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Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

computers, Materials & Continua 2024年第1期78卷 899-914页

作者： Chengsheng Yuan Baojie Cui Zhili Zhou Xinting Li Qingming Jonathan Wu Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of International Relations National University of Defense TechnologyNanjing210039China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable ***,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake *** of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial *** addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual *** response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for *** ridge texture area within the fingerprint image has been identified and designated as the region for perturbation ***,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient ***,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack *** results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

关键词： FLD adversarial attacks adversarial examples gradient optimization transferability

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework 63

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anand, Pallov Aguiar, A. Pedro Sujit, P.B. Universidade Do Porto SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Porto Portugal Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal462060 India

ISBN: (纸本)9798350316339

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic Programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments. © 2024 IEEE.

关键词： Multi agent systems

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Optimizing Industrial System From Machine Learning to Digital Twin-Driven Predictive Maintenance

Optimizing Industrial System From Machine Learning to Digita...

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2024 International Conference on microelectronics, ICM 2024

作者： Ezzahra, Labchiri Fatima Zineb, Hidila Ayoub, Fentis Monteiro, Fabrice Abdoul, Rjoub Abdennasser, Amana Casablanca Morocco Research Center Mundiapolis Casablanca Morocco Lgipm Lorraine University Nancy France Jordan University of Science and Technology Computer Engineering Department Irbid Jordan

ISBN: (纸本)9798350379396

In the context of Industry 4.0, It is undeniable to have an optimized system capable to detect equipment failure. For this purpose, Machine Learning models are used as a decision making approaches to this problematic. Furthermore, we generated a synthetic dataset to validate the approaches. This dataset represents real world of industrial applications and gives data for prior estimation of a failure of a machine based on several variables such as temperature, torque, rotational speed, and tool wear. We trained and compared six machine learning models a Random Forest, XGBoost, SVC, K-Nearest Neighbors, Logistic Regression, and Multi-Layer Perceptron on their performance in predicting machine failures. The study shows that Random Forest was the most accurate model that recorded an overall accuracy of 99% making it suitable to be used in predictive maintenance within the IIoT environment. Finally, this research demonstrates how synthetic data enhances predictive maintenance solutions. © 2024 IEEE.

关键词： Predictive maintenance

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On LMIs for switching systems with switching constraints described by digraphs and non-uniform dwell-time ranges 63

On LMIs for switching systems with switching constraints des...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Pepe, Pierdomenico University of l'Aquila Center of Excellence for Research Dews Department of Information Engineering Computer Science and Mathematics Via Vetoio Coppito i L'Aquila67100 Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316339

In this paper novel linear matrix inequalities (LMIs) are provided for checking the exponential stability of continuous-time linear switching systems. Novelty arises from the possibility of efficiently addressing, by these LMIs, constraints in switching as described by a switches digraph and dwell-time ranges for each mode, which are allowed to be non-uniform. Switches digraphs and dwell-time ranges may allow stability even in case of unstable subsystems in the family. The provided LMIs give the chance to show stability in this rather generally and naturally constrained case. The results provided in this paper are the continuous-time counterpart of analogous ones recently obtained for the linear discrete-time case. The utility of provided LMIs is shown with two numerical examples concerning controller failures. © 2024 IEEE.

关键词： Continuous time systems

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Ground-Glass Lung Nodules Recognition Based on CatBoost Feature Selection and Stacking Ensemble Learning

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Journal of Shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and Stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. Second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCSNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © Shanghai Jiao Tong University 2024.

关键词： Feature Selection

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Leveraging Edge Computing for Enhancing Next Generation Network Services 15

Leveraging Edge Computing for Enhancing Next Generation Netw...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Naveena, S.Sathiya Goyal, Prakhar Narmadha Verma, Devika A. Balaji, N.V. Rampal, Sourav Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Aspect computing is quickly turning into a possible answer for augmenting and streamlining the overall performance of next era community services. Edge computing gives a disbursed computing architecture that allows computation and records storage competencies to are living closer to the edge network, rather than in, valuable records middle. This enables applications to enjoy faster reaction times and reduced latency, growing the rate of facts shipping. Side computing also presents greater scalability, simplifying the combination of IoT gadgets, permitting statistics streaming from more than one asset. As networks pass to 5G, area computing will offer the important flexibility and stability to deal with big amounts of statistics visitors securely and successfully. By leveraging the power of edge computing, corporations can create an agile and resilient service structure, imparting more advantageous safety and a variety of customizable abilities that permit them to capitalize on the success of the 5 G community. © 2024 IEEE.

关键词： Abilities Advantageous Capitalize Customizable Permit Streaming Success Variety

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Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance 5th

Dynamic Multihead Attention for Enhancing Neural Machine Tra...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9789819719228

The research paper delves into the realm of "Attention-Based Neural Machine Translation for Multilingual Communication", focusing on the revolutionary impact of attention mechanisms in enhancing cross-language understanding. Through dynamic allocation of focus to pertinent segments of source sequences, attention mechanisms have significantly improved the precision and coherence of translations, playing a pivotal role in multilingual communication. The paper underscores the importance of this technology in addressing the complexities of aligning words across diverse languages and maintaining grammatical accuracy, crucial for effective cross-lingual interactions. The study also presents a visual flowchart outlining the core components of attention-based NMT, encompassing the encoder, decoder, and attention context. Moreover, the paper extrapolates the future trajectory of attention-based NMT, identifying areas for continued exploration and innovation. researchers are urged to refine attention models to cater to intricate linguistic structures, potentially unveiling novel mechanisms that strike a balance between capturing long-range dependencies and computational efficiency. Furthermore, the paper anticipates the integration of attention-based NMT with multimodal inputs, unlocking the potential for comprehensive communication by incorporating visual and auditory cues. These advancements hold the promise of revolutionising multilingual communication, shaping a future where language barriers are transcended in various domains, from global diplomacy to education and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural machine translation

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-31页

作者： Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner. © 2023 Copyright held by the owner/author(s).

关键词： Visualization

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An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

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Alexandria engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

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