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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Yi, Zexuan Fu, Wenlong Cao, Lihong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781728105352

For the study of new computer architecture inspired by brain, implementing neuron function on hardware has become an emerging research direction. To study the feasibility of biological neuron implementation and acceleration characteristics on the hardware platform, the basic Hodgkin-Huxley (HH) neuron model and a small scale of synapse connection model is implemented on Xilinx Zynq7 Zedboard FPGA, high-level-synthesis method is used to reduce the burden of hardware design. The Hodgkin-Huxley model simulation result of FPGA are validated through high level software simulation tool in Brian2, furthermore, resource utilization and calculation time cost are provided in this paper, finally, a pipeline design of HH neuron model is used to accelerate calculations and reduce resource utilization, the result proves that this design is suitable for HH model simulation. © 2019 IEEE.

关键词： Neurons

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Malware homology identification based on a gene perspective

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Frontiers of Information Technology & Electronic engineering 2019年第6期20卷 801-815页

作者： Bing-lin ZHAO Zheng SHAN Fu-dong LIU Bo ZHAO Yi-hang CHEN Wen-jie SUN State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China Georg-August-University of Goettingen Goettingen 37077 Germany

Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface(API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.

关键词： Malware classification Gene perspective Dependency graph Homology analysis

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Representation Learning on Networks for Community Detection

Representation Learning on Networks for Community Detection

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International Conference on advanced Cloud and Big Data (CBD)

作者： Jingya Zhou Jianxi Fan Jin Wang Baolei Cheng School of Computer Science and Technology Soochow University Suzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Community detection is a fundamental problem in network analysis. In recent years, network representation learning has been leveraged to help the detection of potential communities. However, representation learning and community detection in these studies are usually optimized independently. In addition, they also have a strong assumption that the number of communities is prespecified before detection. To address those problems, we propose a joint representation learning and community detection (JRC) method. To allow the learned representations to be community-aware, JRC introduces two additional terms to the loss function for clustering. One is designed to getting node representations as close as possible to the exemplar of the community. Another is set up as a smoothness regularization to enforce the community properties of high cohesion and low coupling. Experimental results on multiple real-world network datasets show that JRC improves the quality of community detection and outperforms many competitive baselines.

关键词： Couplings Big Data

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A Self-Powered Nanogenerator for the Electrical Protection of Integrated Circuits from Trace Amounts of Liquid

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Nano-Micro Letters 2020年第1期12卷 54-62页

作者： Zhuang Hui Ming Xiao Daozhi Shen Jiayun Feng Peng Peng Yangai Liu Walter WDuley YNorman Zhou Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes National Laboratory of Mineral MaterialsSchool of Materials Science and TechnologyChina University of GeosciencesBeijing 100083People’s Republic of China Department of Mechanics and Mechatronics Engineering Centre for Advanced Materials JoiningUniversity of WaterlooWaterlooON N2L 3G1Canada School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191People’s Republic of China Waterloo Institute of Nanotechnology University of WaterlooWaterlooON N2L 3G1Canada Institute for Quantum Computing University of WaterlooWaterlooON N2L 3G1Canada State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001People’s Republic of China Department of Physics and Astronomy University of WaterlooWaterlooON N2L 3G1Canada

With the increase in the use of electronic devices in many different environments,a need has arisen for an easily implemented method for the rapid,sensitive detection of liquids in the vicinity of electronic *** this work,a high-performance power generator that combines carbon nanoparticles and TiO2 nanowires has been fabricated by sequential electrophoretic deposition(EPD).The opencircuit voltage and short-circuit current of a single generator are found to exceed 0.7 V and 100μA when 6μL of water was *** generator is also found to have a stable and reproducible response to other *** output voltage of 0.3 V was obtained after 244,876,931,and 184μs,on exposure of the generator to 6μL of water,ethanol,acetone,and methanol,*** fast response time and high sensitivity to liquids show that the device has great potential for the detection of small quantities of *** addition,the simple easily implemented sequential EPD method ensures the high mechanical strength of the *** compact,reliable device provides a new method for the sensitive,rapid detection of extraneous liquids before they can impact the performance of electronic circuits,particularly those on printed circuit board.

关键词： Self-powered generator Sub-millisecond response Liquid protection

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Multi-Variant eXecution: state-of-the-Art and Research Challenges

Multi-Variant eXecution: State-of-the-Art and Research Chall...

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International Conference on Communication Software and Networks, ICCSN

作者： Gaofei Zhang Zheng Zhang Bolin Ma Jiang Wang State key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

ISBN: (数字)9781728198156

ISBN: (纸本)9781728198163

As Internet security issues are becoming increasingly serious, many scholars and experts are exploring new defense strategies. The availability of multi-core computers and cloud computing resources has brought back the original costly defense methods to people's vision. Among them, the Multi-Variant eXecution (MVX) defense has received widespread attention and is considered a security technology that may change the rules of cyberspace games. In this paper, we analyze and summarize the current MVX defense technology, focusing on the evolution of the architecture and the key technologies. Finally, we point out the challenges to MVX and discuss future research, which is of great significance for the next work.

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Street-Level Landmarks Acquisition Based on SVM Classifiers

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Computers, Materials & Continua 2019年第5期59卷 591-606页

作者： Ruixiang Li Yingying Liu Yaqiong Qiao Te Ma Bo Wang Xiangyang Luo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Zhengzhou Science and Technology Institute Zhengzhou450001China Henan Institute of Animal Husbandry Economics Zhengzhou450044China State University of New York at Buffalo New York14260-1660United States

High-density street-level reliable landmarks are one of the important foundations for street-level ***,the existing methods cannot obtain enough street-level landmarks in a short period of *** this paper,a street-level landmarks acquisition method based on SVM(Support Vector Machine)classifiers is ***,the port detection results of IPs with known services are vectorized,and the vectorization results are used as an input of the SVM ***,the kernel function and penalty factor are adjusted for SVM classifiers training,and the optimal SVM classifiers are *** that,the classifier sequence is constructed,and the IPs with unknown service are classified using the ***,according to the domain name corresponding to the IP,the relationship between the classified server IP and organization name is *** experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially,and the median geolocation error using evaluated landmarks is reduced by about 2 km.

关键词： Landmarks acquisition SVM street-level IP geolocation

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Revisiting Test Cases to Boost Generate-and-Validate Program Repair

Revisiting Test Cases to Boost Generate-and-Validate Program...

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International Conference on Software Maintenance (ICSM)

作者： Jingtang Zhang Kui Liu Dongsun Kim Li Li Zhe Liu Jacques Klein Tegawendé F. Bissyandé Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics) Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Kyungpook National University Daegu South Korea Monash University Melbourne Australia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg

Fault localization produces bug positions as the basic input for many automated program repair (APR) systems. Given that test cases are the common means that automatic fault localization techniques leverage, we investigate the impact of their characteristics (in terms of quality and quantity) on APR. In particular, we analyze the statements that appear in crash stack traces when test cases fail (note that stack traces are available when an ordinary test case fails since its verdict is often made by assertions that produce errors such as AssertError in Java and JUnit), and explore the possibility of using some relevant crash information to enhance fault localization; this ultimately improves the effectiveness of APR tools. Our study reveals that the considered state-of-the-art APR systems achieve the best performance when fixing bugs associated with boolean type expected values (e.g., assertTrue ()) or assertFalse(). In contrast, they achieve their worst performance when addressing bugs related to null check assertions. Meanwhile, null check bugs as well as the bugs associated with boolean and string type expected values are still the main challenge that should be addressed by the future APR. For exception throwing bugs, existing APR systems present the best performance on fixing NullPointerException bugs, while the tough task of them is to resolve the bugs throwing developer-defined exceptions. The information in stack traces after executing the bug-triggering test cases can be used to effectively improve the performance on fault localization and program repair.

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Malicious Code Detection Technology Based on A3C Algorithm

Malicious Code Detection Technology Based on A3C Algorithm

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Yi Xue Hui Shu Wenjuan Bu Wu Qu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Beijing Venustech Cybervision Co. Ltd. China Beijing China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

In the face of the explosive growth of malicious code, this paper proposes a malicious code detection technology based on the A3C algorithm. The technology constructs a malicious code anti-detection adversarial model through the A3C algorithm of the reinforcement learning. It can automatically generates anti-detection malicious samples that bypass the intelligent model on the existing malicious sample set, constructs the anti-detection sample training data set, uses the anti-detection sample training data set to iteratively train new intelligent detection models, continuously builds a reinforcement learning intelligent detection model with continuously enhanced capabilities.

关键词： Face recognition Software algorithms Training data Reinforcement learning Malware Data models Software engineering

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

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Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging System-on-chip

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A Regional Network Topology Construction Algorithm Based On Sampling Measurement

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Journal of Physics: Conference Series 2021年第1期1861卷

作者： Zhiyuan Tao Yan Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China

Network topology construction is one of the basic means to study the target network. Whether it is the discovery of vital nodes, IP location or network characteristics mining, a complete and accurate network topology is needed. In this paper, a regional network topology construction algorithm based on sampling measurement is proposed to solve the problem of high time cost and resource waste of existing algorithms. Firstly, multiple vantage points located inside and outside the target area are selected to form a probing set. Then the IPes of the target area is extracted based on sampling to form the target IP set. Finally, the probing set is used to probe the target IP set periodically. The network topology of the target region was constructed based on the results of a 40-day internet measurement and compared with the existing database. The experimental results show that the coverage rate of routing nodes in the target area is improved by more than 90% compared with the measured data provided by CAIDA (Center for Applied Internet Data Analysis) and by more than 10% compared with the measured data provided by IPIP.

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