GOHSE: Ontology driven linking of biology resources

We describe the GOHSE system, an application to support browsing of biology resources. The Conceptual Open Hypermedia Service (COHSE) system enhances web resources through the dynamic addition of hypertext links. These links are derived through the use of an ontology and associated lexicon along with a mapping from concepts to possible link targets. GOHSE applies COHSE to Bioinformatics, using the Gene Ontology (GO) as an ontology and associated keyword mappings and GO associations as link targets. The resulting demonstrator provides both glossary functionality and the possibility of building knowledge based hypertext structures linking bioinformatics resources.     

An extension to the ADL SCORM standard to support adaptivity: The t-learning case-study

Current e-learning standards have been designed to provide reusability and interoperability. Besides these features, content personalisation is also necessary, although current standards do not fully support it. In this paper, we study the adaptation possibilities of the SCORM standard and present an extension to permit adaptivity according to user's characteristics. It comprises a syntax for adaptivity rules based on a set of adaptation parameters. The actual values of these adaptation parameters are deduced from the user profile, using inference rules. As a result, adaptive courses are obtained, created with the aim of being personalised before shown to the student.     

LBSN协作式个性化链接预测算法

在基于位置的社交网络中用户链接与位置链接之间具有一定的内在关联,而且不同的用户在社交网络中的表现也存在差异,因此对于以上问题提出一种协作式个性化链接预测算法。针对用户的个性化特征,采用核密度估计方式对用户在时间和空间维度建模,基于兴趣组对用户进行重叠社团划分,并通过社团、好友以及签到关系进行个性化用户链接预测;基于个性化用户链接预测结果,利用从社团重启的随机游走预测用户的个性化位置链接;协作式个性化链接预测算法通过用户链接预测和位置链接预测的迭代使得两者性能相互提升。实验结果表明,所提算法相比于现有算法具有更好的预测性能。     

基于结构特性的超媒体模型

将超媒体的结构特性与形式语言学理论相结合,提供了一种有效地分析超媒体动态结构的特性的方法,该方法将超毁体组件集合映射为语言中的抽象符号集合,组件集合之上的链映射为语言中建立单词的方法,超媒体的结构化机械制映射为语言中的抽象符号集号,组件集合之上的链映射为语言中建立单词的方法,超媒体的结构化机制映射为语言中描述单词集合构造机制的语法,超媒体模拟为转换机,“链引导”动作模拟为“匹配对”序列,“链引导”的计算结果产生与上下文无关文法相对应的虚拟文档,从而为进一步研究超媒体的浏鉴语义及其它动态特性提供了理论基础。     

Argumentation-enabled interest-based personalised recommender system

Recommender systems (RSs) use information filtering to recommend information of interest (to a user). Similarly, personalisation can be adopted for recommendations in e-market. We propose a new and innovative system called as interest-based recommender system (IBRS) for personalisation of recommendations. The IBRS is an agent-based RS that takes into account user's preferences. It can transform a standard product (or service) into a dedicated solution for an individual. The system discovers interesting product alternatives based on user's underlying mental attitudes (likes and dislikes) during the repair process using argumentation. The proposed method combines a hybrid RS approach with automated argumentation-based reasoning between agents. The system improves results by improving the recommendation repair activity. We consider a book recommendation application, for experiment to carry out the system's (objective and subjective) evaluation using standard metrics. The experiments confirm that the proposed IBRS improves user's acceptance of the product as compared with a traditional hybrid method and an argumentation-enabled state-of-the-art recommendation method. The system has been found to be more effective than its traditional counterpart when dealing with the new user problems.     

The narrative approach to personalisation

This article describes the narrative approach to personalisation. This novel approach to the generation of personalised adaptive hypermedia experiences employs runtime reconciliation between a personalisation strategy and a number of contextual models (e.g. user and domain). The approach also advocates the late binding of suitable content and services to the generated personalised pathway resulting in an interactive composition that comprises services as well as content. This article provides a detailed definition of the narrative approach to personalisation and showcases the approach through the examination of two use-cases: the personalised digital educational games developed by the ELEKTRA and 80Days projects; and the personalised learning activities realised as part of the AMAS project. These use-cases highlight the general applicability of the narrative approach and how it has been applied to create a diverse range of real-world systems.     

Personalization in an interactive learning environment through a virtual character

Traditional hypermedia applications present the same content and provide identical navigational support to all users. Adaptive Hypermedia Systems (AHS) make it possible to construct personalized presentations to each user, according to preferences and needs identified. We present in this paper an alternative approach to educational AHS where a virtual character personalizes the interaction with the user through the use of a particular recommender system. The character has natural language communication abilities; it can learn students’ profiles and use this knowledge to recommend appropriate contents and activities. Through its interaction with the user, the character is able to collect and organize information about students in order to identify appropriate suggestions of contents. The recommender system employs a knowledge representation scheme that is easy to understand and to modify, enabling teachers/tutors to explore the types of recommendations being made and to appreciate why they are made. An experiment with computer science students was carried out in order to validate the approach proposed. The results of the experiment showed that the presentation of personalized links through a virtual character had a positive impact in the users’ perception of the system as a learning tool. The combination of the virtual character with a recommender system proved to be a good alternative for the delivery of personalized contents without making constant changes in the main user interface. This approach provides mechanisms to guide users through paths of study followed by students with similar profiles, without violating the human–computer interaction principle of perceived stability.     

Linking Together Books: Experiments in Adapting Published Material into Intermedia Documents

Links are among the distinguishing features of hypermedia and much research revolves around them. We survey issues related to links and address the problem of presenting links in such a way that the user can choose the most appropriate link. This issue is particularly important when a large number of links is available, a subject that has not received much attention yet but will become important as hypermedia document bases grow. We show that presentation of links can be dealt with by filtering out links that are not of interest to the user and by ranking the remaining nodes to simplify choice. After listing several heuristic approaches, we propose a method based on two quantitative measures of knowledge. This method can be used for rigorous numeric ranking of links on the basis of their contribution to the user's total knowledge.     

Context: An on-line documentation system

This paper presents CONTEXT, an on-line documentation system that is simple in concept and implementation. In spite of the simplicity, CONTEXT still provides substantial power and ease-of-use in accessing material in structured on-line manuals. This paper provides an overview of the system from both the writer's and the user's perspective.     

AH-questionnaire: An adaptive hierarchical questionnaire for learning styles

One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the general tendency of the student and not the specific score obtained in each dimension. In this context, we present AH-questionnaire, a new approach to minimize the number of questions needed to classify student Learning Styles. Based on the Felder-Silverman’s Learning Style Model, it aims at classifying students into categories in spite of providing precise scores. The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning style preference with high accuracy and only a few questions.     

Modeling Elementary Cognitive Abilities for Adaptive Hypermedia Presentation

The adaptation of hypermedia can be carried out at three levels, namely the content, navigation and presentation level. The presentation level is the least studied of the three, apparently because it refers to user properties that are not easy to model. In this paper, we present a new approach to modeling cognitive abilities that relies on basic mental functionalities. We describe the Cognitive User Modeling for Adaptive Presentation of Hyper-Documents (CUMAPH) environment, which mainly provides an authoring tool and an adaptation engine. The aim of this environment is to adapt a hyper-document presentation by selecting the elements that best fit the user cognitive profile. Its architecture is based on four main components: a cognitive user model, a hyper-document builder, an adaptation engine and a generic style sheet. To validate our approach, we designed an innovative protocol and conducted an experimental study involving 39 students. The first results show that an adaptive presentation can significantly increase the efficiency of hypermedia presentations.     

A User-Configurable Hypermedia-Based Interface Via the Functional Model of the Link

Hypermedia management systems often make use of database management systems for the persistent storage of links and of the linked information components. In this paper, we argue that a hypermedia management system with its support for reader-directed navigation provides an attractive type of database system interface in that it facilitates ease of database access or more specifically, of locating (‘discovering’) relevant information.

Furthermore, our approach to hypermedia management, based on the Functional Model of the link, provides for extensive user-configurability of that interface. This configurability particularly applies to fully dynamic links, which free the user from much of the link creation and maintenance duties.

A prototype system incorporating this approach is overviewed in this paper.     

Web-based knowledge exchange through social links in the workplace

Knowledge exchange between employees is an essential feature of recent commercial organisations on the competitive market. Based on the data gathered by various information technology (IT) systems, social links can be extracted and exploited in knowledge exchange systems of a new kind. Users of such a system ask their queries and the system recommends known and unknown experts selected out of user's friends. The friends either provide the solution or forward the query to their friends. By means of the established social paths to experts, the system facilitates informal learning and exchange of latent knowledge between organisation members in their workplace. The overall concept, limitations and detailed features of this novel knowledge exchange system are discussed in the article.     

Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE

In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more 'learning-focused' paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learner' preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner's learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system's adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners' attitudes towards the proposed instructional design.     

Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment

Adaptive Educational Hypermedia Systems aim to increase the functionality of hypermedia by making it personalised to individual learners. The adaptive dimension of these systems mainly supports knowledge communication between the system and the learner by adapting the content or the appearance of hypermedia to the knowledge level, goals and other characteristics of each learner. The main objectives are to protect learners from cognitive overload and disorientation by supporting them to find the most relevant content and path in the hyperspace. In the approach presented in this paper, learners' knowledge level and individual traits are used as valuable information to represent learners' current state and personalise the educational system accordingly, in order to facilitate learners to achieve their personal learning goals and objectives. Learners' knowledge level is approached through a qualitative model of the level of performance that learners exhibit with respect to the concepts they study and is used to adapt the lesson contents and the navigation support. Learners' individual traits and especially their learning style represent the way learners perceive and process information, and are exploited to adapt the presentation of the educational material of a lesson. The proposed approach has been implemented through various adaptation technologies and incorporated into a prototype hypermedia system. Finally, a pilot study has been conducted to investigate system's educational effectiveness.     

基于业务QoS保证的中继系统资源分配算法

在基于正交频分多址（OFDMA）的中继系统中,为了满足用户的QoS要求,保证系统的吞吐量最大的同时又保证用户公平性,给出了一种面向业务服务质量的资源分配算法。先根据用户在队列中的等待时延和用户对速率的需求引入时延优先级因子和速率优先级因子,以此计算用户的优先级。然后分别在回程链路和接入链路进行动态资源调度与分配。仿真结果表明,新算法能够兼顾中继用户和直传用户的性能,实现低丢包率、较好地满足 GBR需求,实现较高的系统吞吐量和公平性。     

Augmented reality as an interface to adaptive hypermedia systems

Adaptive Hypermedia has sought to tackle the problems of dealing with complex, heavily structured information and the presentation of views of that structure to users. Increasingly, adaptive content is achieved through different forms of context. Using two case-study applications, we will reflect on how Augmented Reality may present solutions to a number of Adaptive Hypermedia presentation problems. Each case study describes a different physical interaction metaphor for exposing the complex adaptation of hypermedia content in an intuitive way. The preliminary findings of our early evaluations are discussed. Finally, conclusions are drawn as to how Augmented Reality applications could use the modelling techniques of the Adaptive Hypermedia community to deal more easily with complex information.     

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.     

An adaptive website system to improve efficiency with web mining techniques

The paper proposes an adaptive web system—that is, a website that is capable of changing its original design to fit user requirements. For the purpose of improving shortcomings of the website, and also to make it much easier for users to access information, the system analyzes user browsing patterns from their access records. This paper concentrates on the operating-efficiency of a website—that is, the efficiency with which a group of users browse a website. By achieving high efficiency, users spend less operating cost to accomplish a desired user goal. Based on user access data, we analyze each user's operating activities as well as their browsing sequences. With this data, we can calculate a measure of the efficiency of the user's browsing sequences. The paper develops an algorithm to accurately calculate this efficiency and to suggest how to increase the efficiency of user operations. This can be achieved in two ways: (i) by adding a new link between two web pages, or (ii) by suggesting to designers to reconsider existing inefficient links so as to allow users to arrive at their target pages more quickly. Using this algorithm, we develop a prototype to prove the concept of efficiency. The implementation is an adaptive website system to automatically change the website architecture according to user browsing activities and to improve website usability from the viewpoint of efficiency.     

On the use of ancillary data by applying the concepts of the theory of evidence to remote sensing digital image classification

In this study we investigate a new approach to implement concepts developed by the ‘theory of evidence’ to remote sensing digital image classification. In the proposed approach, auxiliary variables are structured as layers in a Geographical Information System (GIS)-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user's and producer's accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.