Academic Integrity Resources - links and guides

an online tutorial, a pdf version, a powerpoint presentation, links to regulations

A community based approach for managing ontology alignments

The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefitfrom human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe a prototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

Problems and Promises of Using LMS Learner Analytics for Assessment: Case Study of a First-Year English Program

Learning management systems (LMS) are widely used in education. They offer the potential for assessing student learning, but the reality of using them for this is problematic. This case study chronicles efforts by librarians at Marquette University to use LMS data to assess students’ information literacy knowledge in Marquette’s first-year English program

Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Developing front-end Web 2.0 technologies to access services, content and things in the future Internet

The future Internet is expected to be composed of a mesh of interoperable web services accessible from all over the web. This approach has not yet caught on since global user?service interaction is still an open issue. This paper states one vision with regard to next-generation front-end Web 2.0 technology that will enable integrated access to services, contents and things in the future Internet. In this paper, we illustrate how front-ends that wrap traditional services and resources can be tailored to the needs of end users, converting end users into prosumers (creators and consumers of service-based applications). To do this, we propose an architecture that end users without programming skills can use to create front-ends, consult catalogues of resources tailored to their needs, easily integrate and coordinate front-ends and create composite applications to orchestrate services in their back-end. The paper includes a case study illustrating that current user-centred web development tools are at a very early stage of evolution. We provide statistical data on how the proposed architecture improves these tools. This paper is based on research conducted by the Service Front End (SFE) Open Alliance initiative

Towards building a review recommendation system that trains novices by leveraging the actions of experts

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find helpful information about a product (Porter, 2010). Thus, there is a need to train reviewers to write better reviews, which could in turn better serve customers, vendors, and online e-stores. In this Thesis, we propose a review recommendation framework to train reviewers to better write about their experiences with a product by leveraging the behaviors of expert reviewers who are good at writing helpful reviews. First, we use clustering to model reviewers into different classes that reflect different skill levels to write a quality review such as expert, novice, etc. Through temporal analysis of reviewer behavior, we have found that reviewers evolve over time, with their reviews becoming better or worse in quality and more or less in quantity. We also investigate how reviews are valued differently across different product categories. Through machine learning-based classification techniques, we have found that, for products associated with prevention consumption goal, longer reviews are perceived to be more helpful; and, for products associated with promotion consumption goal, positive reviews are more helpful than negative ones. In this Thesis, our proposed review recommendation framework is aimed to help a novice or conscientious reviewer become an expert reviewer. Our assumption is that a reviewer will reach the highest level of expertise by learning from the experiences of his or her closest experts who have a similar evolutionary pattern to that of the reviewer who is being trained. In order to provide assistance with intermediate steps for the reviewer to grow from his or her current state to the highest level of expertise, we want to recommend the positive actions—that are not too far out of reach of the reviewer—and discourage the negative actions—that are within reach of the reviewer—of the reviewer’s closest experts. Recommendations are personalized to fit the expertise level of reviewers, their evolution trend, and product category. Using the proposed review recommendation system framework we have found that for a random reviewer, at least 80% of the reviews posted by closest experts were of higher quality than that of the novice reviewer. This is verified in a dataset of 2.3 million reviewers, whose reviews cover products from nine different product categories such as Books, Electronics, Cellphones and accessories, Grocery and gourmet food, Office product, Health and personal care, Baby, Beauty, and Pet supplies. Advisor: Leen-Kiat So

Establishing the design knowledge for emerging interaction platforms

While awaiting a variety of innovative interactive products and services to appear in the market in the near future such as interactive tabletops, interactive TVs, public multi-touch walls, and other embedded appliances, this paper calls for preparation for the arrival of such interactive platforms based on their interactivity. We advocate studying, understanding and establishing the foundation for interaction characteristics and affordances and design implications for these platforms which we know will soon emerge and penetrate our everyday lives. We review some of the archetypal interaction platform categories of the future and highlight the current status of the design knowledge-base accumulated to date and the current rate of growth for each of these. We use example designs illustrating design issues and considerations based on the authors’ 12-year experience in pioneering novel applications in various forms and styles