Collaborative assessment of information provider's reliability and expertise using subjective logic

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user's characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of her peers (on the basis of responses to questions asked by her) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SN. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of her peers using her direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assessment in order to reach a consensus. We emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice. © 2011 ICST

Genesis of Altmetrics or Article-level Metrics for Measuring Efficacy of Scholarly Communications: Current Perspectives

The article-level metrics (ALMs) or altmetrics becomes a new trendsetter in recent times for measuring the impact of scientific publications and their social outreach to intended audiences. The popular social networks such as Facebook, Twitter, and Linkedin and social bookmarks such as Mendeley and CiteULike are nowadays widely used for communicating research to larger transnational audiences. In 2012, the San Francisco Declaration on Research Assessment got signed by the scientific and researchers communities across the world. This declaration has given preference to the ALM or altmetrics over traditional but faulty journal impact factor (JIF)-based assessment of career scientists. JIF does not consider impact or influence beyond citations count as this count reflected only through Thomson Reuters' Web of Science database. Furthermore, JIF provides indicator related to the journal, but not related to a published paper. Thus, altmetrics now becomes an alternative metrics for performance assessment of individual scientists and their contributed scholarly publications. This paper provides a glimpse of genesis of altmetrics in measuring efficacy of scholarly communications and highlights available altmetric tools and social platforms linking altmetric tools, which are widely used in deriving altmetric scores of scholarly publications. The paper thus argues for institutions and policy makers to pay more attention to altmetrics based indicators for evaluation purpose but cautions that proper safeguards and validations are needed before their adoption

Scan to BIM for 3D reconstruction of the papal basilica of saint Francis in Assisi In Italy

The historical building heritage, present in the most of Italian cities centres, is, as part of the construction sector, a working potential, but unfortunately it requires planning of more complex and problematic interventions. However, policies to support on the existing interventions, together with a growing sensitivity for the recovery of assets, determine the need to implement specific studies and to analyse the specific problems of each site. The purpose of this paper is to illustrate the methodology and the results obtained from integrated laser scanning activity in order to have precious architectural information useful not only from the cultural heritage point of view but also to construct more operative and powerful tools, such as BIM (Building Information Modelling) aimed to the management of this cultural heritage. The Papal Basilica and the Sacred Convent of Saint Francis in Assisi in Italy are, in fact, characterized by unique and complex peculiarities, which require a detailed knowledge of the sites themselves to ensure visitor’s security and safety. For such a project, we have to take in account all the people and personnel normally present in the site, visitors with disabilities and finally the needs for cultural heritage preservation and protection. This aim can be reached using integrated systems and new technologies, such as Internet of Everything (IoE), capable of connecting people, things (smart sensors, devices and actuators; mobile terminals; wearable devices; etc.), data/information/knowledge and processes to reach the desired goals. The IoE system must implement and support an Integrated Multidisciplinary Model for Security and Safety Management (IMMSSM) for the specific context, using a multidisciplinary approach

Template Mining for Information Extraction from Digital Documents

published or submitted for publicatio