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IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Weihui Qiu Xin Yang Ming Wei Wei Ren Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Wuhan Institute of Marine Electric Propulsion CSSC P.R. China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Institute of Information Engineering Beijing P.R. China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang P.R. China

ISBN: (纸本)9781665400374

Internet of Things (IoT) current present two trends with respect to ubiquity and mobility. The number of devices increases remarkably and most forthcoming devices are mobile, e.g., Internet of Vehicles (IoV). In large scale mobile IoT, the management of device identification, fast authentication, and certification of public keys experiences upcoming difficulties. Centralized security architecture may not be suitable and in contrast cross-domain security architecture will tackle the ubiquity and mobility better. We note that blockchain is a new decentralized architecture equipped with basic cryptographic settings, which provides new tools such as token and guarantees many security properties such as block integrity. In this paper we design a blockchain-based fast authentication video data forwarding scheme for IoV. Compared with existing protocols, it has the advantages of decentralization, high authentication efficiency, strong trust, and resistance to common attacks. We evaluate our scheme with real experiments over Hyperledger Fabric and the authentication delay is manageable (e.g, 4 seconds in IoV).

关键词： Resistance Protocols Authentication public key computer architecture Near field communication Ubiquitous computing

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RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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Deep Domain Adaptation for Pavement Crack Detection

arXiv

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arXiv 2021年

作者： Liu, Huijun Yang, Chunhua Li, Ao Huang, Sheng Feng, Xin Ruan, Zhimin Ge, Yongxin College of Computer Science Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Chongqing City Management College Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China College of Computer Science And Engineering Chongqing University of Technology Chongqing400054 China Co. Ltd Chongqing400060 China

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Crack detection

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A Fragile Watermarking Algorithm for Speech Authentication by Modifying Least Significant Digits

A Fragile Watermarking Algorithm for Speech Authentication b...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Qing Qian Yunhe Cui School of Information International Joint Research Center for Data Science and High-Performance Computing Guizhou University of Finance and Economics Guiyang China School of Computer Science and Technology Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Speech content is often faced with malicious forgery and tampering in transit. Therefore, we introduce a novel fragile watermarking algorithm by modifying the least significant digits for speech content authentication and tampering recovery. In this letter, the proposed authentication scheme consists of two processes: the first one is watermark generation and embedding, the second one is content authentication and recovery. In the former process, the compressed speech signal, frame number and check information are generated based on speech content to form a watermark. Subsequently, the generated watermark is embedded into the least significant digits of speech sampling points using the proposed watermarking algorithm. In the latter process, the embedded watermark is utilized to locate the tampered area and recover that tampered content synchronously. Experimental results show that the proposed watermarking algorithm is fragile to common signal processing. Additionally, it has favorable inaudibility, accurate location ability and high recovery quality.

关键词： Watermarking Authentication Speech coding Signal to noise ratio Speech processing

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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年 332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding

Discriminative Additive Scale Loss for Deep Imbalanced Class...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Weiming Jiang Yang Wang Qiaolin Ye Mingbo Zhao Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China AI team Shanghai Shizhuang Information Technology Co. Ltd Shanghai China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Information Science and Technology Donghua University Shanghai China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

关键词： Deep learning Training Representation learning Additives databases Soft sensors Conferences

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Dual-domain decoupled fusion network for semantic segmentation of remote sensing images

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Information Fusion 2025年 124卷

作者： Li, Xin Xu, Feng Zhang, Jue Yu, Anzhu Lyu, Xin Gao, Hongmin Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Engineering and Built Environment Griffith University Nathan Australia School of Surveying and Mapping Information Engineering University Zhengzhou China School of Information and Communication Technology Griffith University Nathan Australia

Semantic segmentation of remote sensing images (RSIs) is a challenging task due to the complexity of spatial structures, diverse object scales, and heterogeneous land-cover patterns. Traditional approaches often struggle to effectively balance fine-grained boundary details and global contextual understanding, especially for high-resolution images. In this paper, we propose DDFNet, a novel dual-domain decoupled fusion network, to address these challenges. DDFNet integrates spatial and frequency domain features through a dynamic decoupling and fusion strategy. Specifically, we introduce a Dual-Domain Decoupled Feature Fusion (DDFF) module that selectively combines high- and low-frequency components from both domains, enabling the model to capture local textures and global semantics. To further enhance segmentation accuracy, we design a High-Order Geometric Prior Generation (HGPG) module, which utilizes gradient and curvature information to improve boundary precision and maintain geometric consistency. Extensive experiments on three benchmark datasets — ISPRS Vaihingen, ISPRS Potsdam, and LoveDA — demonstrate that DDFNet achieves state-of-the-art performance. Ablation studies validate the contributions of the DDFF and HGPG modules, and efficiency analysis shows DDFNet's strong adaptability to different computational constraints. © 2025 Elsevier B.V.

关键词： Attention mechanism Feature fusion Remote sensing images Semantic segmentation

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Attribute-Based Searchable Encryption Scheme Supporting Efficient Range Search in Cloud Computing

Attribute-Based Searchable Encryption Scheme Supporting Effi...

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IEEE Conference on Dependable and Secure Computing

作者： Yuan Li Haiyan Wang Shulan Wang Yong Ding College of Computer Science and Software Engineering Shenzhen University Shenzhen P. R. China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen P. R. China College of Big Data and Internet Shenzhen Technology University Shenzhen P. R. China Guangxi Key Laboratory of Cryptography Information Security Guilin P. R. China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.

关键词： Cloud computing data privacy Search methods Conferences Encryption Performance analysis Security

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Prevention of external force damage to overhead power lines based on network and intelligent positioning

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 551-583页

作者： Xie, Bo-Lin Pan, Jeng-Shyang Lin, Yuh-Chung Xie, Yu-Hong Pan, Tien-Szu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Science and Technology Sanda University Shanghai201209 China Guangdong Power Grid Heyuan Power Supply Bureau Transmission Management Office China Southern Power Grid Heyuan517000 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan

In the research on the prevention of external damage to power transmission lines, it is a difficult problem to prevent large machinery vehicles from damaging the overhead transmission lines. After the emergence of video surveillance technology, the accident rate has dropped significantly. But still unable to completely eliminate such accidents. The anti-external damage solution based on network and intelligent positioning is very effective for early warning of accidents. This gives us the opportunity to completely eliminate such accidents. In this paper we utilize the satellite positioning data of the power towers to establish a warning zone along the power line. When the system detects that some large machinery vehicles enter the warning zone, early warning messages will be sent to these vehicles. Afterward, the connection between vehicle drivers and management center can be established such that further instructions can be ordered to avoid accidents. According to our simulations, it presents our proposed mechanism can effectively prevent the damage occurrences which may be caused by large machinery vehicles. © 2021 Journal of Network Intelligence.

关键词： Accidents

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Cryptanalysis and improvement of a remote three-factor authentication protocol for the multi-server environment 2nd

Cryptanalysis and improvement of a remote three-factor authe...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Chen, Chien-Ming Huang, Yanyu Deng, Xiaoting Wu, Tsu-Yang Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Fujian University of Technology Fuzhou350118 China

ISBN: (纸本)9783030045845

Nowadays, a multi-server environment has been widely used in various applications. Under this environment, users can register in one particular register center just one time and log into different application servers. Recently, various authentication protocol for this kind of environment have been proposed. In 2017, Zhang et al. proposed a three-factor authentication protocol with strong robustness. They claimed that their protocol can secure against various kinds of attacks, including perfect forward secrecy. Unfortunately, in this paper, we demonstrated that Zhang et al.’s protocol still cannot provide Perfect Forward Secrecy. To erase the weakness, we proposed an improvement to let the protocol can provide Perfect Forward Secrecy. © Springer Nature Switzerland AG 2019.

关键词： Authentication

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