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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ma, Wenqiang Hau, Yaqing Yu, Hua Tong, Xiangrong Zhu, Zexuan Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350308365

Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem. © 2024 IEEE.

关键词： Optimization algorithms

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Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition

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SN computer Science 2024年第6期5卷 682页

作者： Waziry, Sadaf Rasheed, Jawad Ghabban, Fahad Mahmoud Alsubai, Shtwai Elkiran, Harun Alqahtani, Abdullah Department of Software Engineering Istanbul Aydin University Isanbul 34295 Turkey Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul 34303 Turkey Faculty of Computer Science and Engineering Taibah University Medina Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj 11942 Saudi Arabia

Since the advent of automobiles and driver assistance technologies, traffic sign recognition has been of the utmost importance for Industry 4.0. In the driving system, good data pre-processing is critical. For such objectives, sophisticated transformations or fundamentally computational image processing approaches are out of the question. Convolutional Neural Networks (CNN) have been used to perform more object identification challenges, thus, improving most computer vision applications, both existing and new, because of their excellent recognition rate and rapid execution. This study introduces a method for recognizing traffic signs by utilizing a CNN-based model and the transfer learning concept. The TensorFlow library is used for training the underlying neural network model. The offered approach makes use of the German Traffic Sign Recognition Benchmark (GTSRB) and images from the Traffic Sign Images from Turkey (TSIT) databases. These datasets are dependable and vibrant, and they have been used to train many algorithms. Furthermore, after training the model, the proposed scheme acquired a testing accuracy is 99.44%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CNN German traffic sign Image processing Transfer learning

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Inpainting Technique for Removing Vehicle Logos Using Haar-Like and Fast Marching Methods 8

Inpainting Technique for Removing Vehicle Logos Using Haar-L...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Durmus, Meryem Ergen, Burhan Durmus, Omer Atasoy, Ferhat Samsun University Center for Distance Education Samsun Turkey Firat University College of Engineering Department of Computer Engineering Elazig Turkey Samsun University College of Engineering Department of Software Engineering Samsun Turkey Karabuk University College of Engineering Department of Computer Engineering Karabuk Turkey

ISBN: (纸本)9798331531492

Inpainting is the process of removing an unwanted object or region in an image without degrading the image quality. Inpainting offers a solution to problems such as repairing damaged images or removing unwanted parts of the image. In this study, we focus on the detection of brand logos in vehicle images and the removal of these regions using inpainting. The study consists of two main phases: First, the Haar-like features method is used to detect vehicle logos. Then, the detected logos are removed from the image by applying inpainting with the Fast Marching Method. As a result of the experimental studies, it was found that the methods successfully detected and removed the logos with an accuracy of 98%, and the PSNR value ranged between 25.36 dB and 25.41 dB, and the SSIM value ranged between 0.96692 and 0.96994 while maintaining the image quality. These results show that the proposed method exhibits high performance in removing vehicle logos efficiently. © 2024 IEEE.

关键词： fast marching method haar-like inpainting logo recognition

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Experiment Design to Evaluate MVDC Cable Pole to Ground Arc Fault Models

Experiment Design to Evaluate MVDC Cable Pole to Ground Arc ...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Cuppen, Andre N. Nair, Nirmal-Kumar C. Powerco New Plymouth New Zealand The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

Arcing faults in medium voltage DC (MVDC) cables are foreseen to more severely exert cables and network equipment, as well as have a higher potential to harm people and property by arc-flash and fire than their MV AC equivalent. Understanding the dynamics of the fault transients and detailed analysis of the fault current under different types of faults is essential for designing robust protection logic as well as choosing the optimal grounding scheme. Such analysis requires MVDC arc fault models, suitable to be included in EMT simulations at circuit level, with the focus on MVDC cables, since such models are not available for MVDC cables and components. This paper discusses available models to simulate the static and dynamic behavior of MVDC cable arc faults. The behavior of several MV AC cable faults is qualitatively analyzed. Electrical characteristics of arcing faults in MV cable, relevant models, experiments and field measurements results are critically analyzed before recommending experiments designed to arrive at an empirical and realistic MVDC cable arc model, suitable for EMT and circuit simulations. © 2024 IEEE.

关键词： Power cables

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A Method to Evaluate User-Centered Self-Adaptive Systems for Smart Environments 10

A Method to Evaluate User-Centered Self-Adaptive Systems for...

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10th International Conference on eDemocracy and eGovernment, ICEDEG 2024

作者： Avila, Marcelo Quito, Christian Valdez, Wilson Cardenas-Delgado, Paul Cedillo, Priscila Universidad de Cuenca Computer Science Department Cuenca Ecuador University of Szeged Software Engineering Department Szeged Hungary

ISBN: (纸本)9798350365535

With the advent of Industry 4.0, Ambient Intelligence, and other related technologies, self-Adaptation has been seen in modern systems. In this sense, Human-computer Interaction (HCI) requires an approach focused on the user and the environment, which facilitates and enhances the use of the systems. In this context, the quality of Self-Adaptive Systems (SAS) is essential for their adoption, dissemination, and service. Thus, this study introduces a detailed evaluation method to analyze the quality of HCI in SAS, providing a solution applicable to various contexts aligned with the standard ISO/IEC 25040. This study also presents a quality model aligned with the ISO/IEC 25010 standard. The quality evaluation method allows the objective definition for the measurement application through specific metrics that determine the degree of functional adequacy and interaction capability. Finally, this study demonstrates how to perform a detailed evaluation of the quality of HCI in SAS, using as an example the evaluation of the Spotify system as a case study, applying metrics to evaluate functional adequacy and interaction capacity aligned with ISO/IEC 25040 and 25010 standards. © 2024 IEEE.

关键词： ISO Standards

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LSNSCDA: Unraveling CircRNA-Drug Sensitivity via Local Smoothing Graph Neural Network and Credible Negative Samples

LSNSCDA: Unraveling CircRNA-Drug Sensitivity via Local Smoot...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Ziyu Zhang, Yuanpeng Li, Yahan Zhong, Zeyu Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Wulumuqi China

ISBN: (纸本)9798350386226

This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://***/ZiyuFanCSU/LSNSCDA. © 2024 IEEE.

关键词： Graph neural networks

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Modelling Algal Blooms Triggered by Different Phytoplankton Species with Synergetic Control Theory 10

Modelling Algal Blooms Triggered by Different Phytoplankton ...

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Polyak, Mark Shchegoleva, Aleksandra Saint Petersburg State University Of Aerospace Instrumentation Department Of Computer Technologies And Software Engineering St. Petersburg Russia

ISBN: (纸本)9798350372939

In this work application of the latest achievements of synergetic control theory to the construction of new algal bloom models is considered. A three-component model of zooplankton and phytoplankton is used as the basic model, where phytoplankton is split in two components: toxic and non-toxic. Interactions between species are based on a predator-prey model and competitive Lotka-Volterra equations. Analytical design of aggregated regulators method is applied in order to build controllable and robust systems that can be used to estimate the volume of algal blooms and calculate its timing. © 2024 IEEE.

关键词： Integral equations

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TFFSQL:Enhancing Graph Neural Network through Text Feature Fusion for Text-to-SQL 24

TFFSQL:Enhancing Graph Neural Network through Text Feature F...

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

作者： He, Yaozhen Huang, Enpei Qi, Rongzhi College of Computer Science and Software Engineering Hohai University China United International College Beijing Normal University China

ISBN: (纸本)9798400710049

In text-to-SQL, generating accurate SQL queries is crucial. Existing methods that rely on Graph Neural Networks (GNN) still fall short in capturing complex semantic and relational information. We propose TFFSQL, which obtains the textual information of natural language questions as node features and integrates them with the original node features. It also adopts segmentation processing to handle long queries, generates vectors for each segment, and merges them with the node structural features, effectively capturing the semantic information of natural language queries and improving the efficiency and accuracy of the model in processing long queries. An cross-graph attention is introduced in the Relational Graph Attention Network (RGAT). Through the cross-graph attention, the model can dynamically adjust attention weights between the original graph and the line graph, thereby capturing rich relational information. Additionally, TFFSQL also combines recursive decoding with the ASTormer decoder for decoding. Experimental results show that our method outperforms existing GNN-based methods on the Spider dataset, significantly improving the accuracy of SQL query generation. © 2024 Copyright held by the owner/author(s).

关键词： Structured Query Language

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Analyzing the Impact of Preprocessing Techniques on the Efficiency and Accuracy of Sentiment Classification Algorithms 15

Analyzing the Impact of Preprocessing Techniques on the Effi...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Nikhila Kanigiri, Sai Mekuriyaw, Chachi Goodman, Garrett Alexiou, Michail S. Kennesaw State University Department of Computer Science MariettaGA United States Miami University Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350368833

In the era of data-driven decision-making, the ability to rapidly and accurately analyze vast amounts of textual data is essential. This paper examines the role of text pre-processing techniques in enhancing the effectiveness of sentiment classification algorithms. Such algorithms are pivotal in interpreting the emotional tone of texts and serving as a preliminary step towards reverse-engineering complex data systems. Through comprehensive analysis, we assess the impact of various preprocessing strategies such as keyword removal, lemmatization, and stemming and their combination on the performance of both traditional and advanced machine learning models, including Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers (BERT). The current study evaluates the balance between efficiency and accuracy in sentiment analysis algorithms, illustrating the impact of preprocessing techniques. In addition, it aims to serve as a guideline for selecting preprocessing methods that optimize model performance in both sentiment analysis and textual data reverse-engineering. © 2024 IEEE.

关键词： Adversarial machine learning

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