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O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

Journal of Computer Science & Technology 2018年第5期33卷 1023-1038页

作者： Ji-Zhou Luo Sheng-Fei Shi Guang Yang Hong-Zhi Wang Jian-Zhong Li School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application Shenzhen 518000 China

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

关键词： overlap interval join temporal relation overlap inverted index join algorithm

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Adaptive rate-distortion prediction for multiple reference selection and inter-mode decision

Adaptive rate-distortion prediction for multiple reference s...

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13th Pacific-Rim Conference on Multimedia, PCM 2012

作者： Zhao, Tiesong Zhang, Yun Kwong, Sam Wang, Hanli Chen, Qian Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9783642347771

To minimize Rate-Distortion (RD) cost in video coding, RD Optimization (RDO) technique is adopted, which also brings increasingly computational complexity in Motion Estimation (ME), multiple reference selection and mode decision. In this paper, a Gaussian model based adaptive RD cost prediction method is developed to jointly speed up multiple reference selection and inter-mode decision. Besides the ability of achieving good coding efficiency with much reduced computational power, our method is also with two major advantages. First, it can achieve robust performance, including computational complexity reduction and almost intact video quality, with a wide range of video sequences resolutions and even for various scene changes;second, it could be either individually used or combined with the other efficient mode decision algorithms, and in both cases, our method could achieve remarkable performance, which is revealed by exhaustive experimental results. © 2012 Springer-Verlag.

关键词： Image coding

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms Data models Task analysis Business

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Phase Engineering of Nanomaterials: Metal Nanomaterials

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Chemical Research in Chinese Universities 2025年 1-44页

作者： Chen, Ye Liu, Jiawei Yun, Qinbai Cheng, Hongfei Cui, Xiaoya Fan, Zhanxi Fu, Lei Gao, Chuanbo Ge, Jingjie Ge, Yiyao Guo, Shaojun Han, Sumei Hong, Xun Huang, Bolong Huang, Hongwen Huang, Xiao Huang, Xiaoqing Liao, Xiaozhou Ling, Chongyi Liu, Dong Lu, Yang Lu, Qipeng Niu, Wenxin Saleem, Faisal Shao, Minhua Shao, Qi Shi, Zhenyu Song, Li Sun, Shouheng Tilley, Richard D. Wang, Deli Wang, An-Liang Wang, Jinlan Xi, Pinxian Xia, Younan Xiong, Yujie Yang, Nailiang Yin, Pengfei Yu, Yifu Zhang, Zhicheng Zhou, Meng Zhu, Ye Zhang, Hua Department of Chemistry The Chinese University of Hong Kong Hong Kong SAR P. R. China Hong Kong Branch of National Precious Metals Material Engineering Research Center (NPMM) City University of Hong Kong Kowloon Hong Kong SAR P. R. China Department of Chemical and Biological Engineering & Energy Institute The Hong Kong University of Science and Technology Hong Kong SAR P. R. China Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials Institute of New Energy for Vehicles School of Materials Science and Engineering Tongji University Shanghai P. R. China Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources School of Materials Science and Technology China University of Geosciences Beijing P. R. China Hebei Key Laboratory of Resource Low-carbon Utilization and New Materials China University of Geosciences Beijing P. R. China Department of Chemistry City University of Hong Kong Kowloon Hong Kong SAR P. R. China Hong Kong Institute for Clean Energy City University of Hong Kong Kowloon Hong Kong SAR P. R. China City University of Hong Kong Shenzhen Research Institute Shenzhen P. R. China College of Chemistry and Molecular Sciences Wuhan University Wuhan P. R. China The Institute for Advanced Studies Wuhan University Wuhan P. R. China Sate Key Laboratory of Multiphase Flow in Power Engineering Frontier Institute of Science and Technology Xi’an Jiaotong University Xi’an P. R. China Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Kowloon Hong Kong P. R. China State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing P. R. China School of Materials Science and Engineering Peking University Beijing P. R. China School of Materials Science and Engineering University of Science and Technology Beijing Beijing P. R. China Center of Advanced Nanocatalysis (CAN) Department of Applied Chemist

Phase is a fundamental structural parameter that distinguishes the atomic arrangement in materials. The recent advancement in phase engineering of nanomaterials (PEN) has witnessed the discovery and intriguing physicochemical properties of a number of unconventional phases in metal nanomaterials, enabling their promising applications in catalysis, optics, and so on. With the aid of advanced characterization techniques and theoretical calculations, phase engineering of metal nanomaterials has made significant progress in terms of precisely controlled synthesis, phase transformation, and phase-dependent property studies. This review summarizes the recent progress of PEN with a focus on metal nanomaterials. First, we introduce various synthetic strategies to prepare unconventional-phase metal nanomaterials, including monometallic and multimetallic nanomaterials. Second, we discuss methods to realize phase transformation of metallic nanomaterials. Then, we demonstrate various phase-dependent properties and applications of metal nanomaterials. Last, current challenges and exciting opportunities in the phase engineering of metal nanomaterials are discussed.

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Dynamic Attribute-Based Access Control in Cloud Storage Systems

Dynamic Attribute-Based Access Control in Cloud Storage Syst...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Zechao Liu Zoe L. Jiang Xuan Wang S.M. Yiu Chunkai Zhang Xiaomeng Zhao Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Guangdong Provincial Key Laboratory of High Performance Computing Public Service Platform of Mobile Internet Application Security Industry The University of Hong Kong HKSAR China

ISBN: (纸本)9781509032068

Cloud storage service allows data owner to store their big data in the cloud and provides data access to the users. As the cloud server is not trustworthy, we cannot rely on the server to conduct data access control. To protect data security and privacy, Attribute-Based Encryption (ABE) is a promising technique for data access control in cloud storage, because it provides data owner more direct control on access policies. However, there are two dynamic issues, namely attribute revocation and policy updating, that should be solved first before deploying ABE in practice. In this paper, we design a dynamic attribute-based access control scheme, which can solve the above two problems simultaneously. Besides, our scheme can support large universe of attributes, which makes it more available in cloud storage system. The proposed scheme is proved statically secure in random oracle model.

关键词： Cloud computing Access control Servers Cryptography Adaptation models Big data

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Towards efficient processing and learning with spikes: New approaches for multi-spike learning

arXiv

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arXiv 2020年

作者： Yu, Qiang Li, Shenglan Tang, Huajin Wang, Longbiao Dang, Jianwu Chen Tan, Kay Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Technology Zhejiang University China Co. Ltd. Tianjin China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remains as a challenging problem. In this paper, we make our contributions towards this direction. A simplified spiking neuron model is firstly introduced with effects of both synaptic input and firing output on membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks including association, classification, feature detection. In addition to efficiency, our learning rules demonstrate a high robustness against strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably single neuron is capable of solving multi-category classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules can not only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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CSformer: Bridging Convolution and Transformer for Compressive Sensing

arXiv

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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

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Generalized Visual Quality Assessment of GAN-Generated Face Images

arXiv

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arXiv 2022年

作者： Tian, Yu Ni, Zhangkai Chen, Baoliang Wang, Shiqi Wang, Hanli Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Tongji University Shanghai 200092 China The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation"

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IEEE transactions on cybernetics 2024年第11期54卷 7116页

作者： Lei Zhou Liang Feng Kay Chen Tan Jinghui Zhong Zexuan Zhu Kai Liu Chao Chen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Department of Computer Science City University of Hong Kong kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

关键词： Evolutionary computation Knowledge transfer Cybernetics

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