01758nas a2200217   4500008004100000245006600041210006500107260000900172300001400181490000600195520108700201653002401288653001501312653002401327653001401351100001301365700001501378700001301393700001701406856011701423       2010                        eng d00aExtraction of children's friend relations from activity level0 aExtraction of childrens friend relations from activity level  c2010  a270 - 2750 v83 aThis paper proposes the method to extract the friendship relations of children by using motion sensors. Children learn to fit into society through living in a group, and it's greatly influenced by their friend relations. Although preschool teachers need to observe them to assist in the growth of children's social progress and support the development each child's personality, only experienced teachers can watch over children while providing high-quality guidance. To resolve the problem, this paper proposes a mathematical and objective method that assists teachers with observation. It uses numerical data of activity level recorded by pedometers, and the authors make tree diagram called dendrogram based on hierarchical clustering with recorded activity level. Also, the authors calculate children's "breadth" and "depth" of friend relations by using more than one dendrogram. When the authors record children's activity level in a certain kindergarten for two months and evaluated the proposed method, the results usually coincide with remarks of teachers about the children.10aAcceleration sensor10aClustering10aFriendship relation10aPedometer1 aHaga, H.1 aKaneda, S.1 aKono, A.1 aShintani, K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960659287&partnerID=40&md5=0f94aed0a83203638341b37b6cb7cc8b01839nas a2200205   4500008004100000245010600041210006900147260000900216300001400225490000600239520115800245653002001403653001501423653001401438653002101452653001401473653001401487100001501501856011701516       2010                        eng d00aSpam blog filtering with bipartite graph clustering and mutual detection between spam blogs and words0 aSpam blog filtering with bipartite graph clustering and mutual d  c2010  a108 - 1160 v83 aThis paper proposes a mutual detection mechanism between spam blogs and words with bipartite graph clustering for filtering spam blogs from updated blog data. Spam blogs are problematic in extracting useful marketing information from the blogosphere; they often appear to be rich sources of information based on individual opinion and social reputation. One characteristic of spam blogs is copied-and-pasted articles based on normal blogs and news articles. Another is multiple postings of the same article to increase the chances of exposure and income from advertising. Because of these characteristics, spam blogs share common words, and such blogs and words can form large spam bi-clusters. This paper explains how to detect spam blogs and spam words with mutual filtering based on such clusters. It reports that the maximum precision, or F-measure, of the filtering is 95%, based on a preliminary experiment with approximately six months' updated blog data and a more detailed experiment with one day's data. An advantage of this method for spam blog filtering, as compared to a machine learning approach, is also supported by experiments with SVM.10aBipartite graph10aClustering10aFiltering10aMutual detection10aSpam blog10aSpam word1 aIshida, K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960276152&partnerID=40&md5=575c9621399a49b24a9d7c264810d30401871nas a2200217   4500008004100000245009200041210006900133260000900202300001100211490000600222520115200228653002301380653001501403653001501418653003001433653002501463100001901488700001401507700001501521856011701536       2008                        eng d00aAutomating the management of scientific conferences using information mining techniques0 aAutomating the management of scientific conferences using inform  c2008  a3 - 110 v63 aConference management systems, which help organizers in carrying out tasks in the workflow, of scientific events are widely used in the academic world. In this paper we focus on tasks where methods from the domain of information retrieval, information management and information organization can assist the organizer, the program committee members and the participants. We present a method for the creation of an improved review process by better matching the reviewers expertise with the paper topics, which can to increase the quality of the conference. Furthermore the conference participants benefit from better access to the wealth of information accumulated throughout a conference series. The Conference organizers profit from the reduced workload because of the partial automating of tedious tasks, such as the review assignment, the compilation of the conference program and the creation of poster setup plans. We report on case studies from a small-sized (around 200 participants), a medium-sized (around 400 participants) as well as a large (more than 700 participants) conference in the computer science as well as the medical domains.10aAssignment problem10aAutomation10aClustering10aInformation visualization10aSelf-organizing maps1 aPesenhofer, A.1 aMayer, R.1 aRauber, A.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-56849090253&partnerID=40&md5=bcbf62b3dabc739191df5c6d0fcdad5001529nas a2200229   4500008004100000245009900041210006900140260000900209300001000218490000600228520078200234653001301016653001501029653002601044653002101070653001701091653001901108653002701127100001401154700001401168856011701182       2007                        eng d00aSensitivity analysis of server placement on enterprise network topology through soft computing0 aSensitivity analysis of server placement on enterprise network t  c2007  a1 - 70 v53 aWe analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on a probabilistic genetic program for automatically searching the design space for good network topologies. The experimental results for synthesizing and optimizing 3-level enterprise network (65 user nodes) for an edge-server placement has demonstrated the effectiveness of our methodology in finding good solutions with a static workload in less than five minutes.10aAnalysis10aClustering10aEdge-server placement10aNetwork topology10aOptimization10aSoft computing10aWeb structure analysis1 aSafar, M.1 aHabib, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33947320448&partnerID=40&md5=62ced3281abb66c04e4ac8f00c808c7b