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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable Systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China 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 convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

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An age-structured tuberculosis model with information and immigration:Stability and simulation study

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International Journal of Biomathematics 2023年第2期16卷 153-176页

作者： Lili Liu Jian Zhang Yazhi Li Xinzhi Ren Shanci Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Complea Systems Research Center Shanci University Taiyuan 030006P.R.China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun 558000 P.R.China School of Mathematics and Statistics Southwest University Chongqing 400715P.R.China

This paper establishes an age-structured Tuberculosis(TB)model to investigate the joint impacts of information and immigration of population on the spread of TB ***,we show that the model is point dissipative,and the semi-flow generated by the model has the property of asymptotic smoothness,and then study the existence and global stability of positive steady state by the direct Lyapunov ***,by using Matlab software,we verify the theoretical results,and further explore the influence of information(including information coverage and disease-related memory delay)and immigration on the final size of TB *** simulation results show that both information coverage and immigration are positive correlated with the final size of disease,and disease-related memory delay can affect the arrival time of positive steady state,which implies us that improving information coverage,enlarging disease-related memory,and reducing the immigration of population(especially latent and infected individuals)can effectively control the progression of TB disease.

关键词： TB transmission model age-structured information-related immigration global stability.

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Adaptive Control for Multi-Agent Systems Based on Dynamic Event-triggered Mechanism: In Different Topologies 7

Adaptive Control for Multi-Agent Systems Based on Dynamic Ev...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Huang, Yu Zhang, Huiyan Chen, Zixian Zou, Jiangyan Chongqing Technology and Business University School of Mechanical Engineering Chongqing400067 China Chongqing Technology and Business University National Research Base of Intelligent Manufacturing Service Chongqing400067 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. Chongqing400707 China Chongqing Technology and Business University School of Artificial Intelligence Chongqing400067 China

ISBN: (纸本)9798350363173

Under different communication topologies, this paper investigates the consensus problem for multi-agent systems with general linear dynamics under leader following. A distributed dynamic event-triggered control strategy with output feedback adjustment is proposed by using adaptive control technology of auxiliary variables, and it is applied to different follower communication topologies. The simulation results show that the leader-following systems under different communication modes of the followers can solve the completely distributed consensus problem. Moreover, the Zeno behavior of each multi-agent can be excluded. Finally, the effectiveness of the proposed control strategy is verified by taking multi-vehicle cooperative tracking control as an example. © 2024 IEEE.

关键词： Multi agent systems

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis 1

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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Differences in pseudogene evolution contributed to the contrasting flavors of turnip and Chiifu, two Brassica rapa subspecies

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Plant Communications 2023年第1期4卷 120-135页

作者： Xin Yin Danni Yang Youjie Zhao Xingyu Yang Zhili Zhou Xudong Sun Xiangxiang Kong Xiong Li Guangyan Wang Yuanwen Duan Yunqiang Yang Yongping Yang Plant Germplasm and Genomics Center Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650204China Institute of Tibetan Plateau Research at Kunming Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnanChina University of Chinese Academy of Sciences Beijing 100049China

Pseudogenes are important resources for investigation of genome evolution and genomic diversity because they are nonfunctional but have regulatory effects that influence plant adaptation and ***,few systematic comparative analyses of pseudogenes in closely related species have been ***,we present a turnip(Brassica rapa ***)genome sequence and characterize pseudogenes among diploid Brassica species/*** results revealed that the number of pseudogenes was greatest in Brassica oleracea(CC genome),followed by ***(AA genome)and then Brassica nigra(BB genome),implying that pseudogene differences emerged after species *** Brassica AA genomes,pseudogenes were distributed asymmetrically on chromosomes because of numerous chromosomal insertions/rearrangements,which contributed to the diversity among *** differences among subspecies were reflected in the flavor-related glucosinolate(GSL)***,turnip had the highest content of pungent substances,probably because of expansion of the methylthioalkylmalate synthase-encoding gene family in turnips;these genes were converted into pseudogenes in *** ***(Chiifu).RNA interference-based silencing of the gene encoding 2-oxoglutarate-dependent dioxygenase 2,which is also associated with flavor and anticancer substances in the GSL pathway,resulted in increased abundance of anticancer compounds and decreased pungency of turnip and *** findings revealed that pseudogene differences between turnip and Chiifu influenced the evolution of flavor-associated GSL metabolism-related genes,ultimately resulting in the different flavors of turnip and Chiifu.

关键词： turnip genome comparative genomics pseudogene evolution GSL biosynthesis flavor

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research on Technologies in data Fabric

Research on Technologies in Data Fabric

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Qingyuan Hu Zheng Yin Tao Tao Jibin Wang Zhuo Chen Bohuan Ai Yu Liu Chongzhou Liu China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the continuous advancement of technologies like big data, artificial intelligence, and cloud computing, enterprises are increasingly encountering challenges related to the heterogeneity of data from various sources, difficulties in querying data assets, and understanding business insights. To address these issues, data fabric emerges as a solution that leverages distributed data management. By harnessing metadata and facilitating its activation, data fabric enables organizations to efficiently and accurately manage and locate data assets. However, it is important to note that this concept is still under development and faces obstacles such as establishing connections with additional data sources and unifying semantics. Nonetheless, overall, the concept of data fabric exhibits remarkable potential in assisting enterprises in transitioning towards becoming data-driven organizations.

关键词： Industries Cloud computing Technological innovation Soft sensors Standards organizations Distributed databases Collaboration

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Displacement-Sparse Neural Optimal Transport

arXiv

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arXiv 2025年

作者： Chen, Peter Xie, Yue Zhang, Qingpeng Department of Mathematics Columbia University New York United States HKU Musketeers Foundation Institute of Data Science Hong Kong HKU Shanghai Intelligent Computing Research Center Shanghai China

Optimal Transport (OT) theory seeks to determine the map T : X → Y that transports a source measure P to a target measure Q, minimizing the cost c(x, T(x)) between x and its image T(x). Building upon the Input Convex Neural Network OT solver (Makkuva et al., 2020;Amos et al., 2017) and incorporating the concept of displacement-sparse maps (Cuturi et al., 2023), we introduce a sparsity penalty into the minimax Wasserstein formulation, promote sparsity in displacement vectors ∆(x):= T(x) − x, and enhance the interpretability of the resulting map. However, increasing sparsity often reduces feasibility, causing T#(P) to deviate more significantly from the target measure. In low-dimensional settings, we propose a heuristic framework to balance the tradeoff between sparsity and feasibility by dynamically adjusting the sparsity intensity parameter during training. For high-dimensional settings, we directly constrain the dimensionality of displacement vectors by enforcing dim(∆(x)) ≤ l, where l d. Among maps satisfying this constraint, we aim to identify the most feasible one. This goal can be effectively achieved by adapting our low-dimensional heuristic framework without resorting to dimensionality reduction. We validate our method on both synthesized sc-RNA and real 4i cell perturbation datasets, demonstrating improvements over existing methods. © 2025, CC BY.

关键词： Dimensionality reduction

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Scalable Transactional Stream Processing on Multicore Processors

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IEEE Transactions on Knowledge and data Engineering 2025年第7期37卷 4254-4269页

作者： Zhao, Jianjun Mao, Yancan Yang, Zhonghao Liu, Haikun Zhang, Shuhao Huazhong University of Science and Technology 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 Wuhan5430074 China National University of Singapore 119077 Singapore Nanyang Technological University 639798 Singapore

Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables adaptive scheduling under varying workload characteristics. Building on this foundation, MorphStream is further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally, MorphStream incorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system's ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios. © 1989-2012 IEEE.

关键词： Multicore programming

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