Search results for " Information Retrieval"

Discovering Homophily in Online Social Networks

2018

During the last ten years, Online Social Networks (OSNs) have increased their popularity by becoming part of the real life of users. Despite their tremendous widespread, OSNs have introduced several privacy issues as a consequence of the nature of the information involved in these services. Indeed, the huge amount of private information produced by users of current OSNs expose the users to a number of risks. The analysis of the users’ similarity in OSNs is attracting the attention of researchers because of its implications on privacy and social marketing. In particular, the homophily between users could be used to reveal important characteristics that users would like to keep hidden, hence …

Post-search query modeling in federated web scenario

2014

As opposed to query reformulation oriented towards changes made by a user to specify the information need more precisely, a post-search query modeling is a technique of exploiting syntax variation of gradually extended query which depending on some other factors like e.g. the resource, database or the key word alignment, facilitates the searching process. The study into modeling query submitted to some search engines that utilize different translation semantic paradigms is motivated by a real-world's challenges to retrieve heterogeneous textual documents from the web. For a couple of language pairs, we develop a user-centered framework for imposing the Hidden Web traffic optimization. In li…

A web search methodology for health consumers

2014

Nowadays, many people use the World Wide Web to seek medical and health information but different users, such as providers (e.g., physicians) and consumers (e.g., patients), have different needs and bring different levels of reading ability and prior knowledge. Generic and specific search engines and specialized health sites either do not exploit the whole web or overload users with information. This creates difficulties mainly to consumers who often do not exactly know how to find the desired information. Thus, an information retrieval system for the web that 'drives' the user in finding the relevant information would be very beneficial. This paper describes a web search methodology for he…

Privacy Versus Security in the Internet Era

2015

Abstract There are a lots of conflict going on in the world right now. One of this conflict we are put into discussion here, is the battle for supremacy in the internet, the battle between privacy and security. We are changing fundamentally the way we are doing things till the Internet era arrive. Every data that we use online became a product. There is no more private information, everything became public with or without the population will. There are also a lot of discussion about who have a greater power on the Internet, government, companies, hackers, criminals, terrorists, or others. Unfortunately the neuter population is catch in the middle of this battle.

Embodied Meter Revisited : Entrainment, Musical Content, and Genre in Music-Induced Movement

2022

Previous research has shown that humans tend to embody musical meter at multiple beat levels during spontaneous dance. This work that been based on identifying typical periodic movement patterns, or eigenmovements, and has relied on time-domain analyses. The current study: 1) presents a novel method of using time-frequency analysis in conjunction with group-level tensor decomposition; 2) compares its results to time-domain analysis, and 3) investigates how the amplitude of eigenmovements depends on musical content and genre. Data comprised three-dimensional motion capture of 72 participants’ spontaneous dance movements to 16 stimuli including eight different genres. Each trial was subjected…

Testing a spectral-based feature set for audio genre classification

2011

Automatic musical genre classification is an important information retrieval task since it can be applied for practical purposes such as the organization of data collections in the digital music industry. However, this task remains an open question because the current state of the art shows far from satisfactory outcomes in terms of classification performance. Moreover, the most common algorithms that are used for this task are not designed for modelling music perception. This study suggests a framework for testing different musical features for use in music genre classification and evaluates the performance of this task based on two musical descriptors. The focus of this study is on automa…

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

2019

AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method t…

Content Aware Playlist Generation with Multi-Dimensional Similarity Measure

2016

Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music

2011

Classification of musical audio signals according to expressed mood or emotion has evident applications to content-based music retrieval in large databases. Wrapper selection is a dimension reduction method that has been proposed for improving classification performance. However, the technique is prone to lead to overfitting of the training data, which decreases the generalizability of the obtained results. We claim that previous attempts to apply wrapper selection in the field of music information retrieval (MIR) have led to disputable conclusions about the used methods due to inadequate analysis frameworks, indicative of overfitting, and biased results. This paper presents a framework bas…

EntityBot: Actionable Entity Recommendations for Everyday Digital Task

2022

Our everyday digital tasks require access to information from a wide range of applications and systems. Although traditional search systems can help find information, they usually operate within one application (e.g., email client or web browser) and require the user's cognitive effort and attention to formulate proper search queries. In this paper, we demonstrate EntityBot, a system that proactively provides useful and supporting entities across application boundaries without requiring explicit query formulation. Our methodology is to exploit the context from screen frames captured every 2 seconds to recommend relevant entities for the current task. Recommendations are not restricted to on…