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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Mingrui Liu Xiaoxuan Zhang Lijun Zhang Rong Jin Tianbao Yang Department of Computer Science The University of Iowa Iowa City IA National Key Laboratory for Novel Software Technology Nanjing University China Machine Intelligence Technology Alibaba Group Bellevue WA

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention for developing optimization algorithms with fast convergence. However, the studies of EBC in statistical learning are hitherto still limited. The main contributions of this paper are two-fold. First, we develop fast and intermediate rates of empirical risk minimization (ERM) under EBC for risk minimization with Lipschitz continuous, and smooth convex random functions. Second, we establish fast and intermediate rates of an efficient stochastic approximation (SA) algorithm for risk minimization with Lipschitz continuous random functions, which requires only one pass of n samples and adapts to EBC. For both approaches, the convergence rates span a full spectrum between Õ(1/√n) and Õ(1/√n) depending on the power constant in EBC, and could be even faster than O(1/√n) in special cases for ERM. Moreover, these convergence rates are automatically adaptive without using any knowledge of EBC.

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MFPR: A Personalized Ranking Recommendation with Multiple Feedback

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ACM Transactions on Social Computing 2018年第2期1卷 1–22页

作者： Shi, Chuan Liu, Jian Zhang, Yiding Hu, Binbin Liu, Shenghua Yu, Philip S. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xi Tu Cheng Road Beijing China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing China Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago United States

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users' behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance. © 2018 ACM.

关键词： Recommender systems

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Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization

Combining Word Embedding and Knowledge-Based Topic Modeling ...

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International Conference on Semantic Computing

作者： Seyedamin Pouriyeh Mehdi Allahyari Krys Kochut Gong Cheng Hamid Reza Arabnia Computer Science Department University of Georgia Athens GA USA Computer Science Department Georgia Southern University Statesboro USA National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Word embedding is becoming more popular in the Semantic Web community as an effective approach for capturing semantics in various contexts. In this paper, we combine word embedding and topic modeling to model RDF data for the entity summarization task. In our model, ES-LDAext, which is the extended version of our previous model, we utilize the word embedding to supplement the RDF data before applying entity summarization. In addition, in the model presented here, we use RDF literals as a very good source of information to create more reliable and representative summaries for entities. To do that, we use the Named Entity Recognition approach to extract entities within literals before feeding them into the word embedding model to enrich the RDF data. Experimental results demonstrate the effectiveness of the proposed model.

关键词： Resource description framework Data models Mathematical model Semantics Computational modeling Task analysis Data mining

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Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

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science China(Information sciences) 2018年第3期61卷 118-130页

作者： Wei LI Vincent RIJMEN Zhi TAO Qingju WANG Hua CHEN Yunwen LIU Chaoyun LI Ya LIU School of Computer Science and Technology Donghua University imec-COSIC KU Leuven Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Applied Mathematics and Computer Science Technical University of Denmark Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences College of Science National University of Defense Technology Department of Computer Science and Engineering University of Shanghai for Science and Technology

With the expansion of wireless technology, vehicular ad-hoc networks(VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device(LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections.

关键词： VANETs LED lightweight cipher impossible meet-in-the-middle fault analysis

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Secure Beamforming Design for MISO SWIPT Systems: An Indirectly Optimized Approach

Secure Beamforming Design for MISO SWIPT Systems: An Indirec...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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Secure keyboards against motion based keystroke inference attack 12th

Secure keyboards against motion based keystroke inference at...

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12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016

作者： Du, Shaoyong Gao, Yue Hua, Jingyu Zhong, Sheng State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783319596075

Nowadays, attackers seek various covert channels to access the users’ privacy on the mobile devices. Recent research has demonstrated that the built-in motion sensors can be exploited to monitor the users’ screen taps and infer what they have typed. This paper presents several practical and convenient countermeasures against this attack in terms of the soft keyboard. We find that this attack is sensitive to the motion noise of the mobile device and the layout variation of the soft keyboard. We, thus, present two kinds of countermeasures against this attack by introducing vibration noise in sensor readings and dynamics in the keyboard layout, respectively. We implement these countermeasures on Android platform and recruit 20 volunteers to evaluate these countermeasures’ effectiveness and usability on both the smartphones and tablets. The results show that the proposed countermeasures can effectively reduce the attackers’ keystroke inference accuracy without significantly hurting the typing efficiency. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2017.

关键词： computers

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A Hybrid Replacement Strategy for MOEA/D 13th

A Hybrid Replacement Strategy for MOEA/D

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Chen, Xiaoji Shi, Chuan Zhou, Aimin Xu, Siyong Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University Shanghai200062 China

ISBN: (纸本)9789811328251

In MOEA/D, the replacement strategy plays a key role in balancing diversity and convergence. However, existing adaptive replacement strategies either focus on neighborhood or global replacement strategy, which may have no obvious effects on balance of diversity and convergence in tackling complicated MOPs. In order to overcome this shortcoming, we propose a hybrid mechanism balancing neighborhood and global replacement strategy. In this mechanism, a probability threshold is applied to determine whether to execute a neighborhood or global replacement strategy, which could balance diversity and convergence. Furthermore, we design an offspring generation method to generate the high-quality solution for each subproblem, which can ease mismatch between subproblems and solutions. Based on the classic MOEA/D, we design a new algorithm framework, called MOEA/D-HRS. Compared with other state-of-the-art MOEAs, experimental results show that the proposed algorithm obtains the best performance. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Wang, Ruoyun Chen, Fanxuan Chen, Haoman Lin, Chenxing Shuai, Jincen Wu, Yutong Ma, Lixiang Hu, Xiaoqu Wu, Min Wang, Jin Zhao, Qi Shuai, Jianwei Pan, Jingye Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China Wenzhou Medical University Wenzhou China UCSC Baskin School of Engineering University of California Santa Cruz CA United States Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston MA United States Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China Stony Brook University Stony Brook NY United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging. © 2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Attitude Control of Rigid Body with Inertia Uncertainty and Saturation Input

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Tsinghua science and technology 2017年第1期22卷 83-91页

作者： Xi Ma Fuchun Sun Hongbo Li Bing He Department of Computer Science and Technology Tsinghua UniversityState Key Lab of Intelligent Technology and Systemsand Tsinghua National Laboratory for Information Scienceand Technology (TNList) Department of Aerospace Engineering High-Tech Institute of Xi’an

In this paper, the attitude control problem of rigid body is addressed with considering inertia uncertainty,bounded time-varying disturbances, angular velocity-free measurement, and unknown non-symmetric saturation input. Using a mathematical transformation, the effects of bounded time-varying disturbances, uncertain inertia,and saturation input are combined as total disturbances. A novel finite-time observer is designed to estimate the unknown angular velocity and the total disturbances. For attitude control, an observer-based sliding-mode control protocol is proposed to force the system state convergence to the desired sliding-mode surface; the finite-time stability is guaranteed via Lyapunov theory analysis. Finally, a numerical simulation is presented to illustrate the effective performance of the proposed sliding-mode control protocol.

关键词： attitude control inertial uncertainty angular velocity-free measurement saturation input finite-time observer sliding-mode control

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Segmentation Guided HEp-2 Cell Classification with Adversarial Networks

Segmentation Guided HEp-2 Cell Classification with Adversari...

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Computing, Communications and Applications Conference (ComComAp)

作者： Hai Xie Yejun He Haijun Lei Jong Yih Kuo Baiying Lei Guangdong Engineering Research Center of Base Station Antennas and Propagation Shenzhen Key Lab of Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China Guangdong Province Key Laboratory of Popular High-performance Computers School of Computer and Software Engineering Shenzhen University Computer Science and Information Engineering National Taipei University of Technology Taiwan China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University China

ISBN: (数字)9781728119731

ISBN: (纸本)9781728119748

The analysis of indirect immuno fluorescence (IIF) on human epithelial type 2 (HEp-2) cells is of paramount importance for the autoimmune diseases diagnosis. Essentially, accurate segmentation masks can generate rich boundary information, which is beneficial for improving the performance of the classification task For this reason, this paper proposes a novel segmentation guided HEp-2 cell classification method via generative adversarial networks (GANs), which employs the GANs as the segmentor to generate accurate masks for the subsequent classification task. Specifically, the proposed network architecture consists of three modules (i.e., the generator, discriminator and classifier). The first two modules constitute GANs model, which is trained to obtain better segmentation results via playing a min-max game. The segmentation masks and the corresponding original images are fed to the third module together to identify the category of the trained cell. Furthermore, the Xception and ResNet-50 model are used as the backbone of the segmentation and classification network, respectively. Besides, an improved classification loss function via Gaussian Mixture (GM) is proposed to optimize the classification network The proposed architecture can learn rich boundary information and well represent the class label of HEp2 cell images. Experimental results on the HEp-2 International Conference on Pattern Recognition (ICPR) 2016 task1 dataset demonstrate our proposed model achieves quite promising performance.

关键词： Image segmentation computer architecture Task analysis Microprocessors Gallium nitride Feature extraction Generators

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