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61.
针对自动文摘的信息冗余问题,提出了一种冗余语句消除的方法。利用《同义词词林》来定义词语语义距离计算公式,根据词语的相似度,建立主题词和主题句之间的一一对应关系,借用编码理论中海明距离的理论,得到了文摘中主题句的相似度,设置阈值过滤掉相似度较高的主题句,从而实现了主题句的约简。实验结果证明,该方法提高了文摘的精度。 相似文献
62.
63.
QIN Fei-zhou 《数字社区&智能家居》2008,(15)
计算机只能执行机器代码,只有专业程序人员才能较好地使用此类语言。因此编译程序已成为计算机系统的最重要的系统程序之一。本文主要通过对四则混合计算器的软件的设计分析讲述了编译程序的工作过程及具体实现。 相似文献
64.
Akila Varadarajan Nilesh Patel Bruce Maxim William I. Grosky 《Multimedia Tools and Applications》2008,40(2):211-239
There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical
approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity
in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes
with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level
indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to
handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query
are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results
from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby
facilitating high test scores.
Akila Varadarajan is a Senior Software Engineer at Motorola, IL with the Mobile devices division. Prior joining Motorola, she was a Software development intern at Autodesk, MI and Graduate Research assistant at University of Michigan - Dearborn. She received her MS in Computer Engineering from University of Michigan in 2006 and her BS in Computer Engineering from Madurai Kamaraj University, India in 2003. She is interested in Mobile computing - specifically Human Factors of Mobile Computing, Information retrieval and pattern recognition. Nilesh Patel is Assistant Professor in the department of Computer Science and Engineering at Oakland University, MI. He received his PhD and MS in Computer Science from Wayne State University, MI in 1997 and 1993. He is interested in Multimedia Information Processing - specifically audio and video indexing, retrieval and event detection, Pattern Recognition, Distributed Data Mining in a heterogeneous environment, and Computer Vision with special interest in medical imaging. Dr. Patel has also served in the automotive sector for several years and developed interest in Telematics and Mobile Computing. Bruce Maxim has worked as a software engineer for the past 31 years. He is a member of the Computer and Information Science faculty at the University of Michigan-Dearborn since 1985. He serves as the computing laboratory supervisor and head of the undergraduate programs in Computer Science, Software Engineering, and Information Systems. He has created more than 15 Computer and Information Science courses dealing with software engineering, game design, artificial intelligence, user interface design, web engineering, software quality, and computer programming. He has authored or co-authored four books on programming and software engineering. He has most recently served on the pedagogy subcommittee for Software Engineering 2004 and contributed to the IDGA Game Curriculum Framework 2008 guidelines. William I. Grosky is currently Professor and Chair of the Department of Computer and Information Science at University of Michigan - Dearborn, Dearborn, Michigan. Prior to joining the University of Michigan in 2001, he was Professor and Chair of the Department of Computer Science at Wayne State University, Detroit, Michigan. Before joining Wayne State University in 1976, he was an Assistant Professor in the Department of Information and Computer Science at Georgia Tech, Atlanta, Georgia. He received his B.S. in Mathematics from MIT in 1965, his M.S. in Applied Mathematics from Brown University in 1968, and his Ph.D. in Engineering and Applied Science from Yale University in 1971. 相似文献
William I. GroskyEmail: |
Akila Varadarajan is a Senior Software Engineer at Motorola, IL with the Mobile devices division. Prior joining Motorola, she was a Software development intern at Autodesk, MI and Graduate Research assistant at University of Michigan - Dearborn. She received her MS in Computer Engineering from University of Michigan in 2006 and her BS in Computer Engineering from Madurai Kamaraj University, India in 2003. She is interested in Mobile computing - specifically Human Factors of Mobile Computing, Information retrieval and pattern recognition. Nilesh Patel is Assistant Professor in the department of Computer Science and Engineering at Oakland University, MI. He received his PhD and MS in Computer Science from Wayne State University, MI in 1997 and 1993. He is interested in Multimedia Information Processing - specifically audio and video indexing, retrieval and event detection, Pattern Recognition, Distributed Data Mining in a heterogeneous environment, and Computer Vision with special interest in medical imaging. Dr. Patel has also served in the automotive sector for several years and developed interest in Telematics and Mobile Computing. Bruce Maxim has worked as a software engineer for the past 31 years. He is a member of the Computer and Information Science faculty at the University of Michigan-Dearborn since 1985. He serves as the computing laboratory supervisor and head of the undergraduate programs in Computer Science, Software Engineering, and Information Systems. He has created more than 15 Computer and Information Science courses dealing with software engineering, game design, artificial intelligence, user interface design, web engineering, software quality, and computer programming. He has authored or co-authored four books on programming and software engineering. He has most recently served on the pedagogy subcommittee for Software Engineering 2004 and contributed to the IDGA Game Curriculum Framework 2008 guidelines. William I. Grosky is currently Professor and Chair of the Department of Computer and Information Science at University of Michigan - Dearborn, Dearborn, Michigan. Prior to joining the University of Michigan in 2001, he was Professor and Chair of the Department of Computer Science at Wayne State University, Detroit, Michigan. Before joining Wayne State University in 1976, he was an Assistant Professor in the Department of Information and Computer Science at Georgia Tech, Atlanta, Georgia. He received his B.S. in Mathematics from MIT in 1965, his M.S. in Applied Mathematics from Brown University in 1968, and his Ph.D. in Engineering and Applied Science from Yale University in 1971. 相似文献
65.
Predictive modelling of online dynamic user-interaction recordings and community identification from such data becomes more
and more important with the widespread use of online communication technologies. Despite of the time-dependent nature of the
problem, existing approaches of community identification are based on static or fully observed network connections. Here we
present a new, dynamic generative model for the inference of communities from a sequence of temporal events produced through
online computer- mediated interactions. The distinctive feature of our approach is that it tries to model the process in a
more realistic manner, including an account for possible random temporal delays between the intended connections. The inference
of these delays from the data then forms an integral part of our state-clustering methodology, so that the most likely communities
are found on the basis of the likely intended connections rather than just the observed ones. We derive a maximum likelihood
estimation algorithm for the identification of our model, which turns out to be computationally efficient for the analysis
of historical data and it scales linearly with the number of non-zero observed (L + 1)-grams, where L is the Markov memory length. In addition, we also derive an incremental version of the algorithm, which could be used for
real-time analysis. Results obtained on both synthetic and real-world data sets demonstrate the approach is flexible and able
to reveal novel and insightful structural aspects of online interactions. In particular, the analysis of a full day worth
synchronous Internet relay chat participation sequence, reveals the formation of an extremely clear community structure. 相似文献
66.
Michail Vlachos Kun-Lung Wu Shyh-Kwei Chen Philip S. Yu 《Data mining and knowledge discovery》2008,16(1):109-133
We address the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases.
We follow a two-step methodology: first we identify the burst sections in our data and subsequently we store them for easy
retrieval in an efficient in-memory index. The burst detection scheme imposes a variable threshold on the examined data and
takes advantage of the skewed distribution that is typically encountered in many applications. The detected bursts are compacted
into burst intervals and stored in an interval index. The index facilitates the identification of correlated bursts by performing
very efficient overlap operations on the stored burst regions. We present the merits of the proposed indexing scheme through
a thorough analysis of its complexity. We also manifest the real-time response of our burst indexing technique, and demonstrate
the usefulness of the approach for correlating surprising volume trading events using historical stock data of the NY stock
exchange. While the focus of this work is on financial data, the proposed methods and data-structures can find applications
for anomaly or novelty detection in telecommunication, network traffic and medical data. 相似文献
67.
The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence? 相似文献
68.
This paper describes a new approach of heterogeneous data source fusion. Data sources are either static or active: static data sources can be structured or semi-structured, whereas active sources are services. In order to develop data sources fusion systems in dynamic contexts, we need to study all issues raised by the matching paradigms. This challenging problem becomes crucial with the dominating role of the internet. Classical approaches of data integration, based on schemas mediation, are not suitable to the World Wide Web (WWW) environment where data is frequently modified or deleted. Therefore, we develop a loosely integrated approach that takes into consideration both conflict management and semantic rules which must be enriched in order to integrate new data sources. Moreover, we introduce an XML-based Multi-data source Fusion Language (MFL) that aims to define and retrieve conflicting data from multiple data sources. The system, which is developed according to this approach, is called MDSManager (Multi-Data Source Manager). The benefit of the proposed framework is shown through a real world application based on web data sources fusion which is dedicated to online markets indices tracking. Finally, we give an evaluation of our MFL language. The results show that our language improves significantly the XQuery language especially considering its expressiveness power and its performances. 相似文献
69.
Service discovery is a critical task in distributed computing architectures for finding a particular service instance. Semantic
annotations of services help to enrich the service discovery process. Semantic registries are an important component for the
discovery of services and they allow for semantic interoperability through ontology-based query formulation and dynamic mapping
of terminologies between system domains. This paper evaluates two semantic registries—OWLJessKB implementation and instanceStore—to
determine the suitability of these with regards to the query response time and the overall scalability for use in mathematical
services. Mathematical ontologies from the MONET project are used to undertake comparison. The results demonstrate that the
performance of registries may be compared across two axes: (1) time to initialize (i.e. time to load an initial ontology into
memory); (2) time to query (i.e. time to reason with an ontology loaded into memory). 相似文献
70.
This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video
data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between
low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation
of the event classification and detection technique is presented and a future experiment to refine this technique is proposed.
These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation
scheme proposed in this paper.
相似文献
Bhavani ThuraisinghamEmail: |