By Elizabeth Chang, Tharam Dillon (auth.), Harris Papadopoulos, Andreas S. Andreou, Lazaros Iliadis, Ilias Maglogiannis (eds.)
This publication constitutes the refereed lawsuits of the ninth IFIP WG 12.5 foreign convention on man made Intelligence functions and techniques, AIAI 2013, held in Paphos, Cyprus, in September/October 2013. The 26 revised complete papers offered including a keynote speech on the major occasion and forty four papers of eight collocated workshops have been conscientiously reviewed and chosen for inclusion within the quantity. The papers of the most occasion are equipped in topical sections on info mining, clinical informatics and biomedical engineering, challenge fixing and scheduling, modeling and selection aid platforms, robotics, and clever sign and picture processing.
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Additional info for Artificial Intelligence Applications and Innovations: 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013, Proceedings
Al. . 3 Brief Overview of Our Previous Work on Trust, Reputation and Risk In this section we give a brief description and definitions of the key ideas of Trust, Reputation and Risk defined in our previous work [15,17]. CPS Node CPS Node Actuators WoT Overlay WoT Overlay WoT Kernel WoT Kernel WoT Device WoT Device CPS Node WoT Overlay WoT Kernel WoT Device Physical Environment CPS Fabric CPS Event WoT API CPS Mashups WoT Context WoT Overlay Sensors CPS Node WoT Overlay CPS Event CPS Desktops WoT Kernel CPS Users WoT Device CPS Developers Fig.
3 Data Mining u to populate the ontology backbone and are stored iin a All player annotations are used database for further processsing, that includes statistical as well as data mining teechniques. g. ), and its statisttical significance, as well as high her-level association information governing them. Data mining and machin ne learning techniques will also be employed. The conceepts and attributes described earrlier will enable the transformation of the annotation seet of a given advertisement into a learning feature-value vector.
In all datasets the ratings are integer values in the range [1 5] corresponding to the answers ‘strongly disagree’, ‘disagree’, ‘neither agree nor disagree’, ‘agree’, ‘strongly agree’ respectively. We have set as missing values the instances where voters answered ‘I do not have opinion’ or not answered, the particular question at all. The main characteristics of these datasets are shown in Table 1. com/datasets 6 Experimental Results and Discussion Experiments were designed to investigate the performance of the recommendation methods, which have been reported previously, based on the accuracy of prediction of missing values.