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IEEE International Symposium on Cyberspace Safety and Security

作者： Renhai Chen Yi Wang Jingtong Hu Duo Liu Zili Shao Yong Guan Embedded Systems and CPS Laboratory Department of Computing The Hong Kong Polytechnic University College of Computer Science and Software Engineering Shenzhen University School of Electrical and Computer Engineering Oklahoma State University College of Computer Science Chongqing University College of Computer and Information Management Capital Normal University

ISBN: (纸本)9781479989386

Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This paper targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this paper, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.

关键词： IO Virtualization Metadata Mobile devices virtual machines Cache Memory System degradation of performance Mobile Conference documents

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Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

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What do software design students understand about information hiding? A qualitative case study 14

What do software design students understand about informatio...

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14th Koli Calling International Conference on computing Education Research, Koli Calling 2014

作者： Flores, Pamela Medinilla, Nelson Pamplona, Sonia Computer Languages and Systems and Software Engineering Department Universidad Politécnica de Madrid Spain Computer Science Department Universidad a Distancia de Madrid Spain

ISBN: (纸本)9781450330657

The Information Hiding Principle is a modularization technique aimed at increasing the productivity of software development and maintenance. The Hiding Information concept introduced by Parnas is recognized as one of the key software design principles. However, as the author said, implementing the Principle is harder than it looks. This paper aims to discover what students understand and how they implement, on their designs, the Information Hiding Principle. The research was conducted through a qualitative case study with thirteen Master's students in the domain of software Design. Among the different concepts associated with understanding the Principle we could identify that some students adhere to the idea of dividing in order to hide, while others only care about hiding. Both of them without a successful implementation of the Principle. Few students have some idea about the Principle, however they do not get it all when implementing it. Furthermore, we can conclude that Parnas' statement remains until today. Copyright 2014 ACM.

关键词： software design

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Emotion recognition from thermal infrared images using deep Boltzmann machine

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Frontiers of Computer Science 2014年第4期8卷 609-618页

作者： Shangfei WANG Menghua HE Zhen GAO Shan HE Qiang JI School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab of Computing and Communicating Software of Anhui Province Hefei 230027 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180-3590 USA

Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameters extracted from the facial regions of interest or several hand-crafted features that are commonly used in visible spectrum. Till now there are no image features specially designed for thermal infrared images. In this paper, we propose using the deep Boltzmann machine to learn thermal features for emotion recognition from thermal infrared facial images. First, the face is located and normalized from the thermal infrared im- ages. Then, a deep Boltzmann machine model composed of two layers is trained. The parameters of the deep Boltzmann machine model are further fine-tuned for emotion recognition after pre-tralning of feature learning. Comparative experimental results on the NVIE database demonstrate that our approach outperforms other approaches using temperature statistic features or hand-crafted features borrowed from visible domain. The learned features from the forehead, eye, and mouth are more effective for discriminating valence dimension of emotion than other facial areas. In addition, our study shows that adding unlabeled data from other database during training can also improve feature learning performance.

关键词： emotion recognition thermal infrared images deep Boltzmann machine

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Fusion of visible and thermal images for facial expression recognition

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Frontiers of Computer Science 2014年第2期8卷 232-242页

作者： Shangfei WANG Shan HE Yue WU Menghua HE Qiang JI School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab of Computing and Communicating Software of Anhui Province Hefei 230027 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY12180-3590 USA

Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.

关键词： facial expression recognition feature-level fu-sion decision-level fusion support vector machine Bayesiannetwork thermal infrared images visible spectrum images

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On deploying relays for connected indoor sensor networks

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Journal of Communications and Networks 2014年第3期16卷 335-343页

作者： Zhu, Yanmin Xue, Cuiyao Cai, Haibin Yu, Jiadi Ni, Lei Li, Minglu Li, Bo Shanghai Key Lab. of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Software Engineering Institute East China Normal University China Shanghai Jiao Tong University Hong Kong University of Science and Technology Hong Kong

This paper considers the crucial problem of deploying wireless relays for achieving a connected wireless sensor network in indoor environments, an important aspect related to the management of the sensor network. Several algorithms have been proposed for ensuring full sensing coverage and network connectivity. These algorithms are not applicable to indoor environments because of the complexity of indoor environments, in which a radio signal can be dramatically degraded by obstacles such as walls. We first prove theoretically that the indoor relay placement problem is NP-hard. We then predict the radio coverage of a given relay deployment in indoor environments. We consider two practical scenarios;wire-connected relays and radio-connected relays. For the network with wire-connected relays, we propose an efficient greedy algorithm to compute the deployment locations of relays for achieving the required coverage percentage. This algorithm is proved to provide a Hn factor approximation to the theoretical optimum, where Hn = 1 +1/2+⋯+1/n= ln(n) +1, and n is the number of all grid points. In the network with radio-connected relays, relays have to be connected in an ad hoc mode. We then propose an algorithm based on the previous algorithm for ensuring the connectivity of relays. Experimental results demonstrate that the proposed algorithms achieve better performance than baseline algorithms. © 2014 KICS.

关键词： Approximation algorithms

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Large-scale supervised multimodal hashing with semantic correlation maximization 28

Large-scale supervised multimodal hashing with semantic corr...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014

作者： Zhang, Dongqing Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781577356790

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Semantics

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Supervised hashing with latent factor models 14

Supervised hashing with latent factor models

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37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014

作者： Zhang, Peichao Zhang, Wei Li, Wu-Jun Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450322591

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning optimization is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time. Copyright 2014 ACM.

关键词： Big data

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Coupled group lasso for web-scale CTR prediction in display advertising 31

Coupled group lasso for web-scale CTR prediction in display ...

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31st International Conference on Machine Learning, ICML 2014

作者： Yan, Ling Li, Wu-Jun Xue, Gui-Rong Han, Dingyi Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University China Alibaba Group China

ISBN: (纸本)9781634393973

In display advertising, click through rate (CTR) prediction is the problem of estimating the probability that an advertisement (ad) is clicked when displayed to a user in a specific context. Due to its easy implementation and promising performance, logistic regression (LR) model has been widely used for CTR prediction, especially in industrial systems. However, it is not easy for LR to capture the nonlinear information, such as the conjunction information, from user features and ad features. In this paper, we propose a novel model, called coupled group lasso (CGL), for CTR prediction in display advertising. CGL can seamlessly integrate the conjunction information from user features and ad features for modeling. Furthermore, CGL can automatically eliminate useless features for both users and ads, which may facilitate fast online prediction. Scalability of CGL is ensured through feature hashing and distributed implementation. Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on web- scale CTR prediction tasks. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Forecasting

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Distributed stochastic ADMM for matrix factorization 14

Distributed stochastic ADMM for matrix factorization

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Yu, Zhi-Qin Shi, Xing-Jian Yan, Ling Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450325981

Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy. Copyright 2014 ACM.

关键词： Recommender systems

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