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Pivot probability induction for statistical machine translation with topic similarity

Journal of Computational Information Systems 2013年第20期9卷 8351-8359页

作者： Huang, Yanzhou Shi, Xiaodong Su, Jinsong Chen, Yidong Huang, Guimin Fujian Key Lab. of the Brain-Like Intelligent Systems Xiamen University Xiamen 361005 China Department of Cognitive Science School of Information Science and Technology Xiamen University Xiamen 361005 China School of Software Xiamen University Xiamen 361005 China Research Center on Data Science and Social Computing Guilin University of Electronic Technology Guilin 541004 China

Previous works employ the pivot language approach to conduct statistical machine translation when encountering with limited amount of bilingual corpus. Conventional solutions based upon phrase-table combination overlook the semantic discrepancy between the source-pivot corpus and pivot-target corpus and consequently lead to probability estimation inaccuracy for the induced translation rules. In this paper, the latent topic structure of the document-level training data is learned automatically and each phrase translation rule is assigned to a topic distribution. Furthermore, the phrase probability induction is carried out on the basis of the topic similarity, allowing the translation system to consider the semantic relatedness among different rules. Using BLEU as a metric of translation accuracy, we find out our system is capable of achieving a absolute improvement in in-domain test compared with the baseline system. © 2013 Binary Information Press.

关键词： Semantics

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WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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Accurate Latent Factor Analysis via Particle Swarm Optimizers

Accurate Latent Factor Analysis via Particle Swarm Optimizer...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jia Chen Xin Luo MengChu Zhou School of Cyber Science and Technology Beihang University Beijing China Chongqing Key Lab. of Big Data and Intelligent Computing and the Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chongqing School University of Chinese Academy of Sciences Chongqing China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.

关键词： Adaptation models Analytical models Computational modeling Refining Predictive models Sparse matrices Particle swarm optimization

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Context Shift Reduction for Offline Meta-Reinforcement Learning

arXiv

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arXiv 2023年

作者： Gao, Yunkai Zhang, Rui Guo, Jiaming Wu, Fan Yi, Qi Peng, Shaohui Lan, Siming Chen, Ruizhi Du, Zidong Hu, Xing Guo, Qi Li, Ling Chen, Yunji University of Science and Technology of China USTC Hefei China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China Cambricon Technologies China University of Chinese Academy of Sciences UCAS Beijing China Intelligent Software Research Center Institute of Software CAS Beijing China Shanghai Innovation Center for Processor Technologies SHIC Shanghai China

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent’s generalization ability on unseen tasks. However, the context shift problem arises due to the distribution discrepancy between the contexts used for training (from the behavior policy) and testing (from the exploration policy). The context shift problem leads to incorrect task inference and further deteriorates the generalization ability of the meta-policy. Existing OMRL methods either overlook this problem or attempt to mitigate it with additional information. In this paper, we propose a novel approach called Context Shift Reduction for OMRL (CSRO) to address the context shift problem with only offline datasets. The key insight of CSRO is to minimize the influence of policy in context during both the meta-training and meta-test phases. During meta-training, we design a max-min mutual information representation learning mechanism to diminish the impact of the behavior policy on task representation. In the meta-test phase, we introduce the non-prior context collection strategy to reduce the effect of the exploration policy. Experimental results demonstrate that CSRO significantly reduces the context shift and improves the generalization ability, surpassing previous methods across various challenging domains. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Thermal-aware Energy Efficient Task Scheduling Framework

Thermal-aware Energy Efficient Task Scheduling Framework

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jian Nong Jia Chen Yinqing Wang Wei Qin Xi He Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology Macau China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China High Performance Computing Lab Wuzhou University Wuzhou China

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.

关键词： Data centers Temperature distribution Schedules Processor scheduling Thermal management Ubiquitous computing Energy efficiency

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Abnormal network traffic detection approach based on alive entropy

引用

Tongxin Xuebao/Journal on Communications 2013年第SUPPL.2期34卷 51-57页

作者： Mu, Xiang-Kun Wang, Jin-Song Xue, Yu-Feng Huang, Wei Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

A novel alive entropy-based detection approach was proposed, which detects the abnormal network traffic based on the values of alive entropies. The alive entropies calculated based on the NetFlow data coming from the network traffic of input and output of a whole system, which is essentially a monitored network. In order to decrease false positive rate of abnormal network traffic, different scales are selected to compute the values of alive entropies in different sizes of network traffic. With the low false positive rate of abnormal network traffic, the abnormal network traffic can be effectively detected. Experiments carried out on a real campus network were used to evaluate the effectiveness of the proposed approach. A comparative study illustrates that the proposed approach may easily detect the abnormal network traffic with random characteristics in comparison with some "conventional" approaches reported in the literatures.

关键词： Entropy

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Research of deep packet inspection system based on the MapReduce

引用

Journal of Computational Information Systems 2013年第7期9卷 2587-2594页

作者： Wang, Jinsong Wei, Jun Lin, Sheng Huang, Wei Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

In this paper, we propose a deep packet inspection system based on the MapReduce. The MapReduce which is a parallel distributed programming model developed by Google applies the technology of deep packet inspection. Network protocol analysis technique is invoked to realize detect, effectively reducing the packet number to be matched. Furthermore, cache technology is adopted to store attack signature from database in order to reduce the frequency of reading the rules from library file which improved the detection efficiency. Experimental results show that effectiveness of the proposed system with the high-speed network traffic. Copyright © 2013 Binary Information Press.

关键词： MapReduce

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Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes

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Computers in Biology and Medicine 2024年 175卷 108441-108441页

作者： Su, Ran Han, Jingyi Sun, Changming Zhang, Degan Geng, Jie Wang, Ping Zeng, Xiaoyan School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China CSIRO Data61 EppingNSW 1710 Australia Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin China TianJin Chest Hospital Tianjin University No. 261 Taierzhuang South Road Jinnan District Tianjin China Tianjin Modern Innovative TCM Technology Co. Ltd. Tianjin300392 China The Affiliated Traditional Chinese Medicine Hospital Southwest Medical University Luzhou Sichuan646000 China

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of discovering synergistic drugs. Nowadays, most of the existing deep learning algorithms for anti-cancer drug synergy prediction use deep neural networks and can only implicitly perform feature interaction. This study proposes a deep learning algorithm, named MolCross, which combines implicit feature interaction with explicit features to improve the accuracy of prediction of the anti-cancer drug synergy score. MolCross uses a deep autoencoder to extract features from high-dimensional input, uses the drug-specific subnetworks and cross-network to perform implicit feature interaction and explicit feature interaction respectively, and finally uses a synergy prediction network to combine the two feature interaction methods to obtain the final prediction results. We adopted a five-fold cross validation and compared MolCross with other four anti-cancer drug synergy prediction models. The results show that MolCross has better prediction performance than other models. MolCross also has good performance in terms of cross-cell line and cross-tissue type. Existing studies have demonstrated that cancer molecular subtypes have different sensitivities to targeted therapy. In this study, the features of cancer molecular subtype were introduced in the model using an embedding layer in MolCross to explore the effect of cancer molecular subtype on anti-cancer drug synergy. We also found that the cancer molecular subtype is one of the main factors affecting the synergy between drugs. © 2024 Elsevier Ltd

关键词： Forecasting

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data

An ant colony optimization algorithm for learning brain effe...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinduo Liu Junzhong Ji Aidong Zhang Peipeng Liang Beijing Municipal Key Laboratory of Multimedia and Intelligent Software the College of Computer Science and Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering University at Buffalo The State University of New York Buffalo America Beijing Key Lab of MRI and Brain Informatics Department of Radiology Capital Medical University Beijing China

ISBN: (纸本)9781509016129

Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects.

关键词： Ant colony optimization Bayes methods Measurement Mathematical model Brain modeling Optimization Electronic mail

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