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A deterioration level estimation method via neural network maximizing category-based ordinally supervised multi-view canonical correlation is presented in this paper. This paper focuses on real world data such as industrial applications and has two contributions. First, a novel neural network handling multi-modal features transforms original features into features effectively representing deterioration levels in transmission towers, which are one of the infrastructures, with consideration of only correlation maximization. It can be realized by setting projection matrices maximizing correlations between multiple features into weights of hidden layers. That is, since the proposed network has only a few hidden layers, it can be trained from a small amount of training data. Second, since there exist diverse characteristics and an ordinal scale in deterioration levels, the proposed method newly derives category-based ordinally supervised multi-view canonical correlation analysis (Co-sMVCCA). Co-sMVCCA enables estimation of effective projection considering both within-class divergence and the ordinal scale between classes. Experimental results showed that the proposed method realizes accurate deterioration level estimation.

For realizing quick and accurate access to desired information and ef- fective advertisements or election campaigns, personalized tweet recommendation is highly demanded. Since multimedia contents including tweets are tools for users to convey their sentiment, users’ interest in tweets is strongly influenced by sen- timent factors. Therefore, successful personalized tweet recommendation can be realized if sentiment in tweets can be estimated. However, sentiment factors were not taken into account in previous works and the performance of previous methods may be limited. To overcome the limitation, a method for sentiment-aware per- sonalized tweet recommendation through multimodal Field-aware Factorization Machines (FFM) is newly proposed in this paper. Successful personalized tweet recommendation becomes feasible through the following three contributions: (i) sentiment factors are newly introduced into personalized tweet recommendation, (ii) users’ interest is modeled by deriving multimodal FFM that enables collabora- tive use of multiple factors in a tweet, i.e., publisher, topic and sentiment factors, and (iii) the effectiveness of using sentiment factors as well as publisher and topic factors is clarified from results of experiments using real-world datasets related to worldwide hot topics, “#trump”, “#hillaryclinton” and “#ladygaga”. In addition to showing the effectiveness of the proposed method, the applicability of the pro- posed method to other tasks such as advertisement and social analysis is discussed as a conclusion and future work of this paper.

A novel scheme for retrieving users’ desired contents, i.e., contents with topics in which users are interested, from multiple social media platforms is pre- sented in this paper. In existing retrieval schemes, users first select a particular platform and then input a query into the search engine. If users do not specify suitable platforms for their information needs and do not input suitable queries corresponding to the desired contents, it becomes difficult for users to retrieve the desired contents. The proposed scheme extracts the hierarchical structure of content groups (sets of contents with similar topics) from different social media platforms, and it thus becomes feasible to retrieve desired contents even if users do not specify suitable platforms and do not input suitable queries. This paper has two contributions: (1) A new feature extraction method, Locality Preserving Canoni- cal Correlation Analysis with multiple social metadata (LPCCA-MSM) that can detect content groups without the boundaries of different social media platforms is presented in this paper. LPCCA-MSM uses multiple social metadata as auxiliary information unlike conventional methods that only use content-based information such as textual or visual features. (2) The proposed novel retrieval scheme can re- alize hierarchical content structuralization from different social media platforms. The extracted hierarchical structure shows various abstraction levels of content groups and their hierarchical relationships, which can help users select topics re- lated to the input query. To the best of our knowledge, an intensive study on such an application has not been conducted; therefore, this paper has strong nov- elty. To verify the effectiveness of the above contributions, extensive experiments for real-world datasets containing YouTube videos and Wikipedia articles were conducted.

Indirect augmented reality (IAR) employs a unique approach to achieve high-quality synthesis of the real world and the virtual world, unlike traditional augmented reality (AR), which superimposes virtual objects in real time. IAR uses pre-captured omnidirectional images and offline superimposition of virtual objects for achieving jitter- and drift-free geometric registration as well as high-quality photometric registration. However, one drawback of IAR is the inconsistency between the real world and the pre-captured image. In this paper, we present a new classification of IAR inconsistencies and analyze the effect of these inconsistencies on the IAR experience. Accordingly, we propose a novel IAR system that reflects real-world illumination changes by selecting an appropriate image from among multiple pre-captured images obtained under various illumination conditions. The results of experiments conducted at an actual historical site show that the consideration of real-world illumination changes improves the realism of the IAR experience.

Audio fingerprinting allows us to label an unidentified music fragment within a previously generated database. The use of spectral landmarks aims to obtain a robustness that lets a certain level of noise be present in the audio query. This group of audio identification algorithms holds several configuration parameters whose values are usually chosen based upon the researcher’s knowledge, previous published experimentation or just trial and error methods. In this paper we describe the whole optimisation process of a Landmark-based Music Recognition System using genetic algorithms. We define the actual structure of the algorithm as a chromosome by transforming its high relevant parameters into various genes and building up an appropriate fitness evaluation method. The optimised output parameters are used to set up a complete system that is compared with a non-optimised one by designing an unbiased evaluation model., This work is supported by the research project TIN2014-57251-P.

End-to-end optimised Quality of Service (QoS) and its specific declination for multimedia applications with the end-user Perceived Quality of Service (PQoS) is nowadays a trendy topic in the literature. Many different techniques and approaches have been proposed, which are in general focusing on specific weak technical aspects of the transmission chain in the considered scenario. The end-to-end optimisation of a complete system system is however more complex and its practical realisation remains to be achieved, especially with the added constraint to be transparently integrated in existing legacy systems so as to not perturbate their current modes of operation. In this paper, we propose an architecture set-up within the ICT FP7 OPTIMIX project to study innovative solutions enabling enhanced multimedia streaming in a point to multi-point context for an IP (Internet Protocol) based wireless heterogeneous system, based on cross layer adaptation of the whole transmission chain. The corresponding simulation chain architecture is detailed with the description of the existing and/or future features of each module.