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Enhance Information Flow Tracking with Function Recognition

China Communications 2010年第6期7卷 24-29页

作者： Zhou Kan Huang Shiqiu Huang Shan Qi Zhengwei Gu Jian Guan Haibing School of software Shanghai Jliao Tong University Shanghai 200240 P. R. China Key Lab of Information Network Security Ministry of Public Security Department of Computer Science ＆ Engineering Shanghai Jiao Tong University Shanghai 200240 P. R China Shanghai Key Laboratory of Scalable Computing ＆ System Shanghai P. R. China

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

关键词： function recognition information flow tracking program behavior e-evidence.

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An Empirical Study on Development Set Selection Strategy for Machine Translation Learning∗ 5

An Empirical Study on Development Set Selection Strategy for...

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Joint 5th Workshop on Statistical Machine Translation and MetricsMATR, WMT 2010 at the 48th Conference of the Associationfor Computational Linguistics, ACL 2010

作者： Hui, Cong Zhao, Hai Song, Yan Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China Department of Chinese Translation and Linguistics City University of Hong Kong Hong Kong

ISBN: (纸本)9781932432718

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main contribution in this work is about effective parameter tuning. We discover that there is a significant performance gap as different development sets are adopted. Finally, ten groups of development sets are used to optimize the model weights, and this does help us obtain a stable evaluation result. © 2010 Association for Computational Linguistics

关键词： Machine translation

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A Translation Framework for Virtual Execution Environment on CPU/GPU Architecture

A Translation Framework for Virtual Execution Environment on...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Guoxing Dong Kai Chen Erzhou Zhu Yichao Zhang Zhengwei Qi Haibing Guan School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

GPUs are many-core processors with tremendous computational power. However, as automatic parallelization has not been realized yet, developing high-performance parallel code for GPUs is still very challenging. The paper presents a novel translation framework designed for virtual execution environment based on CPU/GPU architecture. It addresses two major challenges of taking advantage of general purpose computation on graphics processing units (GPGPU) to improve performance: no rewriting the existing source code and resolving binary compatibility issues between different GPUs. The translation framework uses semi-automatic parallelization technology to port existing code to explicitly parallel programming models. It not only offers a mapping strategy from X86 platform to CUDA programming model, but also synchronizes the execution between the CPU and the GPUs. The input to our translation framework is parallelizable part of the program within binary code. With an additional information related to the parallelizable part, the translation framework transforms the sequential code into PTX code and execute it on GPUs. Experimental results on several programs from CUDA SDK Code Samples and Parboil Benchmark Suite show that our translation framework could achieve very high performance, even up to several tens of times speedup over the X86 native version.

关键词： Graphics processing unit Registers Kernel Instruction sets Driver circuits Programming computer architecture

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Performance Tuning Towards a KVM-Based Low Latency Virtualization System

Performance Tuning Towards a KVM-Based Low Latency Virtualiz...

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International Conference on Information Engineering and computer science (ICIECS)

作者： Baojing Zuo Kai Chen Alei Liang Haibing Guan Jun Zhang Ruhui Ma Hongbo Yang School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Utilizing virtualization technology to combine real-time operating system(RTOS)and off-the-shelf time-sharing general purpose operating system (GPOS)is attracting much more interest *** combination has the potential to provide a large application base,and to guarantee timely deterministic response to real-time applications,yet there is no convincible experimental result about its real-time *** this paper,we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings,and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable ***,we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks,so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system.

关键词： Linux Real time systems Kernel Tuning Throughput Hardware

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A Parallel Skeleton Library for Embedded Multicores

A Parallel Skeleton Library for Embedded Multicores

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International Conference on Parallel Processing Workshops (ICPPW)

作者： Xin Liu Jingyu Zhou Daqiang Zhang Yao Shen Minyi Guo EPCC Department of Computer Science MOE-MS Key Laboratory of Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China

Many SoCs adopt multicore architectures. As a result, embedded programmers are also facing the challenge of parallel programming. We propose a parallel skeleton library that can be used on embedded multicores. Our library is implemented in standard C++ using template features. We propose two parallel skeletons to support common program patterns on multicores. In our skeleton library, programmers can easily choose underlying parallel implementations with no code changes. Experimental results show that many applications can take advantage of these two skeletons for performance improvement, sometimes better than hand-parallelized code.

关键词： Skeleton Libraries Multicore processing Kernel System-on-a-chip Runtime Computational modeling

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Vigilance Analysis Based on Continuous Wavelet Transform of EEG Signals

Vigilance Analysis Based on Continuous Wavelet Transform of ...

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International Conference on Biomedical Engineering and computer science

作者： Tian Ouyang Hong-Tao Lu MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781424453153

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. The proposed framework explores the use of continuous wavelet transform in EEG signal processing. A large set of features is extracted from the wavelet coefficients, which are computed from EEG signals with multiple wavelet functions. We use random forest to rank the plenty of features and select the most important ones for later classification. Samples of EEG data are then trained and classified by SVM (Support Vector Machine). On datasets acquired from 5 subjects, our method reveals high classification accuracy (over 96%).

关键词： Electroencephalography (EEG) Vigilance Alert Drowsy Sleep Continuous Wavelet Transform Random Forest SVM

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Vigilance detection based on sparse representation of EEG

Vigilance detection based on sparse representation of EEG

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hongbin Yu Hongtao Lu Tian Ouyang Hongjun Liu Bao-Liang Lu Department of Computer Science and Engineering Shanghai Jiaotong University China MOE-Microsoft Laboratory of Intelligent Computing and Intelligent Systems and the Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become successful tools in the fields of signal reconstruction and machine learning. In this paper, we propose to use the sparse representation of EEG to the vigilance detection problem. We first use continuous wavelet transform to extract the rhythm features of EEG data, and then employ the sparse representation method to the wavelet transform coefficients. We collect five subjects' EEG recordings in a simulation driving environment and apply the proposed method to detect the vigilance of the subjects. The experimental results show that the algorithm framework proposed in this paper can successfully estimate driver's vigilance with the average accuracy about 94.22 %. We also compare our algorithm framework with other vigilance estimation methods using different feature extraction and classifier selection approaches, the result shows that the proposed method has obvious advantages in the classification accuracy.

关键词： Electroencephalography Feature extraction Accuracy Training Continuous wavelet transforms Sleep

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A comparative study on two large-scale hierarchical text classification tasks' solutions

A comparative study on two large-scale hierarchical text cla...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jian Zhang Hai Zhao Bao-Liang Lu Center of Brain-Like Computing and Machine Intelligence(ÇïêDepartment of Computer Science and Engineering Shanghai Jiaotong University Shanghai China MOE-Microsoft Key Laboratory of Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely hierarchical support vector machine (SVM) and k nearest neighbor (k-NN) that are applied to a LSHTC task. Experiment results show that the latter outperforms the former in this LSHTC task, which is quite different from the usual results for normal text categorization tasks. Then this paper does a comparative study on different similarity measures and ranking approaches in k-NN framework for LSHTC task. Conclusions can be drawn that k-NN is more appropriate for the LSHTC task than hierarchical SVM and for a specific LSHTC task. BM25 outperforms other similarity measures and List Weak gains a better performance than other ranking approaches. We also find an interesting phenomenon that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.

关键词： Patents Support vector machines Text categorization Nearest neighbor searches Training Classification algorithms Taxonomy

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Online multiple instance learning with no regret

Online multiple instance learning with no regret

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Mu Li James T. Kwok Bao-Liang Lu Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China

Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been used in a wide range of applications including image classification, object detection and object tracking. Typically, MI algorithms are trained in a batch setting in which the whole training set has to be available before training starts. However, in applications such as tracking, the classifier needs to be trained continuously as new frames arrive. Motivated by the empirical success of a batch MI algorithm called MILES, we propose in this paper an online MI learning algorithm that has an efficient online update procedure and also performs joint feature selection and classification as MILES. Besides, while existing online MI algorithms lack theoretical properties, we prove that the proposed online algorithm has a (cumulative) regret of O(√T), where T is the number of iterations. In other words, the average regret goes to zero asymptotically and it thus achieves the same performance as the best solution in hindsight. Experiments on a number of MI classification and object tracking data sets demonstrate encouraging results.

关键词： Layout Image reconstruction Semiconductor device modeling Image segmentation Stereo vision Surface reconstruction Surface texture Surface fitting Stereo image processing Rendering (computer graphics)

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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