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arXiv 2020年

作者： Lin, Yating Wong, Kamkwai Wang, Yong Zhang, Rong Dong, Bo Qu, Huamin Zheng, Qinghua MOEKLINNS Lab Xi’an Jiaotong University China Hong Kong University of Science and Technology Hong Kong National Engineering Lab of Big Data Analytics Xi’an Jiaotong University China

Tax evasion is a serious economic problem for many countries, as it can undermine the government’s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the respective trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data and interviews with domain experts. Copyright © 2020, The Authors. All rights reserved.

关键词： Taxation

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China 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 Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Universal deep network for steganalysis of color image based on channel representation

arXiv

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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Multi-Omics and Its Clinical Application in Hepatocellular Carcinoma:Current Progress and Future Opportunities

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Chinese Medical Sciences Journal 2021年第3期36卷 173-186页

作者： Wanshui Yang Hanyu Jiang Chao Liu Jingwei Wei Yu Zhou Pengyun Gong Bin Song Jie Tian Department of Nutrition School of Public HealthAnhui Medical UniversityHefei 230032China Department of Radiology West China HospitalSichuan UniversityChengdu 610041China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information TechnologyBeijing 100191China Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Life Science and Technology Xidian UniversityXi'an 710126China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Hepatocellular carcinoma(HCC)is the sixth most common malignancy and the fourth leading cause of cancer related death *** covers over half of cases,leading HCC to be a vital threaten to public *** advances in diagnosis and treatments,high recurrence rate remains a major obstacle in HCC ***-omics currently facilitates surveillance,precise diagnosis,and personalized treatment decision making in clinical ***-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical *** has been widely used in histopathological diagnosis prediction,treatment response evaluation,and prognosis ***-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC,which would reveal the complex multistep process of the *** accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics,and show potential to convert surgical/intervention treatment into an antitumorigenic one,which would greatly advance precision medicine in HCC management.

关键词： hepatocellular carcinoma radiomics proteomics genomics multi-omics

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Manipulating density of magnetic skyrmions via multilayer repetition and thermal annealing

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Physical Review B 2021年第6期104卷 064421-064421页

作者： Xinran Wang Anni Cao Sai Li Jin Tang Ao Du Houyi Cheng Yiming Sun Haifeng Du Xueying Zhang Weisheng Zhao Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao 266000 China Beijing Microelectronics Technology Institute Beijing 100076 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of Chinese Academy of Sciences and University of Science and Technology of China Hefei 230031 China

The magnetic skyrmion, a tiny magnetic texture that holds promise as the next-generation information carrier, has been widely studied in recent years. A fine tunability of skyrmion density is required for its real applications in novel memory and logic devices. Here, we report on the manipulation of skyrmion density at room temperature in a Pt/Co/Ta/MgO system composed of multiple repetitions with perpendicular magnetic anisotropy. Néel-type skyrmions are observed by performing Lorentz transmission electron microscopy and magnetic force microscopy. The influence of structure repetition on skyrmions is experimentally investigated and further understood by micromagnetic simulations. The variation of skyrmion density with repetition number is mainly attributed to magnetic anisotropy. Meanwhile, thermal annealing is utilized to regulate the skyrmion density, where skyrmion-related properties exert a competitive effect. Our findings provide alternative means to manipulate skyrmion density, further allowing for optimized engineering of skyrmion-based devices.

关键词： Magnetic interactions Magnetic texture Skyrmions Spintronics Multilayer thin films Brillouin scattering & spectroscopy Lorentz microscopy Magnetic force microscopy

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Exploring the influence of flooding conditions on crowd evacuation: An experimental study

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Transportation Research Part D: Transport and Environment 2025年 146卷

作者： Shi, Dongdong Wang, Qiao Chen, Juan Lovreglio, Ruggiero Yang, Wei Ma, Jian School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 610031 China School of Built Environment Massey University Auckland 0632 New Zealand Faculty of Geosciences and Engineering Southwest Jiaotong University Chengdu 610031 China Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management Hefei University Hefei 230601 China

Understanding the impact of dynamic water flow on crowd evacuation is essential for improving pedestrian safety under urban floods. Previous research has primarily focused on studying pedestrian movement characteristics in static water scenarios, whereas investigations of pedestrian evacuation under dynamic water flow are still rare. In this study, a series of bottleneck evacuation experiments were performed using a novel experimental flooding setup. Five inflow depths, i.e., 0, 0.3, 0.4, 0.5, and 0.6 m, corresponding to five water flow rates, i.e., 0, 0.225, 0.3, 0.375, and 0.45 m³/s, were designed to simulate dynamic water flow in bottleneck layouts. Two evacuation strategies, including individual evacuation and cooperative evacuation, were considered. How inflow depths and evacuation strategies affect density, speed, and nearest-neighbor distribution at the bottleneck is explored. Our findings demonstrate that the pedestrian speed and flow rate in cooperative scenarios are higher than those in individual scenarios. Total evacuation times are 21.7 %, 10.77 %, 5 %, and 14.85 % faster in cooperative scenarios than those in individual scenarios at inflow depths of 0.3, 0.4, 0.5, and 0.6 m. These results will offer valuable insights for calibrating flood evacuation models and improving crowd management during emergencies.

关键词： Bottleneck evacuation experiment Dynamic water flow Evacuation behavior Evacuation strategy Flooding disasters Inflow depth

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Towards Lightweight Serverless Computing via Unikernel as a Function

Towards Lightweight Serverless Computing via Unikernel as a ...

引用

International Workshop on Quality of Service

作者： Bo Tan Haikun Liu Jia Rao Xiaofei Liao Hai Jin Yu Zhang 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 The University of Texas at Arlington

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

Serverless computing, also known as “Function as a Service (FaaS)”, is emerging as an event-driven paradigm of cloud computing. In the FaaS model, applications are programmed in the form of functions that are executed and managed separately. Functions are triggered by cloud users and are provisioned dynamically through containers or virtual machines (VMs). The startup delays of containers or VMs usually lead to rather high latency of response to cloud users. Moreover, the communication between different functions generally relies on virtual net devices or shared memory, and may cause extremely high performance overhead. In this paper, we propose Unikernel-as-a-Function (UaaF), a much more lightweight approach to serverless computing. Applications are abstracted as a combination of different functions, and each function are built as an unikernel in which the function is linked with a specified minimum-sized library operating system (LibOS). UaaF offers extremely low startup latency to execute functions, and an efficient communication model to speed up inter-functions interactions. We exploit an new hardware technique (namely VMFUNC) to invoke functions in other unikernels seamlessly (mostly like inter-process communications), without suffering performance penalty of VM Exits. We implement our proof-of-concept prototype based on KVM and deploy UaaF in three unikernels (MirageOS, IncludeOS, and Solo5). Experimental results show that U aaF can significantly reduce the startup latency and memory usage of serverless cloud applications. Moreover, the VMFUNC-based communication model can also significantly improve the performance of function invocations between different unikernels.

关键词： Cloud computing FAA Libraries Containers Task analysis Computer architecture Virtual machine monitors

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ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science

arXiv

引用

arXiv 2024年

作者： Ito, Koichi Zhu, Yihan Abdelrahman, Mahmoud Liang, Xiucheng Fan, Zicheng Hou, Yujun Zhao, Tianhong Ma, Rui Fujiwara, Kunihiko Ouyang, Jiani Quintana, Matias Biljecki, Filip Department of Architecture National University of Singapore 4 Architecture Drive Singapore117356 Singapore College of Big Data and Internet Shenzhen Technology University 3002 Lantian Road Shenzhen518118 China Department of Architecture and Civil Engineering City University of Hong Kong 999077 Hong Kong Research & Development Institute Takenaka Corporation 1-5-1 Otsuka Inzai Chiba270-1352 Japan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Singapore-ETH Centre Future Cities Lab Global Programme CREATE campus 1 Create Way #06-01 CREATE Tower 138602 Singapore Department of Real Estate National University of Singapore 15 Kent Ridge Drive Singapore119245 Singapore

Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases. This package is openly available at https://***/koito19960406/ZenSVI, and it is supp

关键词： Open source software

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Partitioning large-scale property graph for efficient distributed query processing 21

Partitioning large-scale property graph for efficient distri...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Pang, Haohan Gan, Peng Yuan, Pingpeng Jin, Hai Hua, Qiangsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Clusters and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead. © 2019 IEEE.

关键词： Semantics

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SCDetector: Software Functional Clone Detection Based on Semantic Tokens Analysis

SCDetector: Software Functional Clone Detection Based on Sem...

引用

IEEE International Conference on Automated Software engineering (ASE)

作者： Yueming Wu Deqing Zou Shihan Dou Siru Yang Wei Yang Feng Cheng Hong Liang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security HUST Wuhan China Shenzhen HUST Research Institute Shenzhen China School of Cyber Science and Engineering HUST Wuhan China School of Computer Science and Technology HUST Wuhan China University of Texas at Dallas United States

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Code clone detection is to find out code fragments with similar functionalities, which has been more and more important in software engineering. Many approaches have been proposed to detect code clones, in which token-based methods are the most scalable but cannot handle semantic clones because of the lack of consideration of program semantics. To address the issue, researchers conduct program analysis to distill the program semantics into a graph representation and detect clones by matching the graphs. However, such approaches suffer from low scalability since graph matching is typically time-consuming. In this paper, we propose SCDetector to combine the scalability of token-based methods with the accuracy of graph-based methods for software functional clone detection. Given a function source code, we first extract the control flow graph by static analysis. Instead of using traditional heavyweight graph matching, we treat the graph as a social network and apply social-network-centrality analysis to dig out the centrality of each basic block. Then we assign the centrality to each token in a basic block and sum the centrality ofthe same token in different basic blocks. By this, a graph is turned into certain tokens with graph details (i.e., centrality), called semantic tokens. Finally, these semantic tokens are fed into a Siamese architecture neural network to train a code clone detector. We evaluate SCDetector on two large datasets of functionally similar code. Experimental results indicate that our system is superior to four state-of-the-art methods (i.e., SourcererCC, Deckard, RtvNN, and ASTNN) and the time cost of SCDetector is 14 times less than a traditional graph-based method (i.e., CCSharp) on detecting semantic clones.

关键词： Scalability Semantics Cloning Detectors Transforms Static analysis Software engineering

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