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Towards a diffraction-based sensing approach on human activity recognition

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第1期3卷 1-25页

作者： Zhang, Fusang Niu, Kai Xiong, Jie Jin, Beihong Gu, Tao Jiang, Yuhang Zhang, Daqing School of Electronics Engineering and Computer Science Peking University State Key Laboratory of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China College of Information and Computer Sciences University of Massachusetts Amherst United States State Key Laboratory of Computer Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Science Royal Melbourne Institute of Technology University Australia

In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration;2) a new deterministic sensing approach to model the received signal variation patterns for different human activities;3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%. © 2019 Association for Computing Machinery.

关键词： Wi-Fi

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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InsDal: A safe and extensible instrumentation tool on Dalvik byte-code for Android applications 24

InsDal: A safe and extensible instrumentation tool on Dalvik...

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24th IEEE International Conference on software Analysis, Evolution, and Reengineering, SANER 2017

作者： Liu, Jierui Wu, Tianyong Deng, Xi Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781509055012

Program instrumentation is a widely used technique in dynamic analysis and testing, which makes use of probe code inserted to the target program to monitor its behaviors, or log runtime information for off-line analysis. There are a number of automatic tools for instrumentation on the source or byte code of Java programs. However, few works address this issue on the register-based Dalvik byte-code of ever-increasing Android apps. This paper presents a lightweight tool, InsDal, for inserting instructions to specific points of the Dalvik byte-code according to the requirements of users. It carefully manages the registers to protect the behavior of original code from illegal manipulation, and optimizes the inserted code to avoid memory waste and unnecessary overhead. This tool is easy to use and has been applied to several scenarios (e.g. energy analysis, code coverage analysis). © 2017 IEEE.

关键词： Android (operating system)

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Empirical investigation of stochastic local search for maximum satisfiability

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Frontiers of computer science 2019年第1期13卷 86-98页

作者： Yi CHU Chuan LUO Shaowei CAI Haihang YOU State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

The maximum satisfiability (MAX-SAT)problem is an important NP-hard problem in theory,and has a broad range of applications in *** local search (SLS)is becoming an increasingly popular method for solving ***,a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT ***,the performance of CCLS on solving industrial MAX-SAT instances lags far *** this paper,we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT ***,we conduct experiments to analyze why CCLS performs poor on industrial *** we propose a new strategy called additive BMS (Best from Multiple Selections)to ease the serious *** integrating CCLS and additive BMS,we develop a new SLS algorithm for MAXSAT called CCABMS,and related experiments indicate the efficiency of ***,we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT,and combine an effective initialization method with CCABMS,resulting in an enhanced *** results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

关键词： empirical investigation stochastic local search maximum satisfiability industrial instances additive BMS

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ExcePy: A Python Benchmark for Bugs with Python Built-in Types 29

ExcePy: A Python Benchmark for Bugs with Python Built-in Typ...

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29th IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2022

作者： Zhang, Xin Yan, Rongjie Yan, Jiwei Cui, Baoquan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781665437868

As bugs of Python built-in types can cause code crashes, detecting them is critical to the robustness of the software. Researchers have concluded plenty of patterns for the bug causes and applied these patterns in detection tools. But these tools are only evaluated on handcrafted bugs or bugs obtained from QA pages. Because such bugs cannot reflect the complex code structures and various bug types encountered in real-world projects, the evaluation result is untrustworthy when applied to these projects. As a result, a collection of real-world reproducible bugs is essential for tool evaluation and future bug-related research. In this paper, we propose ExcePy, a benchmark for providing bugs of Python built-in types. We collect 180 bugs from the evolution of 15 real-world open-source Python projects on GitHub and then manually build test scripts for bug reproduction. Meanwhile, to improve tool evaluation efficiency, we present a code pruning strategy that can minimize buggy code size while retaining bug reproducibility and apply it to ExcePy to provide simplified buggy code. To demonstrate the benefits of ExcePy, we use three static analyzers and two fuzzers to detect bugs collected in ExcePy. We found that simplified code can significantly reduce running time and avoid many tool crashes, and bugs supplied by ExcePy can reveal limitations of existing tools in reporting real-world bugs. © 2022 IEEE.

关键词： Python

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SPrinter: A static checker for finding smart pointer errors in C++ programs 34

SPrinter: A static checker for finding smart pointer errors ...

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34th IEEE/ACM International Conference on Automated software engineering, ASE 2019

作者： Ma, Xutong Yan, Jiwei Li, Yaqi Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781728125084

Smart pointers are widely used to prevent memory errors in modern C++ code. However, improper usage of smart pointers may also lead to common memory errors, which makes the code not as safe as expected. To avoid smart pointer errors as early as possible, we present a coding style checker to detect possible bad smart pointer usages during compile time, and notify programmers about bug-prone behaviors. The evaluation indicates that the currently available state-of-the-art static code checkers can only detect 25 out of 116 manually inserted errors, while our tool can detect all these errors. And we also found 521 bugs among 8 open source projects with only 4 false positives. © 2019 IEEE.

关键词： C++ (programming language)

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Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S‑Boxes

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Cybersecurity 2021年第1期4卷 541-560页

作者： Huizhong Li Guang Yang Jingdian Ming Yongbin Zhou Chengbin Jin Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China School of Cyber Security Nanjing University of Science and TechnologyNanjing 210094China Department of Software Engineering and Software Evaluation Beijing Institute of Computer Technology and ApplicationBeijing 100039China

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic *** most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are reVisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),***,the practical effectiveness of these metrics remains still *** the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these *** of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other ***,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the nonprofiled attacks and profiled attacks,*** experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC *** also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled ***,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different ***,all of them are invalid in this scenario.

关键词： Side-channel attacks NIST lightweight cryptography S-Boxes Transparency order Confusion coefficient

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Evaluating and improving the interpretability of item embeddings using item-tag relevance information

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Frontiers of computer science 2020年第3期14卷 143-158页

作者： Tao LIAN Lin DU Mingfu ZHAO Chaoran CUI Zhumin CHEN Jun MA College of Data Science Taiyuan University of TechnologyJinzhong 030600China Software College Shandong UniversityJinan 250101China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 101408China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent ***,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation ***,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item *** this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance ***,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent *** also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item *** on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes *** leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.

关键词： recommender system matrix factorization item embedding item-tag relevance interpretability

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Accurate Reliability Analysis Methods for Approximate Computing Circuits

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Tsinghua science and Technology 2022年第4期27卷 729-740页

作者： Zhen Wang Guofa Zhang Jing Ye Jianhui Jiang Fengyong Li Yong Wang School of the Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Software Engineering Tongji UniversityShanghai 200092China

In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to *** the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly *** this study,two accurate reliability evaluation methods for approximate computing circuits are *** reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)*** the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout *** accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source *** results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.

关键词： Approximate Computing Circuit(ACC) correlation coefficient iterative Probabilistic Transfer Matrix(PTM) reliability

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A path-oriented approach to generating executable test sequences for extended finite state machines

A path-oriented approach to generating executable test seque...

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IEEE 6th International Symposium on Theoretical Aspects of software engineering, TASE 2012

作者： Wu, Tianyong Yan, Jun Zhang, Jian Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9780769547510

The Extended Finite State Machine (EFSM) is a commonly used model for specifying software systems. A test sequence for an EFSM is a sequence composed of values of input variables, which can make the EFSM "execute" along a complete path from entry to exit. Traditional test sequence generation methods for EFSM almost imitate those FSM based approaches and focus on states identification. Most of them impose significant restrictions on the EFSM. This paper proposes a path-oriented approach to generating test cases for EFSM and presents a tool for test data generation. The experiments show that our tool can generate executable test sequences for EFSM models of software systems automatically in acceptable time. © 2012 IEEE.

关键词： software testing

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