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Media Power Measuring via Emotional Contagion

Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2412-2426页

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore 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

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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Visual abstraction of dynamic network via improved multi-class blue noise sampling

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Frontiers of computer science 2023年第1期17卷 171-185页

作者： Yanni PENG Xiaoping FAN Rong CHEN Ziyao YU Shi LIU Yunpeng CHEN Ying ZHAO Fangfang ZHOU School of Computer Science and Engineering Central South UniversityChangsha 410083China Institute of Big Data Hunan University of Finance and EconomicsChangsha 410205China Rail Data Research and Application Key Laboratory of Hunan Province Changsha 410083China

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

关键词： dynamic network visualization massive sequence view multi-class blue noise sampling visual abstraction

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification 2

Abnormal Clustering and Cross Slicing Transformer for Insect...

引用

2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Mei, Aokun Huo, Hua Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Big Data Analysis Laboratory of Henan Medical Luoyang471023 China

ISBN: (纸本)9798350390254

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images. © 2024 IEEE.

关键词： Image segmentation

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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Robust and Verifiable Privacy Federated Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1895-1908页

作者： Lu, Zhi Lu, Songfeng Tang, Xueming Wu, Junjun Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

Federated learning (FL) safeguards user privacy by uploading gradients instead of raw data. However, inference attacks can reconstruct raw data using gradients uploaded by users in FL. To mitigate this issue, researchers have combined privacy computing techniques with FL. However, these techniques may not ensure the Byzantine robustness of aggregation or the integrity of the aggregated outcomes. Most current robust privacy FL methods assess differences between gradients and benchmarks in the direction, allowing adversaries to poison the aggregation against the magnitude. Furthermore, these methods cannot ensure the integrity of the aggregation results. To overcome these challenges, this study proposes a novel algorithm, robust and verifiable privacy federated learning (RVPFL), which can more effectively eliminate the poisoning attack of the opponent by measuring the direction and magnitude of the gradient in the ciphertext state. The proposed algorithm guarantees the integrity of server aggregation results while safeguarding user privacy. In this study, comprehensive theoretical analysis and experimental validation of RVPFL are conducted to demonstrate its superiority. The proposed RVPFL algorithm solves the Byzantine robustness problem of aggregation and the integrity problem of aggregation results, which helps to research and develop more robust and effective privacy-preserving federal learning techniques. © 2020 IEEE.

关键词： Learning algorithms

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UCat: heterogeneous memory management for unikernels

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Frontiers of computer science 2023年第1期17卷 51-61页

作者： Chong TIAN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing LabSchool of Computing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address *** this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

关键词： unikernel virtualization non-volatile memory heterogeneous memory large page slab allocation

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Internet of Brain, Thought, Thinking, and Creation

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Chinese Journal of Electronics 2022年第6期31卷 1025-1042页

作者： ZHANG Zhimin YIN Rui NING Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering Research Center for Cyberspace Data Analysis and Applications

Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain(IoB), Internet of thought(IoTh), and Internet of thinking(IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation(IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the Io C stage.

关键词： thinking space IoTk Internet of thinking IoTh Internet of thought cyberspace development Internet of Things Internet of brain cyber-physical systems IoB human civilization emergence

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