1,721,073 research outputs found
Crime art on the stone: Graffiti Vandalism on cultural heritage and the anti-graffiti role in its surfaces protection
No full textApparently perceived like an easy thing commonly used, spray paint is a very complex product composed by substances strongly penetrating particularly into the porous materials. This characteristic is very hazardous for our cultural heritage. The problem concerning the surfaces protection from paints and signs is very hard to solve, both for the difficulty to remove these substances and for the variety of the materials that react in a different manner to the various paints and cleaning treatments because of their different physical-chemical characteristics. With the aim to evaluate the damages originated by the spray paints on the stones and the efficacy of anti-graffiti products, some laboratory tests have been carried out. Two different limestones have been selected like supports: a little porous, polishable wakestone and a very porous bio-calcarenite with very scarce mechanical properties. Both these limestones are used as coverings and structural elements of buildings and monuments around Mediterranean basin. Concerning the spray paint cans, the most popular Montana mtn94 has been used, and two commercial anti-graffiti have been applied as protective products. Using Scanning Electron Microscope, Infrared Spectrometry, Colorimetry, Mercury Intrusion Porosimetry and Contact Angle Analysis the interactions stone-paint, stone-antigraffiti and paint-anti-graffiti have been investigated. In order to evaluate the real efficacy of the anti-graffiti, some cleaning and removal paint tests have been carried out. The research highlights that the anti-graffiti cause variations concerns the colour and/or the wettability in both limestones. Their effects are strictly related to the stonework and their microstructure but also the interaction with the paint is influent too
A Survey of Multimedia Recommender Systems: Challenges and Opportunities
No full textMultimedia information has been extensively growing from a variety of sources such as cameras or video recorders. In order to select the useful multimedia objects, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia objects, it is challenging to provide effective multimedia recommendations. In this paper, we therefore conduct a survey in both the multimedia information system and recommender system communities. We further focus on the works that span the two communities, especially the research on multimedia recommender systems. Based on our review, we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights of how to perform the follow-up research
A Framework for High-Level Event Detection in a Social Network Context Via an Extension of ISEQL
No full textWe develop a framework for the detection of high-level events in a social network context, allowing us to identify abnormal or malicious behavior such as spamming. Additionally, we can classify users by analyzing their typical behavior while logged into a social network site. The processing of (real-time) events in our framework is done via an event detection language called ISEQL, which we adapt and extend to fit the requirements of a social network setting. We evaluate our framework experimentally, showing its effectiveness and efficiency
A Generalized Evaluation Framework for Multimedia Recommender Systems
No full textWith the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems
An event detection framework supported by a smart graphical user interface
No full textWe develop a graphical user interface for defining events in a framework for detecting high-level surveillance events from a video stream, as the language used for the events may be too complex for an ordinary user. The language is based on relational algebra extended by intervals, introducing operators whose temporal constraints are described using the well-known Allen's interval relationships. The user interface captures intervals in a descriptive way, supporting the user in providing the missing parameters in a step-bystep manner. In the background, the system checks the user input for consistency and automatically transforms it into relational algebra expressions
High-Level Automatic Event Detection and User Classification in a Social Network Context
No full textWe present a framework for high-level automatic event detection and user classification in a social network context based on a novel temporal extension of relational algebra, which improves and extends our earlier work in the video surveillance context. By means of intuitive and interactive graphical user interfaces, a user is able to gain insights into the inner workings of the system as well as create new event models and user categories on the fly and track their processing through the system in both offline and online modes. Compared to an earlier version, we extended our relational algebra framework with operators suited for processing data from a social network context. As a proof-of-concept we have predefined events and user categories, such as spamming and fake users, on both a synthetic and a real data set containing data related to the interactions of users with Facebook over a 2-year period
Research Challenges in Multimedia Recommender Systems
No full textNowadays, since multimedia information has been extensively growing from a variety of sources, such photos from social networks, unstructured text from different websites, or raw video feed from digital sensors, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia, it is still challenging to provide effective recommendations, and research so far could only address limited aspects. Therefore, in this paper we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights to explain which aspects are worth further investigation
High-Level Surveillance Event Detection Using an Interval-Based Query Language
No full textWe propose a language based on relational algebra extended by intervals for detecting high-level surveillance events from a video stream. The operators we introduce for describing temporal constraints are based on the well-known Allen's interval relationships. The semantics of our language are clearly defined and we illustrate its usefulness by expressing typical events in it and showing the promising results of an experimental evaluation
Evaluation in Multimedia Recommender Systems: A Practical Guide
No full textWith the widespread availability of media technologies, such as real-time streaming, new IoT devices and smartphones, multimedia data are extensively increased and the big multimedia data are rapidly spreaded over various social networks. Thus, different multimedia recommender systems have been emerging to help users select the useful multimedia objects. However, due to distinct features of multimedia objects, it is difficult to conduct a proper evaluation for the multimedia recommender systems, and the evaluation from the general recommender systems might not be totally adopted to evaluate them. In this paper, we therefore review and analyze the evaluation criteria that are used in the previous multimedia recommender system papers. Based on the review, we propose a set of the practical advices to lead practitioners and researchers to perform evaluations for multimedia recommender systems
Factoring Personalization in Social Media Recommendations
No full textNowadays, since social media sites and online social networks have created big media data, it is thus complex and time-consuming for users to find the preferred social media from a large media catalog. Social media recommender systems are therefore emerged to recommend personalized media objects. However, most media recommender systems only focus on one aspect of social media. It is lacking a big picture of how to build an effective social media recommender system. Therefore, this paper tackles this challenge first for specifying the distinct features of media object that can be used for recommender systems, and then discusses five critical aspects that can affect the design of social media recommender systems. This paper further indicates how to assemble these critical aspects and concludes that when we apply traditional recommender algorithms in the media context, those are the critical aspects to improve and optimize social media recommneder systems
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