Using string-matching to analyze hypertext navigation

A method of using string-matching to analyze hypertext navigation was developed, and evaluated using two weeks of website logfile data. The method is divided into phases that use: (i) exact string-matching to calculate subsequences of links that were repeated in different navigation sessions (common trails through the website), and then (ii) inexact matching to find other similar sessions (a community of users with a similar interest). The evaluation showed how subsequences could be used to understand the information pathways users chose to follow within a website, and that exact and inexact matching provided complementary ways of identifying information that may have been of interest to a whole community of users, but which was only found by a minority. This illustrates how string-matching could be used to improve the structure of hypertext collections

マーカーレス腫瘍位置決めを目的とした深層学習に基づく患者固有標的輪郭予測モデルの開発

京都大学新制・課程博士博士(人間健康科学)甲第24542号人健博第113号新制||人健||8(附属図書館)京都大学大学院医学研究科人間健康科学系専攻(主査)教授 中尾 恵, 教授 杉本 直三, 教授 黒田 知宏学位規則第4条第1項該当Doctor of Human Health SciencesKyoto UniversityDFA

WiSeNetor: A Scalable Wireless Sensor Network Simulator

WiSeNetor is a teaching and a research tool that simulates a scalable wireless sensor network on a single computer, based on the “Spamulator” (Aycock, J., Crawford, H., & deGraaf, R., 2008) which simulates the Internet on a single computer. Routing protocols and network discovery algorithms used in mesh networks and cluster tree networks can be demonstrated using this tool. WiSeNetor contains a network creation module, simulated network devices and it simulates routing algorithms. The network creation module spawns a network according to user specified network type, where the type can be a cluster tree or mesh. In this process, neighbor tables are populated and the Spamulator is initiated. The underlying network module of the Spamulator has been reused in WiSeNetor to achieve better scalability. Each simulated network device has an associated server program and a client program that process incoming requests and forward them to appropriate neighboring nodes, respectively. Network devices also log all of the service messages in individual log files that may be used to trace the routing or network discovery process. WiSeNetor has achieved scalability up to 15,000 nodes in the network. Message latency and the average number of hops during simulation testing were comparable to the findings in (Eamsomboon,P., Keeratiwintakorn,P., & Mitrpant, C, 2008) which validates the WiSeNetor

Supporting aspect-based video browsing - analysis of a user study

In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. Our approach has the potential to narrow the "Semantic Gap" issue by allowing users to explore the data collection. First, we describe a clustering technique to identify potential aspects of a search. Then, we use the results to propose suggestions to the user to help them in their search task. Finally, we analyse this approach by exploiting the log files and the feedbacks of a user study