With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to ...
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In this paper, we present a scalable implementation of a topic modeling (Adaptive Link-IPLSA) based method for online event analysis, which summarize the gist of massive amount of changing tweets and enable users to e...
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Moore's law continues to grant computer architects ever more transistors in the foreseeable future, and parallelism is the key to continued performance scaling in modern microprocessors. In this paper, the achievemen...
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Moore's law continues to grant computer architects ever more transistors in the foreseeable future, and parallelism is the key to continued performance scaling in modern microprocessors. In this paper, the achievements in our research project, which is supported by the National Basic Research 973 Program of China, on parallel architecture, are systematically presented. The innovative approaches and techniques to solve the significant problems in parallel architecture design are smnmarized, including architecture level optimization, compiler and language-supported technologies, reliability, power-performance efficient design, test and verification challenges, and platform building. Two prototype chips, a multi-heavy-core Godson-3 and a many-light-core Godson-T, are described to demonstrate the highly scalable and reconfigurable parallel architecture designs. We also present some of our achievements appearing in ISCA, MICRO, ISSCC, HPCA, PLDI, PACT, IJCAI, Hot Chips, DATE, IEEE Trans. VLSI, IEEE Micro, IEEE Trans. computers, etc.
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorith...
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With the increasing demand and the wide application of high performance commodity multi-core processors,both the quantity and scale of data centers grow dramatically and they bring heavy energy *** and engineers have ...
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With the increasing demand and the wide application of high performance commodity multi-core processors,both the quantity and scale of data centers grow dramatically and they bring heavy energy *** and engineers have applied much effort to reducing hardware energy consumption,but software is the true consumer of power and another key in making better use of *** software is critical to better energy utilization,because it is not only the manager of hardware but also the bridge and platform between applications and *** this paper,we summarize some trends that can affect the efficiency of data ***,we investigate the causes of software *** on these studies,major technical challenges and corresponding possible solutions to attain green system software in programmability,scalability,efficiency and software architecture are ***,some of our research progress on trusted energy efficient system software is briefly introduced.
Microblogs, a mixture of new media and social networks, is a hotbed of lurking users. It is meaningful to capture profiles for a lurking user in customized applications because a lurking user can receive all messages ...
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Microblogs, a mixture of new media and social networks, is a hotbed of lurking users. It is meaningful to capture profiles for a lurking user in customized applications because a lurking user can receive all messages except for sending few messages in microblogs. However, it is difficult to capture profiles for a lurking user because of lacking its user-generated contents. In this paper, we propose an approach to generate the lurking user's profiles by its followees' activities. In addition, we present a unified social context graph model to represent the lurking user's followees' activities. And the RWR algorithm is used to generate profiles of the lurking user in this graph model. Extensive experiments show that our approach can effectively determine profiles for lurking users.
Response-computable authentication (RCA) is a two-party authentication model widely adopted by authentication systems, where an authentication system independently computes the expected user response and authenticates...
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Response-computable authentication (RCA) is a two-party authentication model widely adopted by authentication systems, where an authentication system independently computes the expected user response and authenticates a user if the actual user response matches the expected value. Such authentication systems have long been threatened by malicious developers who can plant backdoors to bypass normal authentication, which is often seen in insider-related incidents. A malicious developer can plant backdoors by hiding logic in source code, by planting delicate vulnerabilities, or even by using weak cryptographic algorithms. Because of the common usage of cryptographic techniques and code protection in authentication modules, it is very difficult to detect and eliminate backdoors from login systems. In this paper, we propose a framework for RCA systems to ensure that the authentication process is not affected by backdoors. Our approach decomposes the authentication module into components. Components with simple logic are verified by code analysis for correctness, components with cryptographic/ obfuscated logic are sand boxed and verified through testing. The key component of our approach is NaPu, a native sandbox to ensure pure functions, which protects the complex and backdoor-prone part of a login module. We also use a testing-based process to either detect backdoors in the sand boxed component or verify that the component has no backdoors that can be used practically. We demonstrated the effectiveness of our approach in real-world applications by porting and verifying several popular login modules into this framework.
Video data location plays a key role for Peer-to-Peer (P2P) live streaming applications. In this paper, we propose a new one-hop Distributed Hash Table (DHT) lookup frarrework called Strearre ing-DHT (SDHT) to p...
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Video data location plays a key role for Peer-to-Peer (P2P) live streaming applications. In this paper, we propose a new one-hop Distributed Hash Table (DHT) lookup frarrework called Strearre ing-DHT (SDHT) to provide efficient video data location service. By adopting an enhanced events dissemination mechanism-EDRA+, the accuracy of routing table at peers can be guaranteed. More importantly, in order to enhance the perforlmnce of video data lookup operation without incurring extra overhead, we design a so-called Distributed Index Mapping and Management Mechanism (DIMM) for SDHT. Both analytical modeling and intensive simulation experiments are conducted to demonstrate the effectiveness of SDHT framework. Numerical results show that almost 90% requested video data can be retrieved within one second in SDHT based systems, and SDHT needs only 26% average bandwidth consumption when compared with similar one-hop DHT solutions such as D1HT. This indicates that SDHT framework is an appropriate data lookup solution for time-sensitive network applications such as P2P live streaming.
We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervise...
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We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervised learning (SSL) that has been deeply investigated in various visual classification tasks. However, most previous SSL approaches are not able to incorporate multiple descriptions for enhancing the model capacity. Furthermore, sample selection on unlabeled data was not advocated in previous studies, which may lead to unpredictable risk brought by real-world noisy data corpse. We propose a learning strategy for solving these two problems. As a core contribution, we propose a scalable semi-supervised multiple kernel learning method (S 3 MKL) to deal with the first problem. The aim is to minimize an overall objective function composed of log-likelihood empirical loss, conditional expectation consensus (CEC) on the unlabeled data and group LASSO regularization on model coefficients. We further adapt CEC into a group-wise formulation so as to better deal with the intrinsic visual property of real-world images. We propose a fast block coordinate gradient descent method with several acceleration techniques for model solution. Compared with previous approaches, our model better makes use of large scale unlabeled images with multiple feature representation with lower time complexity. Moreover, to address the issue of reducing the risk of using unlabeled data, we design a multiple kernel hashing scheme to identify the “informative” and “compact” unlabeled training data subset. Comprehensive experiments are conducted and the results show that the proposed learning framework provides promising power for real-world image applications, such as image categorization and personalized Web image re-ranking with very little user interaction.
Because the structure and function of a high-rise building is complex and the density of occupants is high, and the rescue from outside is very difficult, safe and timely evacuation is an important issue under high-ri...
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