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International Conference on E-Business and Information System Security, EBISS

作者： Bin Liang Jianjun Huang Fang Liu Dawei Wang Daxiang Dong Zhaohui Liang Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China School of Information Renmin University of China Beijing China Renmin University of China Beijing CN

In recent years, Web sites have already become the attackers' main target. When attackers embed malicious code in the Web pages, they generally change the display mode of the corresponding HTML tags to make the display effect of malicious code invisible or almost invisible to the browser users. In this paper, the concept of abnormal visibility is proposed to describe the display feature setting of malicious code embedded. According to the concept, a malicious code detection method based on abnormal visibility recognition is designed and a prototype system is implemented. Compared to traditional methods and systems, the method has higher efficiency and less maintenance cost. Besides, a special-purpose JavaScript interpreter is implemented to get the execution output of browser-end scripts that are often used to generate malicious code dynamically by attackers. Experiments show that this system can detect most of the malicious Web pages efficiently and at the same time locate the malicious code in the source code accurately.

关键词： Web pages Displays Java Prototypes HTML Costs Invasive software Fingerprint recognition Laboratories data engineering

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Performance study on logging to another main memory database

Performance study on logging to another main memory database

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International Conference on New Trends in Information and Service Science, NISS

作者： Xiongpai Qin Xiaoyun Zhou Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China Department of Computer Science and Technology Xuzhou Normal University Jiangsu 221008 China

In update intensive main memory database applications, huge volume of log records is generated, to maintain the ACID properties of the database system, the log records should be persistent efficiently. Delegating logging of one main memory database to another main memory database is proposed. The scheme is elaborated in detail in terms of architecture, logging & safeness levels, checkpointing, and recovery. Strict durability and relax durability are provided. When some form of non-volatile memory is used to temporarily holding log records, not only logging efficiency is improved, but also the scheme could guarantee full ACID of the system. We also propose using parallel logging to speedup log persistence by writing logs to multiple disks in parallel. Since interconnection network techniques progress by leaps and bounds, the scheme eliminates the concern about whether the system's overall performance may be slowed down by bandwidth and latency limitations. Experiment results demonstrate the feasibility of the proposal.

关键词： Checkpointing database systems Transaction databases Network servers Availability Nonvolatile memory Writing Communication industry Defense industry Wood industry

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Formal Analysis of Trusted Computing: One Case Study

Formal Analysis of Trusted Computing: One Case Study

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WRI International Conference on Communications and Mobile Computing, CMC

作者： HongWei Zhou JinHui Yuan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Zhengzhou Information Engineering University of China Zhengzhou China

LS 2 is the logic to reason about the property of trusted computing. However, it lacks the capability of modeling the isolation provided by virtualization which is often involved in previous trusted computing system. With the support of changed LS 2 , we model three types of isolation. Moreover, we formally analyze the integrity measurement property of TrustVisor proposed recently which provides the isolated execution environment for security-sensitive code.

关键词： TV Computational modeling Analytical models Virtual machining Time measurement Access control

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Label Enhanced Event Detection with Collective knowledge and Heterogeneous Graph

Label Enhanced Event Detection with Collective Knowledge and...

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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Zhenya Lu Zhuochun Miao Xingrui Zhuo Zan Zhang Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages.

关键词： knowledge engineering Event detection Text recognition Semantics Syntactics Linguistics Transformers

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Multi-Label classification with Missing Labels by Preserving Feature-Label Space Consistency

Multi-Label classification with Missing Labels by Preserving...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zan Zhang Depeng Zhang Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-label classification deals with the problem where an instance is associated with multiple labels simultaneously. Most existing multi-label classification algorithms assume that the labels of the training data are complete. However, we can obtain only a partial label set of each instance in some real applications since labelling data is difficult or costly. Some existing works on multi-label classification with missing labels focus on exploiting label correlations to complete the original label space and simultaneously build a multi-label learning model using label specific features. However, these methods may be suboptimal since they do not preserve feature-label space consistency. In this paper, we propose a Space Consistency-based Multi-Label classification algorithm named SCML to address this issue. First, label correlation in label space is learned to augment the incomplete original label matrix to a new supplementary label matrix, and the multi-label classifier is constructed simultaneously based on the new supplementary label matrix. Then, correlation information in feature space is learned based on the probabilistic neighborhood similarities to preserve feature-label space consistency. Moreover, the proposed algorithm has an effective mechanism for learning label-specific features to improve the multi-label classification with missing labels. Extensive experiments on twelve benchmark data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art algorithms of multi-label learning with missing labels.

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Iterative Context-Aware Graph Inference for Visual Dialog

Iterative Context-Aware Graph Inference for Visual Dialog

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dan Guo Hui Wang Hanwang Zhang Zheng-Jun Zha Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Nanyang Technological University University of Science and Technology of China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-K message passing mechanism. Specifically, in every message passing step, each node selects the most K relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method.

关键词： Visualization History Task analysis Semantics Message passing Neural networks Cognition

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B-IRP: An Improving Integrity Report Protocol for Remote Attestation

B-IRP: An Improving Integrity Report Protocol for Remote Att...

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WRI International Conference on Communications and Mobile Computing, CMC

Trusted platform module (TPM) has little computation capability, and it is the performance bottleneck of remote attestation. In the scenario where the server is the attestation-busy entity which answers attestation requirement frequently, the massive delay is inevitable. Without the modification to TPM, we propose Batch Integrity Report Protocol (BIRP) to overcome the performance bottleneck. B-IRP bundles these requirements in interval as a batch requirement, and creates the response messages for these requirements with one expensive TPM operation. Discussion shows that B-IRP has improved the performance without loss of security.

关键词： Protocols Servers Security Software Software measurement Computer architecture Delay

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yu Huang Ru Wang Zhuqin Wei Guoxin Wang Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology School of Machanical Engineering Beijing Institute of Technology Beijing China

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

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Automatic data distribution in large-scale OLTP applications

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2014年第4期7卷 37-46页

作者： Wang, Xiaoyan Fan, Xu Chen, Jinchuan Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information and Electrical Engineering Ludong University Yantai 264025 China School of Information Renmin University of China Beijing 100872 China

With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abstracts it as a general triangle model called DaWN. Based on data and workload analysis, it presents a novel strategy called ADDS for automatic data distribution in OLTP applications. According to the results of a series of experiments over TPC-C datasets and transactions, the proposed approach shows effective improvements.© 2014 SERSC.

关键词： data handling

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Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images

Uncertainty-based Model Acceleration for Cancer Classificati...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Zeyu Mao, Anyu Wu, Jialun Li, Yang Wang, Chunbao Ding, Caixia Gong, Tieliang Li, Chen Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China The First Affiliated Hospital of Xi'an Jiaotong University Department of Pathology Xi'an China Shaanxi Provincial Cancer Hospital Department of Pathology Xi'an China

ISBN: (纸本)9781665468190

Computational Pathology (CPATH) offers the possibility for highly accurate and low-cost automated pathological diagnosis. However, the high time cost of model inference is one of the main issues limiting the application of CPATH methods. Due to the large size of Whole-Slide Image (WSI), commonly used CPATH methods divided a WSI into a large number of image patches at relatively high magnification, then predicted each image patch individually, which is time-consuming. In this paper, we propose a novel Uncertainty-based Model Acceleration (UMA) method for reducing the time cost of model inference, thereby relieving the deployment burden of CPATH applications. Enlightened by the slide-viewing process of pathologists, only a few high-uncertain regions are regarded as 'suspicious' regions that need to be predicted at high magnification, and most of the regions in WSI are predicted at low magnification, thereby reducing the times of image patch extraction and prediction. Meanwhile, uncertainty estimation ensures prediction accuracy at low magnification. We take two fundamental CPATH classification tasks (i.e., cancer region detection and subtyping) as examples. Extensive experiments on two large-scale renal cell carcinoma classification datasets demonstrate that our UMA can significantly reduce the time cost of model inference while maintaining competitive classification performance. © 2022 IEEE.

关键词： Diseases

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