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ADCS 2007 - Proceedings of the Twelfth Australasian Document Computing Symposium 2007年 64-71页

作者： Lu, Chengye Xu, Yue Geva, Shlomo School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD 4001 Australia

ISBN: (纸本)9780646484372

The extraction of Multiword Lexical Units (MLUs) in lexica is important to language related methods such as Natural Language Processing (NLP) and machine translation. As one word in one language may be translated into an MLU in another language, the extraction of MLUs plays an important role in Cross-Language Information Retrieval (CLIR), especially in finding the translation for words that are not in a dictionary. Web mining has been used for translating the query terms that are missing from dictionaries. MLU extraction is one of the key parts in search engine based translation. The MLU extraction result will finally affect the transition quality. Most statistical approaches to MLU extraction rely on large statistical information from huge corpora. In the case of search engine based translation, those approaches do not perform well because the size of corpus returned from a search engine is usually small. In this paper, we present a new string measurement and new Chinese MLU extraction process that works well on small corpora.

关键词： Extraction

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Interpretations of association rules by granular computing

Interpretations of association rules by granular computing

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作者： Li, Yuefeng Zhong, Ning School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD 4001 Australia Department of Systems and Information Engineering Maebashi Institute of Technology Maebashi 371-0816 Japan

ISBN: (纸本)0769519784

This paper presents interpretations for association rules. It first introduces Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). It then uses extended random sets to present a new algorithm of finding interesting rules. It proves that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. They also provide two measures for dealing with uncertainties in association rules. © 2003 IEEE.

关键词： Association rules

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Utilizing search intent in topic ontology-based user profile for Web mining 06

Utilizing search intent in topic ontology-based user profile...

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2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI'06

作者： Zhou, Xujuan Wu, Sheng-Tang Li, Yuefeng Xu, Yue Lau, Raymond Y.K. Bruza, Peter D. School of Software Engineering and Data Communications Queensland University of Technology QLD 4000 Australia Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)0769527477

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontologybased user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the user's search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval. © 2006 IEEE.

关键词： Web services

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Deploying Approaches for Pattern Refinement in Text Mining

Deploying Approaches for Pattern Refinement in Text Mining

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IEEE International Conference on data Mining (ICDM)

作者： Sheng-tang Wu Yuefeng Li Yue Xu School of Software Engineering and Data Communications Queensland University of Technology QLD Australia

Text mining is the technique that helps users find useful information from a large amount of digital text documents on the Web or databases. Instead of the keyword-based approach which is typically used in this field, the pattern-based model containing frequent sequential patterns is employed to perform the same concept of tasks. However, how to effectively use these discovered patterns is still a big challenge. In this study, we propose two approaches based on the use of pattern deploying strategies. The performance of the pattern deploying algorithms for text mining is investigated on the Reuters dataset RCVI and the results show that the effectiveness is improved by using our proposed pattern refinement approaches.

关键词： Text mining Information retrieval data mining Indexing Frequency databases Text categorization software engineering data communication Australia

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Web based collection selection using singular value decomposition 03

Web based collection selection using singular value decompos...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： J. King Yuefeng Li School of Software Engineering and Data Communications Queensland University of Technology QLD Australia

ISBN: (纸本)9780769519326

As the number of electronic data collections available on the Internet increases, so does the difficulty of finding the right collection for a given query. Often the first time user is overwhelmed by the array of options available, and wastes time hunting through pages of collection names, followed by time reading results pages after doing an ad-hoc search. Automatic collection selection methods try to solve this problem by suggesting the best subset of collections to search based on a query. This is of importance to fields containing large number of electronic collections, which undergo frequent change, and collections that cannot be fully indexed using traditional methods such as spiders. We present a solution to this problem of selecting the best collections and reducing the number of collections needing to be searched. Preliminary tests of the system, conducted on Web search engines, suggest that this solves much of the Web based Collection selection problem.

关键词： Singular value decomposition Internet Search engines Indexing Bandwidth Frequency estimation Feedback software engineering data communication Australia

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An Improvement to Collaborative Filtering for Recommender Systems

An Improvement to Collaborative Filtering for Recommender Sy...

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International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Li-Tung Weng Yue Xu Yuefeng Li R. Nayak School of Software Engineering and Data Communications Queensland University of Technology QLD Australia

Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse user frequency is one of most well known approaches to improve the accuracy of the standard collaborative filtering recommender. In this paper, we propose a statistical attribute distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results we suggest the statistical attribute distance outperforms inverse user frequency in recommendation accuracy and scalability

关键词： Collaboration Filtering Recommender systems Frequency Scalability databases Statistics Measurement standards software engineering data communication

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Multi-Tier Granule Mining for Representations of Multidimensional Association Rules

Multi-Tier Granule Mining for Representations of Multidimens...

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IEEE International Conference on data Mining (ICDM)

作者： Yuefeng Li Wanzhong Yang Yue Xu School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD Australia

It is a big challenge to promise the quality of multidimensional association mining. The essential issue is how to represent meaningful multidimensional association rules efficiently. Currently we have not found satisfactory approaches for solving this challenge because of the complicated correlation between attributes. Multi-tier granule mining is an initiative for solving this challenging issue. It divides attributes into some tiers and then compresses the large multidimensional database into granules at each tier. It also builds association mappings to illustrate the correlation between tiers. In this way, the meaningful association rules can be justified according to these association mappings.

关键词： data mining Multidimensional systems Association rules Itemsets Transaction databases software engineering data communication Australia Costs Frequency

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Gate-level Morphogenetic Evolvable Hardware for Scalability and Adaptation on FPGAs

Gate-level Morphogenetic Evolvable Hardware for Scalability ...

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NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

作者： J. Lee J. Sitte School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD Australia

Traditional approaches to evolvable hardware (EHW), in which the field programmable gate array (FPGA) configuration is directly encoded, have not scaled well with increasing circuit and FPGA complexity. To overcome this there have been moves towards encoding a growth process, known as morphogenesis, however existing approaches have tended to abstract away the underlying FPGA architecture. Although currently commercially available FPGAs are not the most evolution-friendly platforms, having complex architectures and issues with potentially damaging configurations, evolving circuits on commercially available devices without requiring a move to high-level building blocks is a necessary prerequisite for the adoption of EHW to solving real problems in electronic design, repair and adaptation. In this paper we present a morphogenetic EHW model where growth is directed by the gate-level state of the FPGA. We demonstrate that this approach consistently outperforms a traditional EHW approach using a direct encoding, in the number of generations required to find an optimal solution, and in its ability to scale to increases in circuit size and complexity. Issues in EHW problem solvability are also identified, and preliminary work is presented showing that a morphogenetic approach to EHW may be well suited to correcting damaged circuits

关键词： Hardware Scalability Field programmable gate arrays Circuits Biological cells Logic Proteins Encoding Polymers Routing

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Hybrid clustering with application to Web mining

Hybrid clustering with application to Web mining

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Proceedings of the International Conference on Active Media Technology, AMT

作者： Y. Xu School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD Australia

Clustering algorithms fall into two categories: hierarchical clustering and partitional clustering. For hierarchical algorithms, they are static in the sense that they never undo what was done previously, which means that, objects which are committed to a cluster in the early stages, cannot move to another cluster. Partitional clustering does not suffer from this problem, but requires a pre-specified number for the output clusters. This paper presents a hybrid clustering method that combines the advantages of hierarchical clustering and partitional clustering techniques. The proposed hybrid algorithm does not require a number for the output clusters prior to the clustering and the clusters can be rearranged according to a quality measurement. In the present paper, we apply this method to Web page clustering and provide necessary experimental results.

关键词： Web mining Clustering algorithms Partitioning algorithms Merging Web pages Application software software engineering data communication software algorithms Clustering methods

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