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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Xu, Zhiwu Ren, Kerong Song, Fu College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781728133423

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families. © 2019 IEEE.

关键词： Static analysis

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Application of an Improved Watershed Algorithm Based on Distance Map Reconstruction in Bean Image Segmentation

SSRN

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SSRN 2022年

作者： Liu, Hongquan Zhang, Weijin Wang, Fushun Sun, Xiaohua Wang, Junhao Wang, Chen Wang, Xinxin College of Urban and Rural Construction Hebei Agricultural University Baoding071000 China College of Information Science and Technology Hebei Agricultural University Baoding071000 China Hebei Key Laboratory of Agricultural Big Data Baoding071000 China Department of Digital Media Hebei Software Institute Baoding071000 China Agricultural Technology Innovation Center in Mountainous Areas of Hebei Province Baoding071000 China Agricultural Engineering Technology Research Center of National North Mountainous Area Baoding071000 China Hebei Agricultural University Hebei Mountain Research Institute Baoding071000 China

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residual rat

关键词： Image segmentation

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Application of an Improved Watershed Algorithm Based on Distance Map Reconstruction in Bean Image Segmentation

SSRN

引用

SSRN 2022年

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven gray level of the original image, traditional watershed algorithms will generate many incorrect edges, resulting in over-segmentation and under-segmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the gray distribution characteristics of the image, the gray reduction amplitude h was selected to generate the mask image with the same gray distribution trend as that of the original image. The original grayscale map was reconstructed with corresponding thresholds selected according to the false minimums of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residua

关键词： Image segmentation

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Cooperative Game Model of Delegation Computing: Verifier Separated from Calculators

Cooperative Game Model of Delegation Computing: Verifier Sep...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Duo Zhang Youliang Tian Linjie Wang Sheng Gao Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology College of Mathematics and Statistics Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Cryptography and Date Security Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Information Central University of Finance and Economics Beijing China School of Cyber Engineering College of Computer Science and Technology Xi'dian University Xi'an China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

In cloud computing, the delegation computation service is required because end users usually are resource-constrained, and at the same time the correctness of computing results needs to be verified. However, all the existing and available technologies have a high cost to verify which is the main problem faced by the delegation computing under the cloud platform. In order to address this issue, based on the game theory and a smart contract, we construct three protocols. The client and the calculator sign the Prisoner's Protocol to incentivize correct computation by asking the calculator to pay a deposit upfront; The client and the verifier sign the Long-acting Mechanism Protocol to ensure the validity of the verification results; The calculator and the verifier sign the Collusion Protocol to make collusion the most profitable strategy for all colluding parties. Through the combination of these protocols, the work is realized by separation the computing task and the verification task, and the heavy verification task is avoided. Finally, the performance analysis of the results shows that the combination of all the protocols not only solves the problem of verification complexity in the traditional delegation computing, but also guarantees the benefit of the honest player.

关键词： Calculators Contracts Protocols Task analysis Computational modeling Cloud computing Security

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MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models

arXiv

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arXiv 2024年

作者： Liu, Mianxin Ding, Jinru Xu, Jie Hu, Weiguo Li, Xiaoyang Zhu, Lifeng Bai, Zhian Shi, Xiaoming Wang, Benyou Song, Haitao Liu, Pengfei Zhang, Xiaofan Wang, Shanshan Li, Kang Wang, Haofen Ruan, Tong Huang, Xuanjing Sun, Xin Zhang, Shaoting Shanghai Artificial Intelligence Laboratory Shanghai200232 China Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200025 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The Shanghai Artificial Intelligence Research Institute Shanghai Jiao Tong University Shanghai200240 China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Sichuan University West China Hospital Chengdu610041 China The College of Design and Innovation Tongji University Shanghai200092 China The Department of Computer Science and Technology East China University of Science and Technology Shanghai200237 China The School of Computer Science Fudan University Shanghai200433 China Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200092 China

Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce "MedBench", a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300,901 questions) to cover 43 clinical specialties and performs multi-facet evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations for question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals' perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://***. © 2024, CC BY.

关键词： Benchmarking

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Privacy-preserving cloud-aided broad learning system

arXiv

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arXiv 2021年

作者： Liu, Haiyang Zhang, Hanlin Guo, Li Yu, Jia Lin, Jie College of Computer Science and Technology Qingdao University Qingdao266071 China Institute of Big Data Technology and Smart City Qingdao University Qingdao266071 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Electronic and Information Engineering Xian Jiaotong University Xian710049 China

With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges also follow with the use of cloud computing, including the privacy of the data and the correctness of returned results. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm not only improves the efficiency of the algorithm on the client but also ensures that the clients sensitive information is not leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Outsourcing

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Sensitive Labels Matching Privacy Protection in Multi-Social Networks

Sensitive Labels Matching Privacy Protection in Multi-Social...

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IEEE International Conference on Communications (ICC)

作者： Wei Wang Qilin Mu Yanhong Pu Dapeng Man Wu Yang Xiaojiang Du Harbin Engineering University Improving the Governance Capability of Big Data Application Counting National Engineering Laboratory CETC Big Data Research Institute Co. Ltd Harbin China Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory Guiyang College of Computer Science and Technology Harbin Engineering University Harbin China Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In social networks, some private information, such as the personal name, age gender, the number of friends, can be obtained by others. This paper defines a combination degree-neighborhood label matching attack model based on group maps obtained from multi-social networks. Based on the heuristic combination degree attack model, the target combination degree and neighborhood labels are used as the background knowledge of the attacker to obtain the candidate vertices set. The singularity of the sensitive label matching results will expose the sensitive information of the vertex being attacked. In order to solve this privacy attack, this paper proposes a group graph sensitive label generalization L diversity algorithm. This algorithm reduces the probability of sensitive labels being identified by designing a group map sensitive label generalization tree. According to the background knowledge, the number of sensitive labels in the candidate set and the number of sensitive labels obtained by matching are not less than L, so as to protect the sensitive information of the attacked target. The algorithm was evaluated by using three sets of data with different ratios. The experiment results show that the privacy protection algorithm effectively prevents sensitive label privacy attacks consisting of combination degree-domain label matching and better maintains the availability of graph data.

关键词： Social network services data privacy Privacy big data applications Sensitivity Heuristic algorithms

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Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

arXiv

引用

arXiv 2021年

作者： Zhao, Chen Liu, Yuqing Hu, Li Yuan, Zhengzhong College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Minnan Normal University Zhangzhou363000 China Institute of Meteorological Big Data-Digital Fujian Minnan Normal University Zhangzhou363000 China

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

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Beamforming and Power Allocation for Double-RIS-aided Two-way Directional Modulation Network

arXiv

引用

arXiv 2022年

作者： Dong, Rongen Shu, Feng Chen, Ri-Qing Wu, Yongpeng Pan, Cunhua Wang, Jiangzhou The School of Information and Communication Engineering Hainan University Haikou570228 China The College of Computer and Information Science Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou China The Shanghai Key Laboratory of Navigation Location Based Services Shanghai Jiao Tong University Shanghai Minhang200240 China The National Mobile Communications Research Laboratory Southeast University China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

To improve the information exchange rate between Alice and Bob in traditional two-way directional modulation (TWDM) network, a new double-reconfigurable intelligent surface (RIS)-aided TWDM network is proposed. To achieve the low-complexity transmitter design, two analytical precoders, one closed-form method of adjusting the RIS phase-shifting matrices, and semi-iterative power allocation (PA) strategy of maximizing secrecy sum rate (SSR) are proposed. First, the geometric parallelogram (GPG) criterion is employed to give the phase-shifting matrices of RISs. Then, two precoders, called maximizing singular value (Max-SV) and maximizing signal-to-leakage-noise ratio (Max-SLNR), are proposed to enhance the SSR. Evenly, the maximizing SSR PA with hybrid iterative closed-form (HICF) is further proposed to improve the SSR and derived to be one root of a sixth-order polynomial computed by: (1) the Newton-Raphson algorithm is repeated twice to reduce the order of the polynomial from six to four;(2) the remaining four feasible solutions can be directly obtained by the Ferrari's method. Simulation results show that using the proposed Max-SV and Max-SLNR, the proposed GPG makes a significant SSR improvement over random phase and no RIS. Given GPG, the proposed Max-SV outperforms the proposed leakage for small-scale or medium-scale RIS. Particularly, the proposed HICF PA stragey shows about ten percent performance gain over equal PA. Copyright © 2022, The Authors. All rights reserved.

关键词： Modulation

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PAT: Preference-Aware Transfer Learning for Recommendation with Heterogeneous Feedback

PAT: Preference-Aware Transfer Learning for Recommendation w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feng Liang Wei Dai Yunfeng Huang Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.

关键词： Collaboration Matrix decomposition Predictive models data models Probabilistic logic Training Context modeling

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