Mobile Health applications facilitate users in managing their health and receiving health care services. Despite a proven utility, several privacy concerns have been raised. At this frontier, we should be responsible for enhancing our understanding of user privacy concerns and enabling privacy by design. This study uncovers and compares individual privacy concerns within health applications. Our scenario-based approach considers 432 different health application data exchanges. Participants (n=189) assessed 27 unique scenarios across three categories: data type, data sharing, and data retention. The findings show user concerns in the three categories, most prominently, data sharing. From this position, we propose design approaches to improve user awareness and control of health application data. We discuss the finding’s implications, including pre-emptive and predictive strategies for health app developers.

This study investigates the impact of the perceptions of antisocial behaviour on the use of the social media platform Twitter. We extend the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Perception of Antisocial Behaviour as a risk factor, and two supporting constructs: Strategic Self-Presentation and Protective Self-Presentation. We call this extended model Technology Acceptance and Use under Risk (TAUR). We investigate two groups via an online questionnaire, contrasting Anglophone countries (the UK, USA, and Canada, 200 responses), with Saudi Arabia (540 responses). In both cases the data shows that the Perception of Antisocial Behaviour impacts Twitter use, but not directly, rather it negatively impacts the influence of other factors such as Behavioural Intention – it also shows that this affects Anglophones more than Saudis. This indicates that future work should differentiate between different cultural groups, and different solutions may be needed to assuage users’ fears in different parts of the world.

In this paper we summarized the main historical steps in making the Web, its foundational principles and its evolution. First we mention some of the influences and streams of thought that interacted to bring the Web about. Then we recall that its birthplace, the CERN, had a need for a global hypertext system and at the same time was the perfect microcosm to provide a cradle for the Web. We stress how this invention required to strike a balance between the integration of and the departure from the existing and emerging paradigms of the day. We then review the pillars of the Web architecture and the features that made the Web so viral compared to competitors. Finally we survey the multiple mutations the Web underwent no sooner it was born, evolving in multiple directions. We conclude on the fact the Web is now an architecture, an artefact, a science object and a research and development object, and of which we haven’t seen the full potential yet.

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the Graph-based Visual-Semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features; 2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations; 3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. Our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN by promoting the semantic linkage modelling of visual features.

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depression-related feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a cross-domain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.

Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 times speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.

This paper presents a novel Semantic Web browser, called SemWeB. SemWeB supports Web browsing using Linked Open Data and personalization. This is the first time a Semantic Web browser combines the advances in Adaptive Hypermedia (i.e. personalization) and Linked Open Data. In particular, users are provided with a personalized and semantically rich Web browsing experience. For example, SemWeB supplies goal-based adaptive information retrieval from the LOD, adaptive link recommendation using a novel semantic relatedness measure and adaptive content creation. A user based study was used to assess the value of LOD-based hyperlinks and personally relevant content compared with a standard Web browser (Firefox). Results showed that users were able to browse and view more relevant information, as well as, value significance of LOD-based hyperlinks and personalized content in comparison to a standard Web browser.

Programming has been cited as a barrier for female students to enjoy and pursue computing as a career or at higher education. However, there are examples of good practice, which demonstrate that programming can act as a bridge rather than a barrier. As a result of surveying 103 students and interviewing 60 students from 3 different UK higher education institutions and this paper demonstrates that female students can enjoy programming and take it further for their careers.

With the increasing amount of multimedia content on the web added as user generated content in Web 2.0 websites, conventional multimedia information retrieval is presented with new challenges. It is no longer possible to rely only on meta-data based retrieval but to consider also content based techniques combined with the collective knowledge generated by users' contributions and geo-referenced meta-data. Tagging is a modest way to annotate such documents and fails to capture a full semantic description of the document content. This report concerns ongoing research to investigate a means to identify, model and utilise semantic descriptions of the user-generated content in Web 2.0 documents using a hybrid approach. The approach consists of three main components, natural language processing, image analysis and a shared knowledge base. In this paper we describe the complete model but, as the image analysis component is in its early stages, the results focus on the natural language processing and the knowledge base. We show that the additional use of these components can improve retrieval and analysis performance over that based only on Web 2.0 tags.

The World Wide Web has changed the world. It has changed the ways we communicate, collaborate, and educate. We increasingly live in a Web-dependent society in a Web-dependent world. The Web is also the largest human information construct and it is growing faster than any other system. However, it is a striking fact that there is no systematic discipline to study the Web. We need to understand the current, evolving, and potential Web but at the moment we have no means of predicting the impact that future developments in the Web will have on society or business. Web Science aims to anticipate these impacts. It is the study of the social behaviours in the Web at the inter-person, inter-organizational and societal level, the technologies that enable and support this behaviour, and the interactions between these technologies and behaviours. It is therefore inherently interdisciplinary and at even the simplest level represents a fundamental collaboration between computer science and the social sciences.

A preliminary survey of 680 UK school pupils was conducted in order to get an understanding of how experiences of computing out of school and at school influence the perception of computing careers and future career plans in computing. The study found that pupils perceive themselves in IT careers. This poster will demonstrate how these experiences influence future career choice.

This paper presents a semantic portal, SEMPort, which provides better user support with personalized views, semantic navigation, ontology-based search and three different kinds of semantic hyperlinks. Distributed content editing and provision is supplied for the maintenance of the contents in real-time. As a case study, SEMPort is tested on the Course Modules Web Page (CMWP) of the School of Electronics and Computer Science (ECS).

The aim of this article is to produce an alternative view of the adaptive hypermedia (AH) domain from a contextually-aware open hypermedia (OH) perspective. We believe that a wide range of AH techniques can be supported with a small number of OH structures, which can be combined together to create more complex effects, possibly simplifying the development of new AH systems. In this work we reexamine Brusilovsky's taxonomy of AH techniques from a structural OH perspective. We also show that it is possible to identify and model common structures across the taxonomy of adaptive techniques. An agent-based adaptive hypermedia system called HA 3 L is presented, which uses these OH structures to provide a straightforward implementation of a variety of adaptive hypermedia techniques. This enables us to reflect on the structural equivalence of many of the techniques, demonstrates the advantages of the OH approach, and can inform the design of future adaptive hypermedia systems.

The key element to support ad-hoc resource sharing on the Web is to discover resources of interest. The hypermedia paradigm provides a way of overlaying a set of resources with additional information in the form of links to help people find other resources. However, existing hypermedia approaches primarily develop mechanisms to enable resource sharing in a fairly static, centralized way. Recent developments in distributed computing, on the other hand, introduced peer-to-peer (P2P) computing that is notable for employing distributed resources to perform a critical function in a more dynamic and ad-hoc scenario. We investigate the feasibility and potential benefits of bringing together the P2P paradigm with the concept of hypermedia link services to implement ad-hoc resource sharing on the Web. This is accomplished by utilizing a web-based Distributed Dynamic Link Service (DDLS) as a testbed and addressing the issues arising from the design, implementation, and enhancement of the service. Our experimental result reveals the behavior and performance of the semantics-based resource discovery in DDLS and demonstrates that the proposed enhancing technique for DDLS, topology reorganization, is appropriate and efficient.

Many Semantic Web applications address the needs of human readers of the Web (e.g. searching, annotating), but these technologies can also address the needs of human writers of the Web. The WiCK project has explored the application of knowledge bases and services to the Office desktop, in order to assist document production, culminating in the WiCKOffice environment. This aim of this demonstration is to showcase the most recent offshoot of the WiCKOffice development, WiCKLite: a lightweight component for connecting knowledge services to document templates in order to deliver targeted assistance to end users.

VOEU is a virtual university which we designed, implemented and deployed in the Wessex Region of the UK for use by higher surgical trainees in the orthopaedic domain. A particularly original part of this virtual university is the virtual research environment (VRE) which provides the infrastructure to support trainee surgeons in carrying out and publishing research and clinical trails. The current implementation of the VRE is tightly coupled, but we are now engaged in a new project to redeploy the VRE as loosely coupled grid services. This paper describes the VRE, and discusses the advantages that will be achieved by implementing it as grid services.

The research presented in this paper focuses on the issue of how a recommender system can support the task of searching documents and artifacts constructed in a software development project. The "A LA" (Associative Linking of Attributes) system represents a recommender facility built on top of a document management system. The facility provides assistance to finding items by utilising hypertextually connected metadata. In order to determine metadata relationships "A LA" employs techniques of content analysis together with exploiting user-generated metadata and usage logs. An evaluation study that compares querying using a full text search approach with the "A LA" method for finding relevant documents was conducted.

The Web is full of documents which must be interpreted by human readers and by software agents (search engines recommender systems clustering processes etc.). Although Web standards have addressed format obfuscation by using XML schemas and stylesheets to specify unambiguous structure and presentation semantics interpretation is still hampered by the fundamental ambiguity of information in PCDATA text. Even the most easily distinguishable kinds of knowledge such as article citations and proper nouns (referring to people organisations projects products technical concepts) have to be identified by fallible post-hoc extraction processes. The WiCK project has investigated the writing process in a Semantic Web environment where knowledge services exist and actively assist the author. In this paper we discuss the need to make knowledge an explicit part of the document representation and the advantages and disadvantages of this step.

A problem facing many textbook authors (including one of the authors of this paper) is the inevitable delay between new advances in the subject area and their incorporation in a new (paper) edition of the textbook. This means that some textbooks are quickly considered out of date, particularly in active technological areas such as the Web, even though the ideas presented in the textbook are still valid and important to the community. This paper describes our approach to building a companion website for the textbook Hypermedia and the Web: An Engineering Approach. We use Bloom's taxonomy of educational objectives to critically evaluate a number of authoring and presentation techniques used in existing companion websites, and adapt these techniques to create our own companion website using Semantic Web technologies in order to overcome the identified weaknesses. Finally, we discuss a potential model of future companion websites, in the context of an e-publishing, e-commerce Semantic Web services scenario.

The World-Wide Web was originally developed as a shared, writable, hypertext medium, a facility that is still widely needed.We have recently developed a Web-based management reporting system for a legal firm in an attempt to improve the efficiency and management of their overall business process. This paper shares our experiences in relating the firm's specific writing and issue tracking tasks to existing Web, open hypermedia, and Semantic Web research, and describes why we chose to develop a new solution --- a set of open hypermedia components collectively called the Management Reporting System --- rather than employ an existing system.

Article Conceptual linking: ontology-based open hypermedia Share on Authors: Leslie Carr Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKView Profile , Wendy Hall Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKView Profile , Sean Bechhofer Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UKView Profile , Carole Goble Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UKView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 334–342https://doi.org/10.1145/371920.372084Online:01 April 2001Publication History 93citation1,575DownloadsMetricsTotal Citations93Total Downloads1,575Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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We describe the integration of content based techniques for navigation of musical information (primarily in MIDI representation) into a multiagent system for distributed information management. This exercise illustrates the application of the multiagent approach to engineering distributed multimedia information systems and raises some design issues. Keywords Software agents, content based navigation, pitch contours, collaborative filtering, open hypermedia, query routing. 1. INTRODUCTION The goal of our research activity is to bring music into the distributed multimedia information space that we manage and navigate through the use of hypermedia tools. Music proves to be an excellent example of multimedia data: it exists in a variety formats, some highly structured, it can be transmitted using a store-and-forward or streaming model, it can be synchronized with other media. Hence our investigation addresses more general research issues in the application of software agents to mul...

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In this paper, we study how linking mechanisms contribute to the expressiveness of hypertext systems. For this purpose, we formalise hypertext systems as abstract machines. As the primary benefit of hypertext systems is to be able to read documents non-linearly, their expressiveness is defined in terms of the ability to follow links. Then, we classify hypertext systems according to the power of the underlying automaton. The model allow us to compare embedded vs separate links and simple vs generic links. Then, we investigate history mechanisms, adaptive hypertexts, and functional links. Our conclusion is that simple links, whether embeded or separate, generic links, and some adaptive links all give hypertext systems the power of finite state automata. The history mechanism confers them the power of pushdown automata, whereas the general functional links give them Turing completeness.

A hypermedia system was introduced into an undergraduate biology curriculum in order to investigate the patterns of students' interaction with, and attitudes to such a system, both to develop the interface of the system itself, and to provide objective means of describing its usage in a real learning situation. This was seen as a prerequisite to any evaluation of actual learning outcomes. The paper describes the development process which involved not only the evolution of the system itself, but the construction and implementation of assessment tools. The use of these tools enabled the authors to draw a number of conclusions about the patterns of interaction that students demonstrated when given various learning tasks to carry out. Interactions were seen to be driven primarily by the task, rather than by any individual preferences on the part of the users. It is suggested that this may have important implications for the knowledge that users gain from hypermedia systems.

Abstract This paper describes the design and implementation of a multimedia information management environment that is based on an open model for hypermedia. The model, known as Microcosm, is currently implemented in Microsoft Windows and supports many different media types. The open nature of the model and the separation of the links from the data, allows links to be applied across any application running under Windows or the OS/2 version 2 desktop, thus creating a truly integrated environment. One example, which is, discussed in the paper, is the integration of a hypermedia database with a geographical information system. The paper also discusses the application of novel interfaces to dynamic media within a hypermedia system, including the use of moving icons (micons).