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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Shukla, Amar Mahdi, Hussain Falih Tandon, Ishita Singh, Rajkamal Choudhury, Tanupriya School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Generative adversarial networks (GANs) are capable of learning deep representations efficiently without requiring a significant amount of annotation. Through Backpropagation, signals are derived from one network by another through a competitive process. This article presents a machine learning framework that incorporates generative adversarial networks (GANs) and interactive evolutionary computation (IEC) to generate images and to resample those images for the detection of fake images. In our study, we found that GANs trained on a particular domain could produce reliable and compact phenotypic maps. Through the use of a user research method, participants able to produce images closely matched target images, demonstrating the advantages of this unique approach. © 2022 IEEE.

关键词： Generative adversarial networks

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A Web-Based Vaccine Distribution System for Covid-19 Using Vaxallot 4th

A Web-Based Vaccine Distribution System for Covid-19 Using V...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Valarmathi, B. Srinivasa Gupta, N. Prakash, G. BarathyKolappan, A. Padmavathy, N. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Systems Engineer Tata Consultancy Services Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488900

Vaxallot seeks to implement a system to distribute vaccines across high-risk groups accounting for various parameters and prove to be superior to what conventional systems are capable of today. It is a Python flask-based tool backed by infrastructure and data resources from the Covid India central repository;all it needs is a single channel input and a single parameter of the value produced, and the algorithm will take care of the rest. Since it’s Python-based and has an active integration with google sheets, live value updating could be possible for the real-time output of the distribution. The novelty of the proposed mechanism is the unique priority index, a score that accounts for an array of factors associated with the pandemic and is computed for regions in question here;this makes way for better distribution of vaccines. The application has an exclusive segment centered on handling excess units, if any. Moreover, since the application is developed to suit the needs of dynamic demographics, any region can roll out this application for purposes they desire to serve the masses. Since it isn’t bound by a coronavirus, it can be used by the healthcare industry as they deem fit. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Coronavirus

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

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

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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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DALC: Distributed Arithmetic Coding Aided by Linear Codes

arXiv

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arXiv 2025年

作者： Zhou, Junwei Xiao, HaoYun Xiang, Jianwen Lin, Qiuzhen The School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Software Engineering Shenzhen University China

Distributed Arithmetic Coding (DAC) has emerged as a feasible solution to the Slepian-Wolf problem, particularly in scenarios with non-stationary sources and for data sequences with lengths ranging from small to medium. Due to the inherent decoding ambiguity in DAC, the number of candidate paths grows exponentially with the increase in source length. To select the correct decoding path from the set of candidates, DAC decoders utilize the Maximum A Posteriori (MAP) metric to rank the decoding sequences, outputting the path with the highest MAP metric as the decoding result of the decoder. However, this method may still inadvertently output incorrect paths that have a MAP metric higher than the correct decoding path, despite not being the correct decoding path. To address the issue, we propose Distributed Arithmetic Coding Aided by Linear Codes (DALC), which employs linear codes to constrain the decoding process, thereby eliminating some incorrect paths and preserving the correct one. During the encoding phase, DALC generates the parity bits of the linear code for encoding the source data. In the decoding phase, each path in the set of candidate paths is verified in descending order according to the MAP metric until a path that meets the verification criteria is encountered, which is then outputted as the decoding result. DALC enhances the decoding performance of DAC by excluding candidate paths that do not meet the constraints imposed by linear codes. Our experimental results demonstrate that DALC reduces the Bit Error Rate(BER), with especially improvements in skewed source data scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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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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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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Neighborhood-Enhanced Multimodal Collaborative Filtering for Item Cold Start Recommendation

Neighborhood-Enhanced Multimodal Collaborative Filtering for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohui Li Li Zou Zhiying Deng Qi Chen School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

The lack of interaction data of new items in recommendation systems leads to the problem of cold-start item recommendations. Current methods usually approximate content features of the items to interaction embeddings and then use content features for prediction. However, these methods typically learn content features with the same item’s interaction embeddings, resulting in sparse supervised signals for content features and suboptimal recommendation performance. To address this issue, we propose a novel approach called Neighborhood-Enhanced Multimodal Collaborative Filtering (NEMCF) that captures the neighborhood of content features and interaction embeddings in order to enrich the content features. Specifically, we first design a dual graph neural network to aggregate interaction embeddings and content features of co-occurring neighbors of items. Then, we use the aggregated interaction embeddings and content features of items to supplement the content features via contrastive learning. Finally, to enhance the robustness of NEMCF, we randomly employ item content features and interaction embeddings for recommendations. Extensive experimental studies on four benchmark datasets demonstrate that the NEMCF method outperforms several typical cold-start methods and achieves state-of-the-art results.

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