Modelling trust in semantic web applications

This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations. The framework is established on a semantic modelling approach putting forward its suitability to resolving adoption barriers while also highlighting the characteristics of trust that can be exploited. Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation

Author Retains Full Rights

Software and systems complexity can have a profound impact on information security. Such complexity is not only imposed by the imperative technical challenges of monitored heterogeneous and dynamic (IP and VLAN assignments) network infrastructures, but also through the advances in exploits and malware distribution mechanisms driven by the underground economics. In addition, operational business constraints (disruptions and consequences, manpower, and end-user satisfaction), increase the complexity of the problem domain... Copyright SANS Institut

Tips for making a movie. A learning object for autonomous learning

The paper accounts for a developmental research in the area of courseware production for personal, and self-directed learning. A complete learning object was developed in the context of a master program in Technology and Digital Art. The aim is to make this course available through an online platform, using existing social networks to add social learning features. It is considered by the authors to be a kind of Personal Learning Environment, with a specific purpose

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

Knowledge Extraction from Open Data Repository

The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need

Serials Spoken Here–Reports of Conferences, Institutes and Seminars

This quarter's column offers a report from the Acquisitions Institute at Timberline Lodge, held May 14–17, 2016, in Timberline Lodge, Oregon, and also provides coverage of multiple sessions from the Kraemer Copyright Conference, held June 6–7, 2016, in Colorado Springs, Colorado. Some reports are collected, as well, from the NASIG Annual Conference, held June 9–12, 2016, in Albuquerque, New Mexico, and the American Library Association (ALA) Annual Conference, held June 23–28, 2016, in Orlando, Florida. Lastly, there is a report from the International Federation of Library Associations and Institutions (IFLA) World Library and Information Congress, held August 13–19, 2016, in Columbus, Ohio. Topics covered include open access, linked data, copyright, text mining, e-resource management, and digitization

Qualitative and Mixed Methods Social Media Research: A Review of the Literature

Social media technologies have attracted substantial attention among many types of users including researchers who have published studies for several years. This article presents an overview of trends in qualitative and mixed methods social media research literature published from 2007 through 2013. A collection of 229 qualitative studies were identified through a systematic literature review process. A subset of 55 of these articles report studies involving a combination of qualitative and quantitative methods. Articles were reviewed, analyzed, and coded through a qualitative content analysis approach. Overall trends are presented with respect to the entire collection of articles followed by an analysis of mixed methods research approaches identified in the subset of 55 studies. The most commonly used research approaches involved collecting data from people through interview, focus group, and survey methodologies. Content analysis was the second most commonly used approach whereby researchers use Facebook posts, Tweets (Twitter posts), YouTube videos, or other social media content as a data source. Many of the studies involving combinations of quantitative and qualitative data followed a design resembling Creswell and Plano Clark’s basic mixed methods typology (e.g., convergent parallel, explanatory sequential, and exploratory sequential)

Research, development and evaluation of a practical model for sentiment analysis

Sentiment Analysis is the task of extracting subjective information from input sources coming from a speaker or writer. Usually it refers to identifying whether a text holds a positive or negative polarity. The main approaches to carry out Sentiment Analysis are lexicon or dictionary-based methods and machine learning schemes. Lexicon-based models make use of a prede ned set of words, where each of the words composing the set has an associated polarity. Document polarity will depend on the feature selection method, and how their scores are combined. Machine-learning approaches usually rely on supervised classifiers. Although classifiers offer adaptability for specific contexts, they need to be trained with huge amounts of labelled data which may not be available, specially for upcoming topics. This project, contrary to most scientific researches over this field, aims to go further in emotion detection and puts its efforts on identifying the actual sentiment of documents, instead of focusing on whether it may have a positive or negative connotation. The set of sentiments used for this approach have been extracted from Plutchik's wheel of emotions, which defines eight basic bipolar sentiments and another eight advanced emotions composed of two basic ones. Moreover, in this project we have created a new scheme for SA combining a lexicon-based model for getting term emotions and a statistical approach to identify the most relevant topics in the document which are the targets of the sentiments. By taking this approach we have tried to overcome the disadvantages of simple Bag-of-words models that do not make any distinctions between parts of speech (POS) and weight all words commonly using the tf-idf scheme which leads to overweight most frequently used words. Furthermore, in order to improve knowledge, this projects presents a heuristic learning method that allows improving initial knowledge by converging to human-like sensitivity. In order to test proposed scheme's performance, an Android application for mobile devices has been developed. This app allows users taking photos and introducing descriptions which are processed and classi ed with emotions. Classi cation that may be corrected by the user so that system performance statistics can be extracted.El Análisis de Sentimientos consiste en extraer información subjetiva de lenguaje escrito u oral. Habitualmente se basa en identificar si un texto es positivo o negativo, es decir, extraer su polaridad. Las principales formas de llevar a cabo el Análisis de Sentimientos son los métodos basados en dictionarios y en aprendizaje automático. Los modelos basados en léxicos hacen uso de un conjunto predefinido de palabras que tienen asociada una polaridad. La polaridad del texto dependerá los elementos analizados y la forma en la que se combinan sus valores. Las aproximaciones basadas en aprendizaje automático, por el contrario, normalmente se apoyan en clasificadores supervisados. A pesar de que los claificadores ofrecen adaptabilidad para contextos muy específicos, necesitan gran cantidad de datos para ser entrenados no siempre disponibles, como por ejemplo en temas muy novedosos. Este proyecto, al contrario que la mayoría de investigaciones en este campo, intenta ir m as allá en la detección de emociones y pretende identificar los sentimientos del texto en vez de centrarse en su polaridad. El conjunto de sentimientos usados para este proyecto esrá basado en la Rueda de las Emociones de Plutchik, que define ocho sentimientos básicos y ocho complejos formados por dos básicos. Además, en este proyecto se ha creado un nuevo modelo de AS combinando léxicos para extraer las emociones de las palabras con otro estadístico que trata de identificar los temas más importantes del texto. De esta forma, se ha intentado superar las desventajas de los modelos Bag-of-words que no diferencian entre clases de palabras y ponderan todas las palabras usando el esquema tf-idf, que conlleva sobreponderar las palabras más usadas. Asimismo, para mejorar el conocimiento del proyecto, se ha implementado un método de aprendizaje heurístico que permite mejorar el conocimiento inicial para converger con la sensibilidad real de los humanos. Para evaluar el rendimiento del modelo propuesto, una aplicación Android para móviles ha sido desarrollada. Esta app permite a los usuarios tomar fotos e introducir descripciones que son procesadas y clasificadas por emociones. Clasificación que puede ser corregida por el usuario permitiendo así extraer estadísticas del rendimiento del sistema.Ingeniería Informátic