 12th

Multi-objective Evolutionary Architecture Search of U-Net wi...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Ying, Weiqin Yang, Kaihao Wu, Yu Li, Junhui Zhou, Zhekun Huang, Banban School of Software Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789811941085

U-Net and its variants have played important roles in the field of medical image segmentation. However, U-Nets based on conventional 3 * 3 convolution still have some shortcomings, such as the lack of deformation of receptive field. In addition, due to the limited computing resources and memory space on many machines, the allowed sizes of networks deployed on them are also limited. However, it may not be effective to manually design the architectures of U-Nets. In this paper, a U-Net architecture with diamond atrous convolution (DAU-Net) is presented. Furthermore, a multi-objective neural architecture search method with channel sorting of DAU-Net is proposed to search for the better U-Net architectures. Experimental results on the ISIC 2018 dataset of melanoma segmentation show that the proposed method obtains a series of network architectures with different sizes, and the obtained architectures achieve obvious improvements in term of both model sizes and prediction accuracies compared with several popular and manually designed variants of U-Net. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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BlockWhisper: A Blockchain-Based Hybrid Covert Communication Scheme with Strong Ability to Evade Detection 24th

BlockWhisper: A Blockchain-Based Hybrid Covert Communication...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Zehui Xu, Yuwei Huang, Ranfeng Fan, Xinhe Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Software College Northeastern University Shenyang China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819615247

In light of the pervasive significance of privacy protection in contemporary society, the exploration of covert communication technology has been extensive, which is designed to safeguard communication behaviors and contents from disclosure. The decentralization, anonymity, and robustness of blockchain technology render it a more suitable medium for covert communication than traditional networks, which are vulnerable to tracing and destruction. Nevertheless, state-of-the-art blockchain-based covert communication schemes tend to focus on the concealment of a single type of constituent element in a blockchain transaction. These methods are able to resist detection methods for a particular element, yet are easily exposed under the joint detection of multiple classes of methods. The absence of resistance to joint detection methods targeting all elements presents a significant challenge that impedes the advancement of blockchain-based covert channels. Consequently, BlockWhisper is proposed as a blockchain-based hybrid covert channel that is capable of evading the recognition of various detection methods. Following the probability distribution model obtained from a blockchain transaction distribution analysis method, BlockWhisper is implemented using a top address list and a special hybrid coding method. The experiments demonstrated that Graph-based Detection (GD), Similarity-based Detection (SMD), Shape-based Detection (SPD), Entropy-based Detection (ETD), and Machine Learning-based Detection (MLD) yielded inferior results on this scheme, which substantiates the assertion that BlockWhisper possesses a strong ability to evade detection. Moreover, a comparison of BlockWhisper with other schemes reveals that it has a superior channel capacity on the basis of the guarantee of channel feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Covert communication Evading detection

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Boundary-Aware Temporal Dynamic Pseudo-Supervision Pairs Generation for Zero-Shot Natural Language Video Localization 39

Boundary-Aware Temporal Dynamic Pseudo-Supervision Pairs Gen...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Deng, Xiongwen Tang, Haoyu Jiang, Han Zheng, Qinghai Zhu, Jihua School of Software Engineering Xi’an Jiaotong University China School of Software Shandong University China College of Computer and Data Science Fuzhou University China

ISBN: (纸本)157735897X

Zero-shot Natural Language Video Localization (NLVL) aims to automatically generate moments and corresponding pseudo queries from raw videos for the training of the localization model without any manual annotations. Existing approaches typically produce pseudo queries as simple words, which overlook the complexity of queries in real-world scenarios. Considering the powerful text modeling capabilities of large language models (LLMs), leveraging LLMs to generate complete queries that are closer to human descriptions is a potential solution. However, directly integrating LLMs into existing approaches introduces several issues, including insensitivity, isolation, and lack of regulation, which prevent the full exploitation of LLMs to enhance zero-shot NLVL performance. To address these issues, we propose BTDP, an innovative framework for Boundary-aware Temporal Dynamic Pseudo-supervision pairs generation. Our method contains two crucial operations: 1) Boundary Segmentation that identifies both visual boundaries and semantic boundaries to generate the atomic segments and activity descriptions, tackling the issue of insensitivity. 2) Context Aggregation that employs the LLMs with a self-evaluation process to aggregate and summarize global video information for optimized pseudo moment-query pairs, tackling the issue of isolation and lack of regulation. Comprehensive experimental results on the Charades-STA and ActivityNet Captions datasets demonstrate the effectiveness of our BTDP method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Domain Knowledge Graph Completion Based on Attribute Hierarchy 23

Domain Knowledge Graph Completion Based on Attribute Hierarc...

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Proceedings of the 2023 7th International Conference on Electronic Information Technology and computer engineering

作者： Ning Lan Shuqun Yang Department of Computer Science Software Engineering Institute of Guangzhou China School of Electronic and Electric Engineering Shanghai University of Engineering Science China

ISBN: (纸本)9798400708305

Knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing Knowledge graph completion methods mainly embed entities and relations into latent vectors, and numerous researches have taken the rich relationships in knowledge graph into account such as category, entity, relation and semantic. However, only a few studies consider the relation between attributes, which is the basis of describing the entity. This paper proposes the Attribute Hierarchy Knowledge Graph Completion (AH-KGC) method, aiming at leveraging the attribute relation to find the missing obligatory property of entities. Primarily, in AH-KGC, we have discussed the attributes prerequisite relation, which can be described as a tree-like hierarchical structure, and then adopt the search algorithm of preorder traversal based on the hierarchy to find out the missing attributes. Specifically, we prove that attribute prerequisite is a special case of implication, thus can obtain attribute hierarchy from implications relation, which can easily be obtained in much mature research such as expert systems and make full use of the knowledge in various fields to make up for the vacancy of domain knowledge in the knowledge graph. The experiment has been performed on the CN-DBpedia and Probase datasets. The result demonstrates that AH-KGC can effectively complete the missing attributes of entities in knowledge graphs and achieve 100% accuracy under our evaluation system.

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection 38

Dissect Black Box: Interpreting for Rule-Based Explanations ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Yu Li, Ruoyu Wu, Nengwu Li, Qing Lin, Xinhan Hu, Yang Li, Tao Jiang, Yong Shanghai Artificial Intelligence Laboratory China Peng Cheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China Tsinghua University China Tsinghua Shenzhen International Graduate School China Hunan University of Science and Technology China

In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions. The SCD-Tree integrates predictions from the anomaly detection model into its splitting criteria, enhancing the clustering process with the model's insights into anomalies. To further refine these segments, the Gaussian Boundary Delineation (GBD) algorithm is employed to define boundaries within each segmented distribution, effectively delineating normal from anomalous data points. At this point, this approach addresses the curse of dimensionality by segmenting high-dimensional data and ensures resilience to data variability and perturbations through flexible boundary fitting. We transform the intricate operations of anomaly detection into an interpretable rule's format, constructing a comprehensive set of rules for understanding. Our method's evaluation on diverse datasets and models demonstrates superior explanation accuracy, fidelity, and robustness over existing method, proving its efficacy in environments where interpretability is paramount. © 2024 Neural information processing systems foundation. All rights reserved.

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A Novel Hybrid Scheme for Time Series Prediction Using LMS Filter and ISSA-based LSTM 19

A Novel Hybrid Scheme for Time Series Prediction Using LMS F...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Pei Heng Chen, Dong Xia Huang, Han Yao Wu, Xiao Tong Jiang, Hua Wei Ji, Gen Lin Nanjing Normal University School of Computer Science and Technology Nanjing China Nanyang Institute of Technology Department of Computer and Software Engineering Nanyang China Sungkyunkwan Univeristy Department of Computer Science Engineering Suwon Korea Republic of

ISBN: (纸本)9798350346558

In the era of Internet of Everything (IoE), there is an explosive growth in data volumes and the data usually with time series characteristics. Therefore, how to deal with time series data to improve prediction accuracy remains a critical issue. In this paper, an efficient hybrid scheme combining linear and nonlinear models is proposed to greatly improve the prediction performance and simplify the hyperparameter selection process. Gradient-based least mean square (GD-LMS) is introduced for adaptive linear section, while a hyperparameter-determined long short-term memory (LSTM) is used for nonlinear section. In particular, an improved sparrow search algorithm combining opposition-based learning and Cauchy variance (ISSA) is used to construct the optimal hyperparameters for the two-layer LSTM. computer simulations with the UMass weather dataset reveal that the proposed scheme considerably outperforms three benchmarking methods in terms of data prediction accuracy and robustness. © 2022 IEEE.

关键词： Long short-term memory

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