Competitive Video Retrieval with vitrivr

This paper presents the competitive video retrieval capabilities of vitrivr.  The vitrivr stack is the continuation of the IMOTION system which participated to the Video Browser Showdown competitions since 2015. The primary focus of vitrivr and its participation in this competition is to simplify and generalize the system's individual components, making them easier to deploy and use. The entire vitrivr stack is made available as open source software

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Neural content-aware collaborative filtering for cold-start music recommendation

International audienceState-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart. We propose a generative model which leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. The deep content feature extractor can either directly predict the item embedding, or serve as a regularization prior, yielding two variants (strict and relaxed) of our model. Experimental results show that the proposed method reaches state-of-the-art results for a cold-start music recommendation task. We notably observe that exploiting deep neural networks for learning refined user/item interactions outperforms approaches using a more simple interaction model in a content-aware framework

A personality-based behavioural model: Susceptibility to phishing on social networking sites

The worldwide popularity of social networking sites (SNSs) and the technical features they offer users have created many opportunities for malicious individuals to exploit the behavioral tendencies of their users via social engineering tactics. The self-representation and social interactions on SNSs encourage users to reveal their personalities in a way which characterises their behaviour. Frequent engagement on SNSs may also reinforce the performance of certain activities, such as sharing and clicking on links, at a “habitual” level on these sites. Subsequently, this may also influence users to overlook phishing posts and messages on SNSs and thus not apply sufficient cognitive effort in their decision-making. As users do not expect phishing threats on these sites, they may become accustomed to behaving in this manner which may consequently put them at risk of such attacks. Using an online survey, primary data was collected from 215 final-year undergraduate students. Employing structural equation modelling techniques, the associations between the Big Five personality traits, habits and information processing were examined with the aim to identify users susceptible to phishing on SNSs. Moreover, other behavioural factors such as social norms, computer self-efficacy and perceived risk were examined in terms of their influence on phishing susceptibility. The results of the analysis revealed the following key findings: 1) users with the personality traits of extraversion, agreeableness and neuroticism are more likely to perform habitual behaviour, while conscientious users are least likely; 2) users who perform certain behaviours out of habit are directly susceptible to phishing attacks; 3) users who behave out of habit are likely to apply a heuristic mode of processing and are therefore more susceptible to phishing attacks on SNSs than those who apply systematic processing; 4) users with higher computer self-efficacy are less susceptible to phishing; and 5) users who are influenced by social norms are at greater risk of phishing. This study makes a contribution to scholarship and to practice, as it is the first empirical study to investigate, in one comprehensive model, the relationship between personality traits, habit and their effect on information processing which may influence susceptibility to phishing on SNSs. The findings of this study may assist organisations in the customisation of an individual anti-phishing training programme to target specific dispositional factors in vulnerable users. By using a similar instrument to the one used in this study, pre-assessments could determine and classify certain risk profiles that make users vulnerable to phishing attacks.Thesis (PhD) -- Faculty of Commerce, Information Systems, 202

The 21st International Conference on MultiMedia Modeling

© 2015 IEEE. This report on The 21st International Conference on MultiMedia Modeling provides an overview of the best papers and keynote presentations. It also reviews the special sessions on Personal (Big) Data Modeling for Information Access and Retrieval; Social Geo-Media Analytics and Retrieval; and Image or Video Processing, Semantic Analysis, and Understanding

MultiMedia Modeling: 21st International Conference, MMM 2015 Sydney, NSW, Australia, January 5-7, 2015 Proceedings, Part II

These proceedings contain the papers presented at MMM 2015, the 21st International Conference on MultiMedia Modeling. The conference was organised by University of Technology, Sydney, and was held during January 5-7, 2015, at the Aerial UTS Function Centre, Sydney Australia. The accepted contributions represent the state of the art in multimedia modeling research and cover a diverse range of topics including: image and video processing, multimedia encoding and streaming, applications of multimedia modeling, and 3D and augmented reality