NEW shared & interconnected ASL resources: SignStream® 3 Software; DAI 2 for web access to linguistically annotated video corpora; and a sign bank

2017 marked the release of a new version of SignStream® software, designed to facilitate linguistic analysis of ASL video. SignStream® provides an intuitive interface for labeling and time-aligning manual and non-manual components of the signing. Version 3 has many new features. For example, it enables representation of morpho-phonological information, including display of handshapes. An expanding ASL video corpus, annotated through use of SignStream®, is shared publicly on the Web. This corpus (video plus annotations) is Web-accessible—browsable, searchable, and downloadable—thanks to a new, improved version of our Data Access Interface: DAI 2. DAI 2 also offers Web access to a brand new Sign Bank, containing about 10,000 examples of about 3,000 distinct signs, as produced by up to 9 different ASL signers. This Sign Bank is also directly accessible from within SignStream®, thereby boosting the efficiency and consistency of annotation; new items can also be added to the Sign Bank. Soon to be integrated into SignStream® 3 and DAI 2 are visualizations of computer-generated analyses of the video: graphical display of eyebrow height, eye aperture, an

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Classification of Under-Resourced Language Documents Using English Ontology

Automatic documents classification is an important task due to the rapid growth of the number of electronic documents, which aims automatically assign the document to a predefined category based on its contents. The use of automatic document classification has been plays an important role in information extraction, summarization, text retrieval, question answering, e-mail spam detection, web page content filtering, automatic message routing , etc.Most existing methods and techniques in the field of document classification are keyword based, but due to lack of semantic consideration of this technique, it incurs low performance. In contrast, documents also be classified by taking their semantics using ontology as a knowledge base for classification; however, it is very challenging of building ontology with under-resourced language. Hence, this approach is only limited to resourced language (i.e. English) support. As a result, under-resourced language written documents are not benefited such ontology based classification approach. This paper describes the design of automatic document classification of under-resourced language written documents. In this work, we propose an approach that performs classification of under-resourced language written documents on top of English ontology. We used a bilingual dictionary with Part of Speech feature for word-by-word text translation to enable the classification of document without any language barrier. The design has a concept-mapping component, which uses lexical and semantic features to map the translated sense along the ontology concepts. Beside this, the design also has a categorization component, which determines a category of a given document based on weight of mapped concept. To evaluate the performance of the proposed approach 20-test documents for Amharic and Tigrinya and 15-test document for Afaan Oromo in each news category used. In order to observe the effect of incorporated features (i.e. lemma based index term selection, pre-processing strategies during concept mapping, lexical and semantics based concept mapping) five experimental techniques conducted. The experimental result indicated that the proposed approach with incorporation of all features and components achieved an average F-measure of 92.37%, 86.07% and 88.12% for Amharic, Afaan Oromo and Tigrinya documents respectively. Keywords: under-resourced language, Multilingual, Documents or text Classification, knowledge base, Ontology based text categorization, multilingual text classification, Ontology. DOI: 10.7176/CEIS/10-6-02 Publication date:July 31st 201

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201