DYNIQX: A novel meta-search engine for the web

The effect of metadata in collection fusion has not been sufficiently studied. In response to this, we present a novel meta-search engine called Dyniqx for metadata based search. Dyniqx integrates search results from search services of documents, images, and videos for generating a unified list of ranked search results. Dyniqx exploits the availability of metadata in search services such as PubMed, Google Scholar, Google Image Search, and Google Video Search etc for fusing search results from heterogeneous search engines. In addition, metadata from these search engines are used for generating dynamic query controls such as sliders and tick boxes etc which are used by users to filter search results. Our preliminary user evaluation shows that Dyniqx can help users complete information search tasks more efficiently and successfully than three well known search engines respectively. We also carried out one controlled user evaluation of the integration of six document/image/video based search engines (Google Scholar, PubMed, Intute, Google Image, Yahoo Image, and Google Video) in Dyniqx. We designed a questionnaire for evaluating different aspect of Dyniqx in assisting users complete search tasks. Each user used Dyniqx to perform a number of search tasks before completing the questionnaire. Our evaluation results confirm the effectiveness of the meta-search of Dyniqx in assisting user search tasks, and provide insights into better designs of the Dyniqx' interface

Social Search with Missing Data: Which Ranking Algorithm?

Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods

CoAKTinG: Collaborative Advanced Knowledge Technologies in the Grid

Grid infrastructures coupled with semantic web linkage and reasoning open up intriguing new possibilities for scientific collaboration. In this short paper, we outline the research agenda and collaboration technologies under development within the CoAKTinG project: Collaborative Advanced Knowledge Technologies in the Grid. CoAKTinG will provide tools to assist scientific collaboration by integrating intelligent meeting spaces, ontologically annotated media streams from online meetings, decision rationale and group memory capture, meeting facilitation, issue handling, planning and coordination support, constraint satisfaction, and instant messaging/presence. Their integration is illustrated through an extended use scenario

Wikis of Locality: Insights from the Open Guides

In this paper we describe an emerging form of wikis - wikis of locality – that support physical rather than virtual communities. We draw on our experience as administrators of the Open Guide to Milton Keynes, one of the Open Guides family of community developed local information guides built using wiki software, and present observations of the potential value and unique characteristics of wikis of locality from a practitioner’s perspective. Preliminary findings from a current survey of other Open Guide administrators are presented to highlight types of usage, issues and potential areas for future research

From Buddyspace to CitiTag: Large-scale Symbolic Presence for Community Building and Spontaneous Play

In this paper we discuss the conceptual framework and principles that guide our work in the design of large-scale informal environments for collaborative work, learning and play, aiming to foster social bonds and to provide an exciting testbed for emergent social behaviours. We present three different applications we have developed: Buddyspace, an Instant Messaging environment for community building, BumperCars, an online presence-based multiplayer game and CitiTag, an experimental wireless mixed reality game

The Open University at TREC 2006 Enterprise Track ExpertSearch Task

The Multimedia and Information Systems group at the Knowledge Media Institute of the Open University participated in the Expert Search task of the Enterprise Track in TREC 2006. We have proposed to address three main innovative points in a two-stage language model, which consists of a document relevance model and a cooccurrence model, in order to improve the performance of expert search. The three innovative points are based on characteristics of documents. First, document authority in terms of their PageRanks is considered in the document relevance model. Second, document internal structure is taken into account in the co-occurrence model. Third, we consider multiple levels of associations between experts and query terms in the co-occurrence model. Our experiments on the TREC2006 Expert Search task show that addressing the above three points has led to improved effectiveness of expert search on the W3C dataset

COVID-19 Antibody Test/Vaccination Certification There’s an app for that

Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 ‘Immunity Passport’ has been mooted as a way to enable individuals to return back to work. While the quality of antibody testing, the avail- the ability of vaccines, and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification for test results and vaccinations. Methods: We developed a prototype mobile phone app and requisite decentralized server architecture that facilitates instant verification of tamper-proof test results. Personally identifiable information is only stored at the user’s discretion, and the app allows the end-user selectively to present only the specific test result with no other personal information revealed. The architecture, designed for scalability, relies upon (a) the 2019 World Wide Web Consortium standard called ‘Verifiable Credentials’, (b) Tim Berners-Lee’s decentralized personal data platform ‘Solid’, and (c) a Consortium Ethereum-based blockchain. Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data. Benchmark performance tests show it to scale linearly in the worst case, as significant processing is done locally on each app. For the test certificate Holder, Issuer (e.g. healthcare staff, pharmacy) and Verifier (e.g. employer), it is ‘just another app’ which takes only minutes to use. Conclusions: The app and decentralized server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect ‘waiting in the wings’ for the biological issues, plus key ethical issues raised in the discussion section, to be resolved

Dyniqx: a novel meta-search engine for metadata based cross search

The effect of metadata in collection fusion has not been sufficiently studied. In response to this, we present a novel meta-search engine called Dyniqx for metadata based cross search. Dyniqx exploits the availability of metadata in academic search services such as PubMed and Google Scholar etc for fusing search results from heterogeneous search engines. In addition, metadata from these search engines are used for generating dynamic query controls such as sliders and tick boxes etc which are used by users to filter search results. Our preliminary user evaluation shows that Dyniqx can help users complete information search tasks more efficiently and successfully than three well known search engines respectively

MSG Instant Messenger: Social Presence and Location for the "Ad Hoc Learning Experience"

"Elearning2.0" promises to harness the power of three of today's most disruptive technologies: social software, elearning, and Web2.0. Our own work in this disruptive space takes as a starting premise that social networking is critical for learning: finding the right person can be more important than 'scouring the web for an answer', particularly when hand-holding or other explanatory services are required. Moreover, it 'feels good' to know that others are around! With this in mind, we have taken our older BuddySpace tool, and created a new tool we call MSG, which is simpler, available as Open Source, and integrates cleanly with a number of other services, including Moodle and Google Maps.Editor: Patrick McAndrew (Open University, UK).Reviewers: Robert Schuwer (Open University, NL) and Patrick McAndrew (Open University, UK)

Exploiting semantic association to answer 'vague queries'.

Although today's web search engines are very powerful, they still fail to provide intuitively relevant results for many types of queries, especially ones that are vaguely-formed in the users own mind. We argue that associations between terms in a search query can reveal the underlying information needs in the users mind and should be taken into account in search. We propose a multi-faceted approach to detect and exploit such associations. The CORDER method measures the association strength between query terms, and queries consisting of terms having low association strength with each other are seen as vague queries. For a vague query, we use WordNet to find related terms of the query terms to compose extended queries, relying especially on the role of least common subsumers (LCS). We use relation strength between terms calculated by the CORDER method to refine these extended queries. Finally, we use the Hyperspace Analogue to Language (HAL) model and information flow (IF) method to expand these refined queries. Our initial experimental results on a corpus of 500 books from Amazon shows that our approach can find the right books for users given authentic vague queries, even in those cases where Google and Amazon's own book search fail