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18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

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arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： Data visualization

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Quantum machine learning of eco-environmental surfaces

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科学通报（英文版） 2022年第10期67卷 1031-1033页

作者： Tianxiang Yue Yi Liu Zhengping Du John Wilson Dongyi Zhao Yu Wang Na Zhao Wenjiao Shi Zemeng Fan Xiaomin Zhao Qin Zhang Hongsheng Huang Qingyuan Wu Wei Zhou Yimeng Jiao Zhe Xu Saibo Li Yang Yang Bojie Fu State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90089-0374USA Origin Quantum Computing Technology Company Limited Hefei 230093China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China Monitoring Station for Weather and Eco-Environment of WuGong Mountain Pingxiang 337000China School of Geographical Sciences Southwest UniversityChongqing 400715China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China

The computer advances of the past century can be traced to the increase in their numbers on chips that has accompanied the miniaturization of ***,computers are nearing the fundamental limits of such miniaturization[1].Many practical problems require huge amounts of computational resources that exceed the capabilities of today's computers.A 54-qubit quantum computer on the other hand can solve in minutes a problem that would take a classical machine 10,000 years[2].

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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Android Malware Detection Based on Sensitive Permissions and APIs 2nd

Android Malware Detection Based on Sensitive Permissions and...

引用

2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Zhao, Chunhui Wang, Chundong Zheng, Wenbai Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9783030213725

With the widespread use of the Android operating system, the number of applications based on the Android platform is growing. How to effectively identify malware is critical to the security of phones. This paper proposes an Android malware detection method based on the combination of sensitive permissions and API features. This method extracts the permission features and API features by decompiling the APK file, and then uses the mutual information to select sensitive permissions and APIs as feature sets. On this basis, an ensemble learning model based on decision tree classifier and KNN classifier is used to quickly and accurately detect unknown APKs. The experimental results show that the discriminative accuracy of the proposed method is higher than that of the permission set or the API set alone, and the accuracy rate can reach up to 95.5%. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization

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Journal of systems Science & Complexity 2019年第2期32卷 634-656页

作者： LIU Ao DENG Xudong REN Liang LIU Ying LIU Bo School of Management Wuhan University of Science and Technology Center for Service Science and Engineering Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Economics and Management Beihang University Academy of Mathematics and Systems Science Chinese Academy of Sciences

As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature *** this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.

关键词： Evolutionary algorithms fruit fly optimization function optimization meta-heuristics

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Methods to Improve the Long Distance Time-Varying Channel Transmission Performance of Expendable Profiler

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China Ocean Engineering 2019年第6期33卷 753-761页

作者： ZHENG Yu ZHANG Xiao-yang WANG Xiao-rui GUO Xing-xin LI Hong-zhi School of Life Sciences Tianjin Polytechnic UniversityTianjin 300387China Tianjin Key Laboratory of Photoelectric Detection Technology and System Tianjin Polytechnic UniversityTianjin 300387China National Marine Data and Information Service Tianjin 300171China National Ocean Technology Center Tianjin 300112China

To improve the transmission performance of XCTD channel, this paper proposes a method to measure directly and fit the channel transmission characteristics by using frequency sweeping method. Sinusoidal signals with a frequency range of 100 Hz to 10 k Hz and an interval of 100 Hz are used to measure transmission characteristics of channels with lengths of 300 m, 800 m, 1300 m, and 1800 m. The correctness of the fitted channel characteristics by transmitting square wave, composite waves of different frequencies, and ASK modulation are verified. The results show that when the frequency of the signal is below 1500 Hz, the channel has very little effect on the signal. The signal compensated for amplitude and phase at the receiver is not as good as the uncompensated ***, when the signal frequency is above 1500 Hz, the channel distorts the signal. The quality of signal compensated for amplitude and phase at receiver is better than that of the uncompensated signal. Thus, we can select the appropriate frequency for XCTD system and the appropriate way to process the received signals. Signals below1500 Hz can be directly used at the receiving end. Signals above 1500 Hz are used after amplitude and phase compensation at the receiving end.

关键词： expandable conductivity temperature depth(XCTD)profiler time-varying channel modeling frequency sweeping method amplitude and phase compensation transmission performance

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Cloud detection from visual band of satellite image based on variance of fractal dimension

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Journal of systems Engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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