A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of Semantic Web resources, that is, relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper, we propose a relation-based page rank algorithm to be used in conjunction with Semantic Web search engines that simply relies on information that could be extracted from user queries and on annotated resources. Relevance is measured as the probability that a retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definitio

LO-MATCH: A semantic platform for matching migrants' competences with labour market's needs

Citizens' mobility and employability are receiving ever more attention by the European legislation. Various instruments have been defined to overcome lexical and semantic differences in the descriptions of qualifications, résumés and job profiles. However, the above differences still represent a significant constraint when abilities of non-European people have to be validated either for education and training or occupation purposes. In this work, a web platform that exploits semantic technologies to address such heterogeneity issues is presented. The platform allows migrants to annotate their knowledge, skills and competences in a shared format based on the European tools. The resulting knowledge base is then used to enable the automatic matchmaking of job seekers' abilities with companies' needs. The platform can additionally be used to support students and workers in the identification of their competence gap with respect to a given education or occupation opportunity, so that to personalize their further trainin

How Blockchain, Virtual Reality and Augmented Reality are converging, and why

Nowadays, breakthrough technologies, such as virtual reality (VR), augmented reality (AR), and Blockchain, have definitively attracted the attention of a huge number of investors worldwide. Although, at first glance, Blockchain (traditionally used for financial services) seems to have little to none to share with VR and AR (originally adopted for entertainment), in the last few years several use cases started to appear showing effective ways to integrate these technologies. In this article, an overview of opportunities investigated by current solutions combining VR, AR, and Blockchain will be discussed, showing how they allowed both companies and academic researchers cope with issues affecting traditional services and products in a rather heterogenous set of application domains. Opportunities that could foster the convergence of these technologies and boost them further are also discussed

Establishing the Technical Activities and Technical Committees of IEEE Consumer Technology Society

The IEEE Consumer Technology Society (CTSoc) is the oldest technical society: it was part of IRE 1920, which merged with AIEE to form IEEE in 1963. As CTSoc claims to be an IEEE Technical Society and is actually one of the 39 IEEE Societies operating under the IEEE Technical Activities Board, it was essential for its recent organizational restructure to include a Technical Activities (TAs) area. This article summarizes the efforts that have been put recently in place over slightly more than two years (since September 2019) by a group of volunteers under the guidance of CTSoc's President and with the help of VP of TAs to establish the TAs area and its 15 technical committees (TCs)

Benchmarking unsupervised near-duplicate image detection

Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while limiting the false positives due to visually similar images. Since the rate of false alarms grows with the dataset size, a very high specificity is thus required, up to 1-10^-9 for realistic use cases; this important requirement, however, is often overlooked in literature. In recent years, descriptors based on deep convolutional neural networks have matched or surpassed traditional feature extraction methods in content-based image retrieval tasks. To the best of our knowledge, ours is the first attempt to establish the performance range of deep learning-based descriptors for unsupervised near-duplicate detection on a range of datasets, encompassing a broad spectrum of near-duplicate definitions. We leverage both established and new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a known ground truth is provided for all possible pairs over a general, large scale image collection. To compare the specificity of different descriptors, we reduce the problem of unsupervised detection to that of binary classification of near-duplicate vs. not-near-duplicate images. The latter can be conveniently characterized using Receiver Operating Curve (ROC). Our findings in general favor the choice of fine-tuning deep convolutional networks, as opposed to using off-the-shelf features, but differences at high specificity settings depend on the dataset and are often small. The best performance was observed on the MFND benchmark, achieving 96% sensitivity at a false positive rate of 1.43x10^-6

An algorithmic and architectural study on Montgomery exponentiation in RNS

The modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS

Is immersive virtual reality the ultimate interface for 3D animators?

Creating computer animations is a labor-intensive task. Existing virtual reality (VR)-based animation solutions offer only heterogeneous subsets of traditional tools' functionalities. We present an add-on for the Blender animation suite that enables users to switch between native and immersive VR-based interfaces and employ the latter to perform a representative set of tasks

Comparing technologies for conveying emotions through realistic avatars in virtual reality-based metaverse experiences

With the development of metaverse(s), industry and academia are searching for the best ways to represent users' avatars in shared Virtual Environments (VEs), where real-time communication between users is required. The expressiveness of avatars is crucial for transmitting emotions that are key for social presence and user experience, and are conveyed via verbal and non-verbal facial and body signals. In this paper, two real-time modalities for conveying expressions in Virtual Reality (VR) via realistic, full-body avatars are compared by means of a user study. The first modality uses dedicated hardware (i.e., eye and facial trackers) to allow a mapping between the user’s facial expressions/eye movements and the avatar model. The second modality relies on an algorithm that, starting from an audio clip, approximates the facial motion by generating plausible lip and eye movements. The participants were requested to observe, for both the modalities, the avatar of an actor performing six scenes involving as many basic emotions. The evaluation considered mainly social presence and emotion conveyance. Results showed a clear superiority of facial tracking when compared to lip sync in conveying sadness and disgust. The same was less evident for happiness and fear. No differences were observed for anger and surprise