Search

Abstract In the past decade, there has been an increasing need for semantic-aware data search and indexing in textual (structured and NoSQL) databases, as full-text search systems became available to non-experts where users have no knowledge about the data being searched and often formulate query keywords which are different from those used by the authors in indexing relevant documents, thus producing noisy and sometimes irrelevant results. In this paper, we address the problem of semantic-aware querying and provide a general framework for modeling and processing semantic-based keyword queries in textual databases, i.e., considering the lexical and semantic similarities/disparities when matching user query and data index terms. To do so, we design and construct a semantic-aware inverted index structure called SemIndex , extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex , in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. To investigate the practicality and effectiveness of SemIndex , we discuss its physical design within a standard commercial RDBMS allowing to create, store, and query its graph structure, thus enabling the system to easily scale up and handle large volumes of data. We have conducted a battery of experiments to test the performance of SemIndex , evaluating its construction time, storage size, query processing time, and result quality, in comparison with legacy inverted index. Results highlight both the effectiveness and scalability of our approach. [ABSTRACT FROM AUTHOR]

The pointing task is the process of pointing to an object on a computer monitor using a pointing device, or physically touching an object with the hand or finger. It is an important element for users when manipulating visual computer interfaces such as traditional screens and touch-screens. In this context, Fitts’ Law remains one of the central studies that have mathematically modeled the pointing method, and was found to be a good predictor of the average time needed to perform a pointing task. Yet, in our modern computerized society, accessing visual information becomes a central need for all kinds of users, namely users who are blind or visually impaired. Hence, the goal of our study is to evaluate whether Fitts’ Law can be applied for blind candidates using the vibration modality on a touch-screen. To achieve this, we first review the literature on Fitts’ Law and visual data accessibility solutions for blind users. Then, we introduce an experimental framework titled FittsEVAL studying the ability of blind users to tap specific shapes on a touch-screen, while varying different parameters, namely: target distance, target size, and the angle of attack of the pointing task. Experiments on blindfolded and blind candidates show that Fitts’ Law can be effectively applied for blind users using a vibrating touch-screen under certain parameters (i.e., when varying target distance and size), while it is not verified under others (i.e., when varying the angle of attack). This can be considered as a first step toward a more complete experimental evaluation of vibrating touch-screen accessibility, toward designing more adapted interfaces for the blind. [ABSTRACT FROM AUTHOR]

Accessing visual information becomes a central need for all kinds of tasks and users (from accessing graphics and charts in news articles, to viewing images of items on sale on e-commerce sites), especially for blind users. In this context, digital tools of assistance, using adapted software (screen readers, talking browsers, etc.), hardware (force feedback mouse, piezo-electric pins, etc.), and more recently touch-screen technology (using smart phones or smart tablets) have been increasingly helping blind persons access and manipulate information. While effective with textual information, yet existing solutions remain limited when handling visual information. In this context, the goal of our study is to shed light on how the vibration modality can be perceived by blind users when accessing simple contour-based images and visual graphics on a touch-screen. In this paper, we target the vibration-only modality, compared with audio-kinesthetic or multimodal vibro-audio solutions. Our main motivation is that the potentials and limitations of touch-screen vibration-only feedback need to be fully studied and understood prior to integrating other modalities (such as sound, human speech, or other forms of haptic feedback). This could prove very useful in a range of applications: allowing blind people to access geographic maps, to navigate autonomously inside and outside buildings, as well as to access graphs and mathematical charts (for blind students). To achieve our goal, we develop a dedicated experimental protocol, titled EVIAC, testing a blind user's capacity in learning, distinguishing, identifying, and recognizing basic shapes and geometric objects presented on a vibrating touch-screen. Extensive tests were conducted on blindfolded and blind candidates, using a battery of evaluation metrics including: i) accuracy of shape recognition, ii) testers’ average response time, iii) number and duration of finger strokes, iv) surface area covered by the testers’ finger path trails, as well as iv) finger path correlation with the surface of the target shape. Results show that blind users are generally capable of accessing simple shapes and graphics presented on a vibrating touch-screen. However, results also underline various issues, ranging over: prolonged response time (e.g., blind users require 1 min and 22 s on average to recognize a basic shape), reduced touch-screen surface coverage, and low correlation between the surface of the target shape and the tester's vibration trails. The latter issues need to be further investigated to produce optimal recipes for using touch-screen technology to support image accessibility for blind users. [ABSTRACT FROM AUTHOR]

Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity. [ABSTRACT FROM PUBLISHER]

• Automates health assessment, recommendation, and monitoring services offered by expert • Uses fuzzy logic to simulate the "human common sense" thought process in nutrition assessment • Performs patient health and weight assessment and recommends target BFP and weight goal • Performs caloric intake and physical exercise recommendation based on target BFP and weight goal • Performs progress evaluation of patient health state and adjusts recommendations accordingly Establishing a healthy lifestyle has become a very important aspect in people's lives. The latter requires maintaining a healthy nutrition by considering the type and quantity of consumed foods. It also requires maintaining an active lifestyle including the necessary amount of physical exercise to regulate one's intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts to perform health assessment, whose services are costly, time consuming, and not readily available. While various e-nutrition solutions have been developed, yet most of them perform meal planning without performing health state assessment or evaluation (traditionally provided by human experts). To our knowledge, there is no existing automated solution to perform nutrition health assessment, recommendation, and progress evaluation, which are central pre-requites to the meal planning task. In this study, we introduce a novel framework titled PIN for Personalized Intelligent Nutrition recommendations. PIN relies on the fuzzy logic paradigm to simulate human expert health assessment capabilities, including weight, caloric intake, and exercise recommendations as well as progress evaluation and recommendation adjustments. It includes three essential and complementary modules: i) Weight Assessment and Recommendation (WAR), ii) Caloric Intake and Exercise Recommendation (CIER), and iii) Progress Evaluation and Recommendation Adjustment (PERA). This underlines the first computerized solution for nutrition health assessment. We have conducted a large battery of experiments involving 50 patient profiles and 11 nutrition expert evaluators to test the performance of PIN and evaluate its health assessment quality. Results show that PIN 's assessment and recommendations are on a par with and sometimes surpass those of human nutritionists. [ABSTRACT FROM AUTHOR]

During the past two decades, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this context, knowledge representation and processing have recently shown a significant contribution to the simulation modeling domain, where knowledge graphs have been used in different fields to build knowledge representations for multiple purposes. In this paper, we introduce VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure outdoor weather events in vivid 3D visualizations. We design and implement an integrated knowledge graph (KG) representation for VOWES, by creating two constituent KGs: (i) Weather KG describing weather data and events, and (ii) Simulator KG describing 3D simulation components and properties, and connecting them with the (iii) Semantic Sensor Network (SSN) KG to form an integrated structure serving as the knowledge backbone of the VOWES simulation environment. We make use of the Unity 3D engine to build and design the simulator environment and its virtual sensors, and integrate the Mapbox SDK and the WeatherStack API for realistic real-world weather mapping. We have conducted qualitative evaluations involving 13 expert and 30 non-expert testers, to assess the quality of VOWES' KGs and its simulation environment. Results show that more than 80% of the testers gave a combined quality score ≥ 3 out of 4 on most evaluation criteria. We have also conducted performance evaluations to test VOWES loading, execution, and data search time, among other features. Results show that most operations require almost instantaneous or linear time, where search, refresh, and export operations share almost identical performance levels, with execution time increasing by approximately 179 μs for every added game object. This highlights the simulation tool's time performance in running large simulation projects, and its ability to simulate complex weather environments with large numbers of sensors and weather phenomena. [ABSTRACT FROM AUTHOR]