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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 China College of Computer and Information Science Chongqing Normal University Chongqing 401331 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature selection Fuzzy rough set Multi-view Weight reconstruction

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Enrichment, Borrowing, And Mining: A data-Driven Approach To Colonoscopic Lesion Classification

Enrichment, Borrowing, And Mining: A Data-Driven Approach To...

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IEEE International Symposium on Biomedical Imaging

作者： Shuangyi Tan Yuhao Du ZhenHua Wu Dejun Fan Xutao Lin Zhen Li Xiang Wan Guanbin Li The Chinese University of Hong Kong(Shenzhen) Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Gastrointestinal Endoscopy Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases Biomedical Innovation Center The Sixth Affiliated Hospital Sun Yat-Sen University

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to classify colonoscopic lesions. However, they commonly encounter two main issues: 1) Their performance is limited by the dataset size and the diversity of lesion types included; 2) They tend to directly extract deep features from the original Region of Interests (ROIs) of the lesion, neglecting features of the lesion’s edge background as well as detailed texture and color information. To address these issues, we first propose a publicly available colonoscopic lesion classification dataset that includes all major categories of intestinal lesions. Secondly, we innovatively utilize additional colonoscopy video detection data with consistency between frames to enhance the lesion classification model by leveraging an online-target network. Simultaneously, to better mine the characteristics of lesions in data, we propose an ROI broaden strategy and a deep-fusion module, enabling the model to learn lesion-related information such as lesion edges, morphology, texture, and color. Experiments show that our method outperforms existing supervised and semi-supervised state-of-the-art methods, demonstrating the effectiveness of our approach. The GitHub link for our work is https://***/Zoe-TAN/EBM-polyp-cls.

关键词： Image color analysis Image edge detection Prevention and mitigation Colonoscopy Morphology Feature extraction data models

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Dynamic targets searching assistance based on virtual camera priority

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Virtual Reality & Intelligent Hardware 2021年第6期3卷 484-500页

作者： Zixiang ZHAO Quanwei ZHOU Xiaoguang HAN Lili WANG State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100191China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518052China The Chinese University of Hong Kong Shenzhen 999077China Shenzhen Research Institute of Big Data Shenzhen 999077China

Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality *** studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual ***,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more *** We design a virtual camera priority-based assistance to help the user search dynamic targets *** of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for *** assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic *** method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic *** method is suitable for various virtual reality applications that do not require manual supervision or *** A user study is designed to evaluate the proposed *** results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more *** advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task *** We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual cam

关键词： Searching assistance Virtual environment Path guidance Redirection

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A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

A Hybrid Neural Network with Smart Skip Connections for High...

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IEEE International Conference on Multi Topic

作者： Hafsa Wazir Jawad Ahmad Muazzam A. Khan Sana Ullah Jan Fadia Ali Khan Muhammad Shahbaz Khan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Computer Science HITEC University Taxila Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained IoT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen’s kappa coefficient, Matthew’s correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

关键词： Hands Performance evaluation Correlation coefficient Accuracy Neural networks Gesture recognition Electromyography data models Internet of Things Reliability

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services Computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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Ecological Security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

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computers, Materials & Continua 2021年第10期69卷 985-1001页

作者： Xiaozhou Li Zhenggang Xu Yuanyuan Fu Qi Jin Yunlin Zhao Neal NXiong Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha410004China Hunan Urban and Rural Ecological Planning and Restoration Engineering Research Center Hunan City UniversityYiyang413000China College of Information and Electronic Engineering Hunan City UniversityYiyang413000China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.

关键词： Ecological security PSR model evaluation resource-exhausted cities

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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