商品の形状デザインと素材選択への感性工学の応用

国立大学法人長岡技術科学大

The Spatial Historian: Creating a Spatially Aware Historical Research System

The intent of this study is to design a geospatial information system capable of facilitating the extraction and analysis of the fragmentary snapshots of history contained in hand-written historical documents. This customized system necessarily bypasses off-the-shelf GIS in order to support these unstructured primary historical research materials and bring long dormant spatial stories previously hidden in archives, libraries, and other documentary storage locations to life. The software platform discussed here integrates the tasks of information extraction, data management, and analysis while simultaneously giving primary emphasis to supporting the spatial and humanistic analysis and interpretation of the data contents. The premise of this research study is that by integrating the collection of data, the extraction of content, and the analysis of information from what has traditionally been post-data collection analysis and research process, more efficient processing and more effective historical research can be achieved

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Video annotation for studying the brain in naturalistic settings

Aivojen tutkiminen luonnollisissa asetelmissa on viimeaikainen suunta aivotutkimuksessa. Perinteisesti aivotutkimuksessa on käytetty hyvin yksinkertaistettuja ja keinotekoisia ärsykkeitä, mutta viime aikoina on alettu tutkia ihmisaivoja yhä luonnollisimmissa asetelmissa. Näissä kokeissa on käytetty elokuvaa luonnollisena ärsykkeenä. Elokuvan monimutkaisuudesta johtuen tarvitaan siitä yksinkertaistettu malli laskennallisen käsittely mahdollistamiseksi. Tämä malli tuotetaan annotoimalla; keräämällä elokuvan keskeisistä ärsykepiirteistä dataa tietorakenteen muodostamiseksi. Tätä dataa verrataan aivojen aikariippuvaiseen aktivaatioon etsittäessä mahdollisia korrelaatioita. Kaikkia elokuvan ominaisuuksia ei pystytä annotoimaan automaattisesti; ihmiselle merkitykselliset ominaisuudet on annotoitava käsin, joka on joissain tapauksissa ongelmallista johtuen elokuvan käyttämistä useista viestintämuodoista. Ymmärrys näistä viestinnän muodoista auttaa analysoimaan ja annotoimaan elokuvia. Elokuvaa Tulitikkutehtaan Tyttö (Aki Kaurismäki, 1990) käytettiin ärsykkeenä aivojen tutkimiseksi luonnollisissa asetelmissa. Kokeista saadun datan analysoinnin helpottamiseksi annotoitiin elokuvan keskeiset visuaaliset ärsykepiirteet. Tässä työssä tutkittiin annotointiin käytettävissä olevia eri lähestymistapoja ja teknologioita. Annotointi auttaa informaation organisoinnissa, mistä syystä annotointia ilmestyy nykyään kaikkialla. Erilaisia annotaatiotyökaluja ja -teknologioita kehitetään jatkuvasti. Lisäksi videoanalyysimenetelmät ovat alkaneet mahdollistaa yhä merkityksellisemmän informaation automaattisen annotoinnin tulevaisuudessa.Studying the brain in naturalistic settings is a recent trend in neuroscience. Traditional brain imaging experiments have relied on using highly simplified and artificial stimuli, but recently efforts have been put into studying the human brain in conditions closer to real-life. The methodology used in these studies involve imitating naturalistic stimuli with a movie. Because of the complexity of the naturalistic stimulus, a simplified model of it is needed to handle it computationally. This model is obtained by making annotations; collecting information of salient features of the movie to form a data structure. This data is compared with the brain activity evolving in time to search for possible correlations. All the features of a movie cannot be reliably annotated automatically: semantic features of a movie require manual annotations, which is in some occasions problematic due to the various cinematic techniques adopted. Understanding these methods helps analyzing and annotating movies. The movie Match Factory Girl (Aki Kaurismäki, 1990) was used as a stimulus in studying the brain in naturalistic settings. To help the analysis of the acquired data the salient visual features of the movie were annotated. In this work existing annotation approaches and available technologies for annotation were reviewed. Annotations help organizing information, therefore they are nowadays found everywhere. Different tools and technologies are being developed constantly. Furthermore, development of automatic video analysis methods are going to provide more meaningful annotations in the future

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Advances in Nonnegative Matrix Decomposition with Application to Cluster Analysis

Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning and data mining. NMF seeks to approximate a nonnegative data matrix by a product of several low-rank factorizing matrices, some of which are constrained to be nonnegative. Such additive nature often results in parts-based representation of the data, which is a desired property especially for cluster analysis.  This thesis presents advances in NMF with application in cluster analysis. It reviews a class of higher-order NMF methods called Quadratic Nonnegative Matrix Factorization (QNMF). QNMF differs from most existing NMF methods in that some of its factorizing matrices occur twice in the approximation. The thesis also reviews a structural matrix decomposition method based on Data-Cluster-Data (DCD) random walk. DCD goes beyond matrix factorization and has a solid probabilistic interpretation by forming the approximation with cluster assigning probabilities only. Besides, the Kullback-Leibler divergence adopted by DCD is advantageous in handling sparse similarities for cluster analysis.  Multiplicative update algorithms have been commonly used for optimizing NMF objectives, since they naturally maintain the nonnegativity constraint of the factorizing matrix and require no user-specified parameters. In this work, an adaptive multiplicative update algorithm is proposed to increase the convergence speed of QNMF objectives.  Initialization conditions play a key role in cluster analysis. In this thesis, a comprehensive initialization strategy is proposed to improve the clustering performance by combining a set of base clustering methods. The proposed method can better accommodate clustering methods that need a careful initialization such as the DCD.  The proposed methods have been tested on various real-world datasets, such as text documents, face images, protein, etc. In particular, the proposed approach has been applied to the cluster analysis of emotional data

Divide-and-conquer based summarization framework for extracting affective video content

YesRecent advances in multimedia technology have led to tremendous increases in the available volume of video data, thereby creating a major requirement for efficient systems to manage such huge data volumes. Video summarization is one of the key techniques for accessing and managing large video libraries. Video summarization can be used to extract the affective contents of a video sequence to generate a concise representation of its content. Human attention models are an efficient means of affective content extraction. Existing visual attention driven summarization frameworks have high computational cost and memory requirements, as well as a lack of efficiency in accurately perceiving human attention. To cope with these issues, we propose a divide-and-conquer based framework for an efficient summarization of big video data. We divide the original video data into shots, where an attention model is computed from each shot in parallel. Viewer's attention is based on multiple sensory perceptions, i.e., aural and visual, as well as the viewer's neuronal signals. The aural attention model is based on the Teager energy, instant amplitude, and instant frequency, whereas the visual attention model employs multi-scale contrast and motion intensity. Moreover, the neuronal attention is computed using the beta-band frequencies of neuronal signals. Next, an aggregated attention curve is generated using an intra- and inter-modality fusion mechanism. Finally, the affective content in each video shot is extracted. The fusion of multimedia and neuronal signals provides a bridge that links the digital representation of multimedia with the viewer’s perceptions. Our experimental results indicate that the proposed shot-detection based divide-and-conquer strategy mitigates the time and computational complexity. Moreover, the proposed attention model provides an accurate reflection of the user preferences and facilitates the extraction of highly affective and personalized summaries.Supported by the ICT R&D program of MSIP/IITP. [2014(R0112-14-1014), The Development of Open Platform for Service of Convergence Contents]