2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)最新文献

{"title":"Giving Alloy a family","authors":"Renato Neves, A. Madeira, M. Martins, L. Barbosa","doi":"10.1109/IRI.2013.6642513","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642513","url":null,"abstract":"Lightweight formal methods ought to provide to the end user the rigorousness of mathematics, without compromising simplicity and intuitiveness. Alloy is a powerful tool, particularly successful on this mission. Limitations on the verification side, however, are known to prevent its wider use in the development of safety or mission critical applications. A number of researchers proposed ways to connect Alloy to other tools in order to meet such challenges. This paper's proposal, however, is not establishing a link from Alloy to another single tool, but rather to “plunge” it into the HETS network of logics, logic translators and provers. This makes possible for Alloy specifications to “borrow” the power of several, non dedicated proof systems. Semantical foundations for this integration are discussed in detail.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering and summarization topics of subject knowledge through analyzing internal links of Wikipedia","authors":"I-Chin Wu, Chi-Hong Tsai, Yu-Hsuan Lin","doi":"10.1109/IRI.2013.6642458","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642458","url":null,"abstract":"This work introduces a semantics-based navigation application called WNavis. It facilitates informationseeking activities in internal link-based websites within Wikipedia. Our goal is to develop an application that helps users easily find related articles on a given topic and then quickly check the content of articles to explore concepts in Wikipedia. We constructed a subject-based network by analyzing the internal links of Wikipedia and applying a semantic relatedness analysis to measure the strength of the semantic relationships between articles. In order to locate specific information and enable users to quickly explore and read subject-related articles, we propose a social network analysis (SNA)-based topic summarization technique that extracts meaningful sentences from articles. We applied a number of intrinsic evaluation methods to demonstrate the efficacy of the summarization techniques. Our findings have implications for the design of a navigation tool that can help users explore topics and increase their subject knowledge.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116225323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the structure and the computational complexity of the inverse difference and Laplacian Pyramids for still image decomposition","authors":"R. Kountchev, M. Milanova, S. Rubin, R. Kountcheva","doi":"10.1109/IRI.2013.6642524","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642524","url":null,"abstract":"In this work, the structures of the Inverse Difference Pyramid (IDP) and its modification - the Reduced IDP (RIDP), are compared and evaluated with the famous Laplacian Pyramid for multi-level decomposition of digital images. The computational complexity of both decompositions is also evaluated. On the basis of the comparison of the block diagrams, which represent the recursive calculation of the 3-level decompositions and of the evaluation of their structures complexity, are outlined the basic advantages of the RIDP for pipeline image processing. The results obtained could be used for design of coders for image compression, aimed at real-time applications.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123311868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRIS — Computational research infrastructure for science","authors":"Eduard Constantin Dragut, Peter Baker, Jia Xu, M. Sarfraz, E. Bertino, Amgad Madkour, Raghu Agarwal, Ahmed R. Mahmood, Sangchun Han","doi":"10.1109/IRI.2013.6642486","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642486","url":null,"abstract":"The challenges facing the scientific community are common and real: conduct relevant and verifiable research in a rapidly changing collaborative landscape with an ever increasing scale of data. It has come to a point where research activities cannot scale at the rate required without improved cyberinfrastructure (CI). In this paper we describe CRIS (The Computational Research Infrastructure for Science), with its primary tenets to provide an easy to use, scalable, and collaborative scientific data management and workflow cyberinfrastructure for scientists lacking extensive computational expertise. Some of the key features of CRIS are: 1) semantic definition of scientific data using domain vocabularies; 2) embedded provenance for all levels of research activity (data, workflows, tools etc.); 3) easy integration of existing heterogeneous data and computational tools on local or remote computers; 4) automatic data quality monitoring for syntactic and domain standards; and 5) shareable yet secure access to research data, computational tools and equipment. CRIS currently has a community of users in Agronomy, Biochemistry, Bioinformatics and Healthcare Engineering at Purdue University (cris.cyber.purdue.edu).","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding environmental influences on performing password-based mobile authentication","authors":"Sergey Maydebura, D. Jeong, Byunggu Yu","doi":"10.1109/IRI.2013.6642543","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642543","url":null,"abstract":"In a mobile environment, text-based passwords are still the most common mechanism for user authentication. Although various studies have been conducted to investigate what password composition policies are better oriented for mobile users, a limited study has been performed to understand the impact of password composition policies and environmental settings to mobile users' password typing abilities. In this paper, we present a study that investigates password strength, user behavior, and user sentiment across two password composition policies under two environmental conditions such as stationary (sedentary position) and on-the-go (while walking). From the study, we correlate our results with user behaviors under different environmental conditions to provide suggestions for password-composition policies for mobile-based authentication.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124946381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining software repositories to acquire software risk knowledge","authors":"Ching-Pao Chang","doi":"10.1109/IRI.2013.6642510","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642510","url":null,"abstract":"Knowing and managing the software risks are important for software project management. The information collected from past projects can be used to obtain the knowledge of the software risk. The challenge to acquire the knowledge of software risk is that the software development environment is complex and contains large amount factors that may affect the software projects. This study proposes an approach that applies data mining techniques on the data collected from historic software projects to acquire software risk knowledge. The obtained software risk knowledge can be used to facilitate software project management. The advantage of the proposed approach is that the software risk knowledge can be acquired automatically according to the selected attributes. The proposed approach is applied on a business project to demonstrate how the software risk knowledge can be acquired.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature list aggregation approaches for ensemble gene selection on patient response datasets","authors":"T. Khoshgoftaar, Randall Wald, D. Dittman, Amri Napolitano","doi":"10.1109/IRI.2013.6642488","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642488","url":null,"abstract":"Many cancer treatments destroy healthy cells along with cancerous ones, and can leave patients fatigued and with a compromised immune system. This makes it especially important to determine whether or not a given cancer treatment will work for the patient or will just cause further harm. Recently there has been work on using gene expression profiles (DNA microarrays) to predict how a patient will respond to a cancer treatment. However, these profiles carry the problem of high dimensionality (a very large number of features (genes) per instance), thus necessitating dimension-reducing techniques such as feature (gene) selection (data preprocessing techniques from the domain of data mining to find an ideal feature set). A particularly promising subset of feature selection techniques are ensemble feature selection techniques, which perform multiple instances of feature selection and aggregate the results into a single decision. Traditionally, this is accomplished by ranking the features in each list by a metric and aggregating the ranks of each feature into a single final decision for the feature. Many forms of aggregation have been considered, both in terms of how to generate the distinct lists and how to combine the ranks from each list. However, all of these works have assumed ranks must be created perlist and then aggregated in a separate step - rather than aggregating the scores of each list directly and performing ranking only on the final list. This work compares two feature list aggregation approaches (rank-based aggregation and score-based aggregation) using the mean aggregation technique in terms of classification. We use fifteen patient response datasets along with three feature selection techniques as the basis for the ensemble feature selection, and we employ four feature subset sizes and two classifiers. Our results show that in general, the rank-based aggregation approach outperforms the score-based aggregation approach for a majority of scenarios for both classifiers. However, this is not always the case and careful consideration is required before making a decision between the two.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of stability analysis of feature subset selection techniques","authors":"T. Khoshgoftaar, Alireza Fazelpour, Huanjing Wang, Randall Wald","doi":"10.1109/IRI.2013.6642502","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642502","url":null,"abstract":"With the proliferation of high-dimensional datasets across many application domains in recent years, feature selection has become an important data mining task due to its capability to improve both performance and computational efficiencies. The chosen feature subset is important not only due to its ability to improve classification performance, but also because in some domains, knowing the most important features is an end unto itself. In this latter case, one important property of a feature selection method is stability, which refers to insensitivity (robustness) of the selected features to small changes in the training dataset. In this survey paper, we discuss the problem of stability, its importance, and various stability measures used to evaluate feature subsets. We place special focus on the problem of stability as it applies to subset evaluation approaches (whether they are selected through filter-based subset techniques or wrapper-based subset selection techniques) as opposed to feature ranker stability, as subset evaluation stability leads to challenges which have been the subject of less research. We also discuss one domain of particular importance where subset evaluation (and the stability thereof) shows particular importance, but which has previously had relatively little attention for subset-based feature selection: Big Data which originates from bioinformatics.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning-based algorithm for deflection routing in optical burst-switched networks","authors":"Soroush Haeri, Wilson Wang-Kit Thong, Guanrong Chen, L. Trajković","doi":"10.1109/IRI.2013.6642508","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642508","url":null,"abstract":"In this paper, we propose a Q-learning based deflection routing algorithm that may be employed to resolve contention in optical burst-switched networks. The main goal of deflection routing is to successfully deflect a burst based only on a limited knowledge that network nodes possess about their environment. Q-learning, one of the reinforcement learning algorithms, has been proposed in the past to help generate deflection decisions. The complexity of existing reinforcement learning-based deflection routing algorithms depends on the number of nodes in the network. The proposed algorithm scales well for larger networks because its complexity depends on the node degree rather than the network size. The algorithm is implemented using the ns-3 network simulator. Simulation results show that it has comparable performance to an existing reinforcement learning deflection routing scheme while having lower memory requirements.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131778438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographic clustering of research universities in specialized fields using Microsoft Academic Search","authors":"D. Kaczynski, Gongzhu Hu","doi":"10.1109/IRI.2013.6642469","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642469","url":null,"abstract":"Search is no doubt the most common activities on the Web. One of the most frequent search activities people in the educational and academic communities do is to search for research topics and papers and their author/affiliation information. For example, undergraduate students searching for graduate schools and master-level students searching for institutions for doctoral studies may go online to find universities that are conducting research in a highly specific field of their interest. A common way of doing this is to search journals and conferences for pertinent articles, and then accumulating the affiliations of the authors. In this paper, we present an interactive web-based academic search interface for discovering geographic clusters of academic institutions given a specific field by the user. The underlying system interacts with Microsoft Academic Search API and Google Places API, as well as Weka for clustering analysis. The system can be easily extended to accommodate other types of queries.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133149929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}