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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Modi, Pranit Asgola, Abhishek Mishra, Hiya Charanya, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350328202

Blockchain technology, born as the backbone of cryptocurrencies like Bitcoin, has rapidly expanded into a versatile platform spanning various industries. This review paper delves into the critical aspects of security and anonymity within blockchain applications. It simplifies complex technical concepts while exploring the foundation of blockchain security, including cryptography, decentralization, consensus mechanisms, and the challenges posed by smart contracts. Additionally, it examines the pseudonymous nature of blockchain addresses, privacy-enhancing cryptocurrencies, and the delicate balance between transparency and anonymity. As we navigate these intricacies, we address challenges such as scalability, regulatory concerns, and potential threats from quantum computing. This paper aims to provide a comprehensive yet easily digestible understanding of the security and anonymity dimensions in blockchain applications. © 2024 IEEE.

关键词： Blockchain

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Credit Card Fraud Detection Algorithm Based on Improved Particle Swarm Optimization 9

Credit Card Fraud Detection Algorithm Based on Improved Part...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Qiu, Xun Qin, Weijia Guo, Chaoyang Mao, Wencheng School Of Computer Science Harbin Engineering University Harbin China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350376548

With the popularity of mobile payment, the problem of financial fraud is becoming increasingly prominent, and the limitations of traditional anti-fraud methods make it more difficult to fight fraud. Many fraud detection tools lack generalization and cannot provide services for the majority of enterprises. To solve this problem, this paper introduces and compares the performance of a variety of popular meta-heuristic algorithms in the field of feature selection, in order to pursue the best performance and generalization. In addition, this paper improves the particle swarm optimization algorithm by introducing cost-effectiveness, so that it can show better performance in the feature selection problem. In this paper, through a comprehensive evaluation of the system, a variety of performance indicators and confusion matrix are examined to accurately evaluate the performance of the system. In particular, the potential of the system in practical applications is highlighted, and it is expected to achieve significant results in real scenarios. And through the analysis of detailed performance indicators and confusion matrix, the feasibility and effectiveness of the system in solving practical problems and coping with various challenges are highlighted. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

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computers, Materials & Continua 2022年第2期70卷 3875-3890页

作者： D.Kothandaraman A.Balasundaram R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh School of Computer Science and Artificial Intelligence SR UniversityWarangalTSIndia School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering KCG College of TechnologyChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennaiIndia

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

关键词： Link stability internet of things optimal energy optimal bandwidth residual energy

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Comparative Study of Machine Learning Models and Distributed Runoff Models for Predicting Flood Water Level

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 215-222页

作者： Kubo, Tasuku Okazaki, Takeo Graduate school of Engineering and Science University of the Ryukyus Senbaru Nishihara Japan Department of Computer Science and Intelligent Systems University of the Ryukyus Senbaru Nishihara Japan

Conventional flood forecasting methods can be roughly classified as physics-based models and data-oriented models, which both require parameter optimization. In parameter optimization, a search is generally done to minimize the magnitude of overall errors. However, under- and overestimation errors are not equivalent in flood forecasting since underestimation of the water level leads to delays in decision making. We propose a risk-aware forecasting method that uses a weighted loss function. We applied the proposed method to both physics-based models and machine learning models and compared the prediction results to clarify the difference in the prediction results according to the base model used. The results show that the model optimized by the weighted loss function reduced the underestimation error while maintaining the overall error. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Errors

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection 16

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Malik, Aman Behera, Dayal Kumar Swain, Amulya Ratna Hota, Jhalak Birla Institute of Technology and Science Department of Computer Science & Information Systems Hyderabad India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350371154

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as 'fake news,' has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content. © 2024 IEEE.

关键词： Information dissemination

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Recognition of carrier-based aircraft flight deck operations based on dynamic graph

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Chinese Journal of Aeronautics 2025年第3期38卷 474-490页

作者： Xingyu GUO Jiaxin LI Hua WANG Junnan LIU Yafei LI Mingliang XU School of Computer Science and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China School of Geo-Science&Technology Zhengzhou UniversityZhengzhou 450001China

Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.

关键词： Carrier-based aircraft Flight deck operation Operation recognition Long spatial-temporal trajectories Dynamic graph Biased random walk Graph embeddings

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