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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhang, Liu Wang, Chaokun Ba, Yiyuan Wang, Hao Wang, Jianmin Department of Computer Science and Technology Tsinghua University China School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology China Key Laboratory for Information System Security Ministry of Education China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China

ISBN: (纸本)9781450306164

Content-aware music adaption, i.e. music resizing, in temporal constraints starts drawing attention from multimedia communities because of the need of real-world scenarios, e.g. animation production and radio advertisement production. The goal of music resizing is to change the length of a music track to a user preferred length using a series of basic operations, e.g. compression, prolonging, cropping and repeating. The only existing music resizing approach so far, called LyDAR, suffers from some limitations. For example, it cannot support prolonging a music track and cannot compress music pieces with very small stretch rates. In this paper, we propose MUSIZ, a generic framework for MUsic reSIZing. Observing the diversity of quality degradation for different segments, we propose the concept of stretch-resistance to measure the degree of quality degradation after a segment is stretched. MUSIZ stretches high stretch-resistance segments intensively and relieves low stretch-resistance segments to reduce the negative impact on the stretched music piece. For short length resizing requests, we develop a contiguity-preservative cropping algorithm to remove segments before stretching, while smoothing the abrupt change at the joint between two segments. Comprehensive experimental results show that MUSIZ is superior to the existing approaches. © 2011 ACM.

关键词： stretch-resistance music retargeting music resizing

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A Privacy Leak Detection Mechanism based on Service Binding 10

A Privacy Leak Detection Mechanism based on Service Binding

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10th International Symposium on system security, Safety, and Reliability, ISSSR 2024

作者： Wang, Boyang He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid. © 2024 IEEE.

关键词： Static analysis

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for Processing Large-scale datasets on Distributed Platforms 10

FBOD: An Outlier Detection Algorithm based on Data Features ...

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10th International Symposium on system security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Song, Hu Cheng, Xinyun Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms. © 2024 IEEE.

关键词： Anomaly detection

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A Lexical Updating Algorithm for Sentiment Analysis on Chinese Movie Reviews 5

A Lexical Updating Algorithm for Sentiment Analysis on Chine...

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5th International Conference on Advanced Cloud and Big data, CBD 2017

作者： Song, Yiwei Gu, Kaiwen Li, Huakang Sun, Guozi Bell Honor School Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781538610725

With the prevalence of Internet, sentiment analysis gets popularity among the world. Researchers have made use of kinds of online documents like commodities reivews and movie reviews as training samples to train their models and classfiers, by which they could speculate the underlying emotion in new ones. Douban is a Chinese online community where users share their personal reviews to express their feelings about movies. Those Chinese movie reviews were utilized by us to train our lexicon-based model. Yet multiple words in a ready-made lexicon do not agree with the movie reviews in a specific domain, which means the original lexicon acquires being updated to gain higher accuracy. In this paper we introduce a lexical updating algorithm based on a widely used lexicon. After turns of training of updating, this lexicon is capable of classifying sentiment among movie reviews. The experimental result shows our model using the updated lexicon could get a better performance than the primitive lexicon-based model. © 2017 IEEE.

关键词： Sentiment analysis

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intelligent Contract Timestamp Vulnerability Detection Based on key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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Improvement of the DPoS Consensus Mechanism in Blockchain Based on PLTS 7

Improvement of the DPoS Consensus Mechanism in Blockchain Ba...

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7th IEEE International Conference on Big data security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on intelligent data and security, Bigdatasecurity/HPSC/IDS 2021

作者： Chen, Shuyu Xie, Mingyue Liu, Jun Zhang, Yunan School of Big Data and Software Engineering Chongqing University Chongqing China College of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781665439275

Contemporarily, DPoS is the fastest, most effective, decentralized and flexible consensus mechanism among all consensus mechanisms. Similar to the vote of the board, the holders cast a certain number of delegates to perform verification and accounting on their behalf in DPoS. In order to select the most appropriate delegates to implement the operations in blockchain, the DPoS consensus mechanism needs to be improved. Therefore, an improved DPoS consensus mechanism based on Probabilistic Linguistic Term Set (PLTS) is proposed in this paper. Just like human elections, this method allows each node to vote 'support' and 'oppose' for the delegate node. The efficient and flexible method reduces the probability of malicious nodes becoming delegates and guarantees the security of block generation in the blockchain. The effectiveness of the improved DPoS was verified by examples. © 2021 IEEE.

关键词： Blockchain

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A New Approach to the Position of Acupoints on the Back of Humans

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Recent Patents on Mechanical Engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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Real-time stealth transmission via dither-based bias control

Real-time stealth transmission via dither-based bias control

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Yuanxiang Shao, Weidong Zhong, Linsheng Dai, Xiaoxiao Yang, Qi Deng, Lei Liu, Deming Cheng, Mengfan Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We report a real-time 1 kbps stealthy transmission in the 10 Gbps QPSK public communication. The stealth data is embedded in dither signals of bias control. The scheme is compatible with existing optical transmission ... 详细信息

ISBN: (纸本)9781957171258

关键词： Light transmission

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G2G: Generalized Learning by Cross-Domain Knowledge Transfer for Federated Domain Generalization

G2G: Generalized Learning by Cross-Domain Knowledge Transfer...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinqian Chen Jin Zhang Xiaoli Gong College of Computer Science Nankai University China Key Lab of Data and Intelligent System Security Ministry of Education China

We propose G2G, based on the global model of Generalized learning to solve the Federated Domain Generalization (FedDG) task. FedDG aims to collaboratively train a global model that can directly generalize to the unseen target domain without data sharing. Existing methods face challenges from both data heterogeneity, arising from imbalanced as well as non-independent and identical distributions (non-IID) among all domains, and model heterogeneity due to personalized requirements for client models. Also, these methods suffer from unnecessary time cost due to aggregation and distribution. G2G addresses these issues by making the global model acquire in-domain classification knowledge during local knowledge transfer and gain extensive knowledge in cross-domain training. Moreover, G2G eliminates waiting time by allowing clients to train independently when not trained with the global model. G2G outperforms state-of-the-art (SOTA) methods by 1.52%, 2.35%, and 1.11% on three datasets, respectively.

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