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Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor

computers, Materials & Continua 2018年第12期57卷 533-547页

作者： Guoyuan Lin Bowen Liu Pengcheng Xiao Min Lei Wei Bi School of Computer Science and Technology China University of Mining&TechnologyXuzhou221116China State Key Laboratory for Novel Software Technology hosted at Nanjing University Nanjing210023China Department of Mathematics University of EvansvilleIndiana47722USA Information Security Center Beijing University of Posts and TelecommunicationsBeijing100876China SeeleTech Corporation San Francisco94107USA Zabatech Corporation Beijing100088China

Anti-detection is becoming as an emerging challenge for *** paper solves the threats of anti-detection from the threshold setting *** webpages are considered to complicate threshold setting condition when the threshold is *** to the common visual behavior which is easily attracted by the salient region of webpages,image retrieval methods based on texton correlation descriptor(TCD)are improved to obtain enough webpages which have similarity in the salient region for the images of *** are two steps for improving TCD which has advantage of recognizing the salient region of images:(1)This paper proposed Weighted Euclidean Distance based on neighborhood location(NLW-Euclidean distance)and double cross windows,and combine them to solve the problems in TCD;(2)Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting *** results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable,and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain.

关键词： Anti-phishing blockchain texton correlation descriptor weighted euclidean distance image retrieval

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Stochastic optimization for non-convex inf-projection problems

arXiv

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arXiv 2019年

作者： Yan, Yan Xu, Yi Zhang, Lijun Wang, Xiaoyu Yang, Tianbao Department of Computer Science University of Iowa Iowa CityIA52242 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Intellifusion

In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem include difference of convex (DC) functions and a family of bi-convex functions as special cases. We develop stochastic algorithms and establish their first-order convergence for finding a (nearly) stationary solution of the target non-convex function under different conditions of the component functions. To the best of our knowledge, this is the first work that comprehensively studies stochastic optimization of non-convex inf-projection minimization problems with provable convergence guarantee. Our algorithms enable efficient stochastic optimization of a family of non-decomposable DC functions and a family of bi-convex functions. To demonstrate the power of the proposed algorithms we consider an important application in variance-based regularization, and experiments verify the effectiveness of our inf-projection based formulation and the proposed stochastic algorithm in comparison with previous stochastic algorithms based on the min-max formulation for achieving the same effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Functions

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Inspiremeposing: Learn pose and composition from portrait examples 26

Inspiremeposing: Learn pose and composition from portrait ex...

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26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Sheng, Bin Jin, Yuxi Li, Ping Wang, Wenxiao Fu, Hongbo Wu, Enhua Shanghai Jiao Tong University Department of Computer Science and Engineering China Macau University of Science and Technology Faculty of Information Technology China City University of Hong Kong School of Creative Media Hong Kong University of Macau Faculty of Science and Technology China Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software China

ISBN: (纸本)9783038680734

Since people tend to build relationship with others by personal photography, capturing high quality photographs on mobile device has become a strong demand. We propose a portrait photography guidance system to guide user’s photographing. We consider current scene image as our input and find professional photograph examples with similar aesthetic features for it. Deep residual network is introduced to gather scene classification information and represent common photograph rules by features, and random forest is adopted to establishing mapping relations between extracted features and examples. Besides, we implement our guidance system on a camera application and evaluate it by user study. © 2018 The Author(s).

关键词： Photography

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Privacy Computing: Concept, Computing Framework, and Future Development Trends

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Engineering 2019年第6期5卷 1179-1192页

作者： Fenghua Li Hui Li Ben Niu Jinjun Chen Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthornVIC 3122Australia

With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.

关键词： Privacy computing Private information description Privacy metric Evaluation of the privacy-preserving effect Privacy computing language

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of computer science & technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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A Novel Joint Optimization Method for Adaptive Hand-held Video Stabilization Based on Spatialtemporal Consistency

A Novel Joint Optimization Method for Adaptive Hand-held Vid...

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2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)

作者： Xiao Li Shuai Li Hong Qin Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

ISBN: (纸本)9781510885455

Video stabilization for hand-held camera is vital in many high-level video enhancement applications. Although quite a few proposed approaches have achieved remarkable success in recent years, many technical challenges still prevail for video(from hand-held camera) containing wide-range scenes and highlyvariable objects. In particular, we are lacking effective and versatile strategies to adaptively handle saliency preservation, parallax diminution, self-adaptive smoothing, cropping area decrement and video completeness in a consistent spatial-temporal fashion. To ameliorate, this paper develops a novel, joint optimization method to successively respect spatial structure consistency and temporal feature constraints. Our aim is to devise a new adaptive video stabilization technique by resorting to new modeling strategies. This paper's key originality is hinged upon joint utility of both self-adaptive intrinsic mode functions(IMFs) based on empirical mode decomposition(EMD) in temporal domain for video signal and mesh-structure constraint enforcement in the spatial domain. As a result, our new approach can optimize camera trajectory of wobbly video, and synchronously fine-tune the camera path based on key features to make the shaking video much closer to the original trend. To validate our joint optimization approach for adaptive video stabilization, we conduct comprehensive experiments on public benchmarks, and make extensive and quantitative evaluations with available state-of-the-art methods as well as popular commercial software. All of our experiments demonstrate the advantages of the joint optimization method in terms of versatility, accuracy, and efficiency.

关键词： hand-held Camera adaptive video stabilization spatial structure consistency feature-centric EMD self-adaptive IMF selection video extrapolation and interpolation

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RF-Kinect: A wearable RFID-based approach towards 3D body movement tracking

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第1期2卷 1-28页

作者： Wang, Chuyu Liu, Jian Chen, Yingying Xie, Lei Liu, Hongbo Lu, Sanglu Nanjing University State Key Laboratory for Novel Software Technology 163 Xianlin Ave Nanjing210046 China Rutgers University Department of Electrical and Computer Engineering North BrunswickNJ08902 United States Indiana University Purdue University Indianapolis Department of Computer Information and Technology IndianapolisIN46202 United States

The rising popularity of electronic devices with gesture recognition capabilities makes the gesture-based human-computer interaction more attractive. Along this direction, tracking the body movement in 3D space is desirable to further facilitate behavior recognition in various scenarios. Existing solutions attempt to track the body movement based on computer version or wearable sensors, but they are either dependent on the light or incurring high energy consumption. This paper presents RF-Kinect, a training-free system which tracks the body movement in 3D space by analyzing the phase information of wearable RFID tags attached on the limb. Instead of locating each tag independently in 3D space to recover the body postures, RF-Kinect treats each limb as a whole, and estimates the corresponding orientations through extracting two types of phase features, Phase Difference between Tags (PDT) on the same part of a limb and Phase Difference between Antennas (PDA) of the same tag. It then reconstructs the body posture based on the determined orientation of limbs grounded on the human body geometric model, and exploits Kalman filter to smooth the body movement results, which is the temporal sequence of the body postures. The real experiments with 5 volunteers show that RF-Kinect achieves 8.7? angle error for determining the orientation of limbs and 4.4cm relative position error for the position estimation of joints compared with Kinect 2.0 testbed. 2018 Association for Computing Machinery.

关键词： Radio frequency identification (RFID)

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Temporal-Spatial Consistency and Sampling Neural Network Based Adaptive Hand-held Video Deblurring

Temporal-Spatial Consistency and Sampling Neural Network Bas...

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作者： Xiao LI Shuai LI Hong QIN Ai-min HAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

Video deblurring for hand-held camera is vital in many high-level video enhancement applications. Although quite a few proposed approaches have achieved remarkable success in recent years, many technical challenges still prevail for video(from hand-held camera) containing wide-range scenes and highly-variable objects. In particular, we are lacking effective and versatile strategies to adaptively handle high-level alignment, parallax diminution, unclear area detection, sharpness enhancement in a consistent fashion. To ameliorate, this paper develops a novel method to successively respect temporal-spatial alignment and precise deblurring. Our aim is to devise a new adaptive video deblurring technique by resorting to new modeling strategies. This paper's key originality is hinged upon the joint utility of both self-adaptive intrinsic mode functions(IMFs) based on empirical mode decomposition(EMD) in the temporal domain for video signal and mesh-structure constraint enforcement in the spatial domain, beside, this paper innovatively jointly utilities the subsampling convolution, upsampling convolution and luckiness threshold value based back propagation, which takes video deblurring as a high-precision adaptive convolution. As a result, our new approach can adaptively improve the clarity of the video, and synchronously optimize the camera trajectory of wobbly video. To validate our optimization approach for adaptive video deblurring, we conduct comprehensive experiments on public benchmarks, and make extensive and quantitative evaluations with available state-of-the-art methods as well as popular commercial software. All of our experiments demonstrate the advantages of the optimization method in terms of versatility, accuracy, and efficiency.

关键词： Neural network Adaptive video deblurring Temporal alignment Spatial consistency alignment Luckiness threshold value

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