In this paper we introduce the Smart Tempo-Spatial Hotspots Finder (Smart-TSH-Finder) for smart data crawling in the Second Life (SL) virtual world. Classical methods of crawling data from SL lead to irrelevant data content because of the dynamic nature of avatars and objects in SL. In order to build artificially intelligent expert avatar agents that are able to provide intelligent services for other typical avatars in virtual world we attempt to enhance the quality of extracted data from SL. Based on experimental observation, avatars tend to gather in some places for different amounts of time, which forms temporal and spatial hotspots. Utilizing the Tempo-Spatial characteristics of the avatars behavior in virtual worlds could improve the quality of the extracted data. Smart-TSHFinder implements a Tempo-Spatial Hotspots finding mechanism to crawl dynamic contents such as chat conversations from Second Life. The system introduces two mechanisms: the Tempo-Spatial Hotspots Detection, and the Tempo-Spatial Hotspots Prediction. Our smart chat conversations that have been crawled showed good enhancement in content quality of the crawled chat conversation which will enrich the future textual analysis work. Additionally, we found that extracting avatars interactions and behavior in the Tempo-Spatial Hotspots in addition to chat conversations can help in generation a more coherent social network model for SL.
Learning objects, which are the base component of m-learning system, are usually target to modifications in contexts and formats. The device- dependent applications of hand-held devices have proven to be ineffective for creating m-learning courseware. Learning Objects Metadata (LOM) is the most popular standard specification for learning objects but lacks the ability to facilitate platforms descriptions.
This paper outlines various aspects of design and implementation of Web Services Oriented Rendering Architecture (WSORA) which combines LOM Editor with any available published web services. This arrangement is devised in order to make a device-independent m-learning gateway between different mobile devices, such as cell phones, PDAâ??s, palmtops, and laptops and the vast learning objects available on the World Wide Web. The key technologies behind WSORA are extending the IEEE LOM base scheme structure, LOM Editor, device-independent LO generator, and web services. The major advantage of WSORA is thus achieved to give mobile devices of different types clean and quick access to learning objects customarily designed for desktop browsers.
One of the central challenging and most difficult problems in Natural Language Processing is the capability to identify what a word means with respect to a context in which it comes into view. This problem is called Word Sense Disambiguation (WSD). It is ubiquitous across all languages but it has greater challenges in Semitic languages like Arabic language. In this paper we present what researches have been done to solve the problem of Arabic word sense disambiguation.
Studying the text messages of a user such as his posts in Facebook or his tweets in Twitter can help in detecting his topics of interests. User in Social Network Systems (SNS) posts text messages about a wide diverse of topics. Posts usually written in a non-standard language, which make it not applicable to the standard Natural Language Processing (NLP) techniques used to catch the relations between words in text. In many cases there are semantic relations between the contained entities of posts that can infer the interest of the user. Bag-Of-Words (BOW) based text classification techniques classify this kind of messages to a wide diverse of topics, but they fail in catching the implicit semantic relation between the contained entities. In this paper we propose a technique to discover the implicit semantic relations between entities in text messages, which can infer the interests of a user. The proposed technique based on a semantically enriched graph representation of entities contained in text messages generated by a user, a new algorithm (Root-Path-Degree) is invented and used to find the most representative sub-graph that reflects the semantic implicit interests of the user. An evaluation was done using manually annotated posts of 687 Facebook users. Precision and Recall results showed our technique performs better than the standard BOW technique.
There have been recent, marked increases in the challenges of privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). This calls into question the current model, in which different parties generate, exchange and monitor massive amounts of personal data related to EPPR. Ethereum blockchain has demonstrated that trusted, auditable transactions is visible using a decentralized network of nodes accompanied by a general ledger. Thus, the rapid development of educational and professional data generators such as online universities and distance learning, learners need to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized approach to manage EPPR using Ethereum blockchain technology. The decentralized approach provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers. Utilizing Ethereum blockchain features, can provide solutions with the main concerns of exchanging data between parties such as privacy, accountability and data interoperability. The aim of this approach is to also facilitate educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules (GDPR) in United Arab Emirates (UAE).
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
