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International Conference on Pattern Recognition

作者： Wenshuang Liu Wenting Chen Yuanlue Zhu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR). The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-lM) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

关键词： Training Face recognition Generators

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Innovative Urban Planning for Harnessing Blockchain and Edge Artificial Intelligence for Smart City Solutions

Innovative Urban Planning for Harnessing Blockchain and Edge...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： K. E. Purushothaman N. Ragavendran S. P. Ramesh V. Ganesh Karthikeyan G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing (SoC) SASTRA Deemed to be University Thanjavur Tamil Nadu India RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research explores the potential of blockchain and edge AI to revolutionize smart city planning and management. By incorporating these emerging technologies, cities can address current urban challenges, including traffic congestion, energy management, and data security. This research study highlights the benefits of blockchain, such as simplified processes, reduced costs, enhanced security, and transparency. Additionally, this study reviews the applications of edge AI in smart transportation, focusing on vehicle detection, counting, and identification algorithms. The potential of blockchain technology in smart energy trading, vehicle transfer, and encrypted communication is also discussed. Overall, this research study provides valuable insights for researchers to explore new application areas for blockchain and edge AI in the context of smart cities.

关键词： Smart cities Vehicle detection Scalability Urban planning Edge AI Blockchains Smart transportation Energy management Sustainable development Traffic congestion

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Research on Multi Beam Sounding Based on Spatial Position Relationship and Backtracking Algorithm

Research on Multi Beam Sounding Based on Spatial Position Re...

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control, Electronics and computer Technology (ICCECT), IEEE International Conference on

作者： Bensen Xian Bo Liu Yufen Li Baicheng Zhu Qiuyan Chen Jinlong Diao Wenkai Sun Jiahuan Mo Shaoyuan Bai Ya'nan Zhang College of Environmental Science and Engineering Guilin University of Technology Guilin China School of Computer Science and Engineering Guilin University of Technology Guilin China School of Mathematics and Statistics Guilin University of Technology Guilin China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of Technology Guilin China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

The multi beam depth measurement system is an efficient underwater terrain measurement method, which has wide applications in marine terrain surveys, underwater archaeology, and other fields. Therefore, exploring the problem of multi beam survey lines is of great research significance for the exploration of marine disciplines. This article mainly uses SketChup for drawing. With the help of mathematical geometry and triangle theory, a seawater depth calculation model, a coverage width calculation model, and an optimal survey line design model are constructed. The indicator values of each measurement point are solved using Python. This paper establishes a mathematical model for the coverage width of multi beam depth measurement and the overlaps rate between adjacent bands. Firstly, the spatial structure is analyzed as a planar Fig. Then, this paper uses a plan for geometric analysis to obtain right angled triangles with similar ratios. Through physical analysis, a seawater depth measurement model was established. Then, geometric analysis is performed using the obtained planar Fig to obtain a right angled triangle with similar ratios. Then, a depth measurement model for seawater is obtained through physical angle analysis. The coverage width measurement model and the repetition rate measurement model are established using the sine theorem and equal substitution. Finally, using Python, we calculate the index values for the listed positions with an open angle of 120°, a slope of 1.5°, and a center point seawater depth of 70m. For example, the depth, coverage width, and overlap rate of the measuring line at a distance of 200m from the center point are 64.763m, 224.885m, and 13.837%, respectively.

关键词： Surveys Geometry Analytical models Area measurement Sea measurements Position measurement Length measurement

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Reversible Data Hiding Based on Run-Level Coding in H.264/AVC Video Streams

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computers, Materials & Continua 2020年第5期63卷 911-922页

作者： Yi Chen Hongxia Wang Xuyun Zhang School of Information Science and Technology Southwest Jiaotong UniversityChengdu611756China College of Cybersecurity Sichuan UniversityChengdu610064China Department of Electrical Computer and Software EngineeringUniversity of AucklandAuckland1023New Zealand

This paper presents a reversible data hiding(RDH)method,which is designed by combining histogram modification(HM)with run-level coding in H.264/advanced video coding(AVC).In this scheme,the run-level is changed for embedding data into H.264/AVC video *** order to guarantee the reversibility of the proposed scheme,the last nonzero quantized discrete cosine transform(DCT)coefficients in embeddable 4×4 blocks are shifted by the technology of histogram *** proposed scheme is realized after quantization and before entropy coding of H.264/AVC compression ***,the embedded information can be correctly extracted at the decoding ***-signal-noise-to-ratio(PSNR)and Structure similarity index(SSIM),embedding payload and bit-rate variation are exploited to measure the performance of the proposed *** results have shown that the proposed scheme leads to less SSIM variation and bit-rate increase.

关键词： Reversible data hiding run-level coding visual quality bit-rate increase H.264/AVC video streams

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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MQWAGS: Research on Job Scheduling Algorithms Based on Cloud Computing 20

MQWAGS: Research on Job Scheduling Algorithms Based on Cloud...

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4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Qiu, Gang Jiang, Liping Zhang, Yajun College of Computer Science and Technology Shandong University Jinan China Changji College Department of Computer Engineering Changji China School of Information Science and Engineering Shandong University Jinan China School of Software Xinjiang University Wulumuqi China

ISBN: (纸本)9781450375603

With the rapid development of digital technology, from the application of traditional databases and scientific computing to the emerging cloud computing services, the analysis, and processing of massive data has become the focus of society. Providing low-cost, scalable, and configurable shared cloud services to users on cloud service platforms is a new hotspot for the development of major cloud service providers. Job scheduling plays an important role in improving the overall system performance of cloud service capabilities. Simple job scheduling strategies (such as Fair and FIFO scheduling) do not consider job size and may degrade performance when jobs of different sizes arrive. This paper proposes the MQWAG (Multi-Queue Load-Sensitive Greedy Scheduling Algorithm) job scheduling algorithm to reorder multi-queue jobs so that short jobs are executed preferentially in multiple queues. In our experiments, our algorithm shortened the average job completion time by about 26% compared with other algorithms. © 2020 ACM.

关键词： Scheduling

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and Technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation

USBDAN: Unsupervised Scale-aware and Boundary-aware Domain A...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yongtao Zhang Ning Yuan Bing Liu Aocai Yang Hongwei Yu Kuan Lv Jixin Luan Pianpian Hu Haijun Lei Tianfu Wang Guolin Ma Baiying Lei School of Biomedical Engineering Health Science Centers Shenzhen University Department of Medical Imaging Heping Hospital Affiliated to Changzhi Medical College Changzhi China Department of Radiology China-Japan Friendship Hospital Beijing China School of Computer Science and Software Engineering Shenzhen University Shenzhen China

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.

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The Role of AI in Healthcare Policy Development and Management

The Role of AI in Healthcare Policy Development and Manageme...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Sapna Yadav Pravin Mane Virendra Kumar Swarnkar Shital kumar Rawandale Alaknanda S. Patil Kiran S. Katke Niyati Bhat Entrepreneurship Mindset Curriculum State Council of Educational Research and Training Delhi Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development Pune Department of Computer Science & Engineering Bharti Vishwavidyalaya Durg Chhattisgarh Dean Industry Institute Interaction PCET's Pimpri Chinchwad College of Engineering Pune Maharashtra India E&TC department JSPM NTC Pune Department of Computer Engineering Sanjivani College of engineering Kopergaon Ahmednagar Software Developer Master of Science california state university long beach CA USA

This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms

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Efficient Byzantine-Robust Federated Learning Based on the Multi-Message Shuffle Protocol for Consumer Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Jin, Chunhua Li, Lulu Wang, Jiahao Li, Jinbiao Liu, Junyi Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer Software Engineering with Huaiyin Institute of Technology Huai’an China Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai’an China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Jinan University College of Information Science and Technology China

The accelerated advancement of consumer Internet of Things (CIoT) devices has resulted in their pervasive integration into the fabric of contemporary lifestyles. As these devices are capable of collecting vast quantities of data pertaining to users, they present a significant risk to the privacy and security of individuals. Federated learning is an advanced machine learning paradigm that prioritises the preservation of user data privacy and enhances security by eliminating the need for centralised data collection. As a result, it is a promising approach to address user data privacy and security concerns. However, federated learning still faces challenges such as privacy attacks, Byzantine attacks and high communication overheads. To address these challenges, we propose an efficient Byzantine-robust federated learning method based on multi-message shuffle protocol. To achieve privacy protection and Byzantine robustness, we introduce an innovative aggregation strategy inspired by the multi-message shuffle protocol for differential privacy. To improve communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis indicates that our multi-message shuffle protocol is capable of satisfying the requisite differential privacy conditions. Compared with the state-of-the-art methods, our proposed scheme demonstrates superior convergence capabilities on the real-world dataset when approximately 40% of participants are Byzantine, achieving accuracies of 96.78%, which illustrates its strong robustness. Upon reduction of the transmitted model parameters to 1% of the original size, the proposed scheme exhibits a maximum accuracy degradation of 0.35%. which significantly reduces the communication overhead while achieving high accuracy. In addition, the scheme also achieves an accuracy of 88.73% on the Non-iid MotionSense dataset, which is superior to other schemes. © 2014 IEEE.

关键词： Federated learning

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