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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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Fuzhou PM2.5 Prediction and Related Factors analysis 16th

Fuzhou PM2.5 Prediction and Related Factors Analysis

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

作者： Zhang, Wen-Ji Chen, Li-Wen Zhou, Yao Zheng, Ri-Jing Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

The prediction of urban air pollutant concentration requires processing of large amounts of meteorological data and complex changes of air pollutants. PM2.5 is affected by many factors, including meteorological factors and pollutant factors. However, most existing methods either do not consider the corresponding relationship between the surrounding detection sites or ignore the strength of the correlation between them. Without considering the spatial correlation or having too much weak correlation factors, the input will affect the accuracy of the prediction results. To solve this limitation, we use the Pearson correlation coefficient (PCC) to make a correlation analysis of PM2.5 influencing factors. According to PCC, we can extract highly relevant data and drop weaker data. Then, we use the long short-term memory (LSTM) model to predict the PM2.5 of the monitoring station. The experimental results show that selecting input factors based on PCC can improve the prediction performance of the model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Modeling and Simulation for Battery Energy Storage System Participating in Power System Frequency Modulation Based on Power System analysis Software Package 1

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作者： Cao, Pengcheng Li, Peiqiang Sun, Peidong Zhang, Zhongkai Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Kota Kinabalu Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Battery energy storage technology, with its fast and accurate power response, has become the focus of the auxiliary means of power system frequency modulation. However, the traditional simulation software lacks an accurate battery energy storage system component model, which affects the accuracy of analyzing the response characteristics of the energy storage system to a certain extent. This paper presents an electromechanical transient model of battery energy storage system without time delay, which considers the participation of energy storage system in frequency modulation dead zone and battery charging and discharging power. The control model is built based on the node current injection method by using the power system analysis integrated program, and the EPRI-7 standard system is selected for simulation analysis. The simulation results show that the model has good implant ability. And when the load is cut off in the power system, the energy storage system participating in frequency modulation can effectively maintain the frequency stability. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer software

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Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization

arXiv

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arXiv 2018年

作者： Hu, Zhiwen Bai, Zixuan Bian, Kaigui Wang, Tao Song, Lingyang National Engineering Laboratory for Big Data Analysis and Applications School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China

—Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

Designing preamplifier for sensing atmospheric electrostatic...

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2020 International Symposium on Electronic Information Technology and Communication engineering, ISEITCE 2020

作者： Wu, Changhao Xiong, Yi Zeng, Wei National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei230601 China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures Center for Electron Microscopy Wuhan University Wuhan430072 China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network. © Published under licence by IOP Publishing Ltd.

关键词： Feedback amplifiers

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Comments on a privacy preserving public auditing mechanism for shared cloud data 17

Comments on a privacy preserving public auditing mechanism f...

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4th Multidisciplinary International Social Networks Conference, MISNC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Wu, Mu-En Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Harbin Institute of Technology Shenzhen Graduate School Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Mathematic Soochow University Taipei Taiwan

ISBN: (纸本)9781450348812

Auditing mechanisms are received much attentions from researchers due to the developing of cloud storage. Recently, Wang et al. proposed a privacy preserving public auditing mechanism for shared cloud data with supporting group dynamic. However, we find some security flaws in their mechanism. In this paper, a forgery attack and a data corruption attack are proposed to demonstrate the insecurity. Then, we propose a modified scheme based on their mechanism. The security analysis is demonstrated that our improvement can resist the mentioned two attacks. © 2017 ACM.

关键词： Cloud storage

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Mining of Multiple Fuzzy Frequent Itemsets with Transaction Insertion 4th

Mining of Multiple Fuzzy Frequent Itemsets with Transaction ...

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4th Euro-China Conference on Intelligent data analysis and applications, ECC 2017

作者： Wu, Tsu-Yang Lin, Jerry Chun-Wei Zhang, Yuyu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9783319685267

In this paper, we thus present an algorithm to efficiently update the multiple fuzzy frequent itemsets from the quantitative dataset with transaction insertion. The designed approach is based on the Fast UPdated (FUP) concept to divide the transformed linguistic terms into four cases, and each case is performed by the designed approach for updating the discovered information. Also, the fuzzy-list (FL) structure is adopted to reduce the generation of candidates without multiple database scans. Experiments are conducted to show that the proposed algorithm outperforms the state-of-the-art approach. © 2018, Springer International Publishing AG.

关键词： data mining

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An efficient fifth-order interpolation-based Hermite WENO scheme for hyperbolic conservation laws

arXiv

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arXiv 2024年

作者： Xie, Xiaoyang Tao, Zhanjing Jiao, Chunhai Zhang, Min College of Engineering Peking University Beijing100871 China School of Mathematics Jilin University Changchun130012 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China

In this paper, we develop a simple, efficient, and fifth-order finite difference interpolation-based Hermite WENO (HWENO-I) scheme for one- and two-dimensional hyperbolic conservation laws. We directly interpolate the solution and first-order derivative values and evaluate the numerical fluxes based on these interpolated values. We do not need the split of the flux functions when reconstructing numerical fluxes and there is no need for any additional HWENO interpolation for the modified derivative. The HWENO interpolation only needs to be applied one time which utilizes the same candidate stencils, Hermite interpolation polynomials, and linear/nonlinear weights for the solution and first-order derivative at the cell interface, as well as the modified derivative at the cell center. The HWENO-I scheme inherits the advantages of the finite difference flux-reconstruction-based HWENO-R scheme [Fan et al., Comput. Methods Appl. Mech. Engrg., 2023], including fifth-order accuracy, compact stencils, arbitrary positive linear weights, and high resolution. The HWENO-I scheme is simpler and more efficient than the HWENO-R scheme and the previous finite difference interpolation-based HWENO scheme [Liu and Qiu, J. Sci. Comput., 2016] which needs the split of flux functions for the stability and upwind performance for the high-order derivative terms. Various benchmark numerical examples are presented to demonstrate the accuracy, efficiency, high resolution, and robustness of the proposed HWENO-I scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

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Time-Series analysis on the Impact Level upon Taiwan Stock Market with Events of Signing International Agreements 3

Time-Series Analysis on the Impact Level upon Taiwan Stock M...

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3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016

作者： Tsai, Pei-Wei Lin, You-Xin Zhang, Jing Chang, Jui-Fang Chen, Jhih-Sheng Zhang, Jilin College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of International Business National Kaohsiung University of Applied Sciences Kaohsiung Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou China

ISBN: (纸本)9781509010936

Stock investment is one of the available conventional financial commodities in the investment market. The individual price of stocks including stocks in the field of industrial, medical, financial, overall, etc. Is subject to many different aspects. The relationship between aspects, the international economy, and the stock price is complex and is not easy to be categorized. Moreover, the trend of the world increases the cooperation and links between countries and different nations. Many agreements in tourism, trade, etc. Are signed between countries. In this paper, we focus on the effect on the stock prices caused by the agreements signed between Taiwan and other countries. The time series method is utilized to analyze the effect on the stock price forecasting caused by 5 of the incidents including WTO, Taiwan-Nicaragua FTA, Taiwan-Honduras FTA, and Taiwan-Japan BIA. Both the pre-event and the post-event periods are included for observing the fluctuation behavior of the stock market price. The MAPE value is used as the criterion to verify the correctness of the forecasting result. The MAPE values obtained in all experiments are all less than 10%. It implies that the time series method presents good forecasting ability in the analyzed cases. © 2016 IEEE.

关键词： Forecasting

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