ENHANCING IMAGE FINDABILITY THROUGH A DUAL-PERSPECTIVE NAVIGATION FRAMEWORK

This dissertation focuses on investigating whether users will locate desired images more efficiently and effectively when they are provided with information descriptors from both experts and the general public. This study develops a way to support image finding through a human-computer interface by providing subject headings and social tags about the image collection and preserving the information scent (Pirolli, 2007) during the image search experience. In order to improve search performance most proposed solutions integrating experts’ annotations and social tags focus on how to utilize controlled vocabularies to structure folksonomies which are taxonomies created by multiple users (Peters, 2009). However, these solutions merely map terms from one domain into the other without considering the inherent differences between the two. In addition, many websites reflect the benefits of using both descriptors by applying a multiple interface approach (McGrenere, Baecker, & Booth, 2002), but this type of navigational support only allows users to access one information source at a time. By contrast, this study is to develop an approach to integrate these two features to facilitate finding resources without changing their nature or forcing users to choose one means or the other. Driven by the concept of information scent, the main contribution of this dissertation is to conduct an experiment to explore whether the images can be found more efficiently and effectively when multiple access routes with two information descriptors are provided to users in the dual-perspective navigation framework. This framework has proven to be more effective and efficient than the subject heading-only and tag-only interfaces for exploratory tasks in this study. This finding can assist interface designers who struggle with determining what information is best to help users and facilitate the searching tasks. Although this study explicitly focuses on image search, the result may be applicable to wide variety of other domains. The lack of textual content in image systems makes them particularly hard to locate using traditional search methods. While the role of professionals in describing items in a collection of images, the role of the crowd in assigning social tags augments this professional effort in a cost effective manner

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

publishedVersio

Software variability in service robotics

Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments. To this end, the software needs to be variable—to customize robots for different customers, hardware, and operating environments. However, variability adds substantial complexity and needs to be managed—yet, ad hoc practices prevail in the robotics domain, challenging effective software reuse, maintenance, and evolution. To improve the situation, we need to enhance our empirical understanding of variability in robotics. We present a multiple-case study on software variability in the vibrant and challenging domain of service robotics. We investigated drivers, practices, methods, and challenges of variability from industrial companies building service robots. We analyzed the state-of-the-practice and the state-of-the-art—the former via an experience report and eleven interviews with two service robotics companies; the latter via a systematic literature review. We triangulated from these sources, reporting observations with actionable recommendations for researchers, tool providers, and practitioners. We formulated hypotheses trying to explain our observations, and also compared the state-of-the-art from the literature with the-state-of-the-practice we observed in our cases. We learned that the level of abstraction in robotics software needs to be raised for simplifying variability management and software integration, while keeping a sufficient level of customization to boost efficiency and effectiveness in their robots’ operation. Planning and realizing variability for specific requirements and implementing robust abstractions permit robotic applications to operate robustly in dynamic environments, which are often only partially known and controllable. With this aim, our companies use a number of mechanisms, some of them based on formalisms used to specify robotic behavior, such as finite-state machines and behavior trees. To foster software reuse, the service robotics domain will greatly benefit from having software components—completely decoupled from hardware—with harmonized and standardized interfaces, and organized in an ecosystem shared among various companies

The Effect of Interactivity on Online Consumer Response: An Investigation into the UK Car Brand Websites

The research presented in this thesis is concerned with the effects of interactivity on consumer responses to brands and their web sites within the context of consumer-brand interactions online. More specifically, this research aimed to determine: a) whether brands are perceived differently by consumers online versus offline, b) whether the interactivity of a brand's web site influenced consumer perceptions and attitudes to that brand and its web site, and, c) how brands add value to their web sites, and what effect the concept of value has in consumer-brand interactions online. To answer these research questions, a two-phase study was conducted. In the first phase, exploratory interviews with Internet and brand experts were conducted, and interview transcripts were analysed under Grounded Theory methodology guidelines. This exploratory phase allowed this researcher to develop an original conceptual model, grounded in empirical data and supported by extant literature. In the second phase of the study, the propositions emerging from the conceptual model were tested using data obtained from controlled laboratory experiments conducted with seventy-eight participants from the Open University staff and research students. The data was statistically analysed and substantial support was found for the model and the central proposition that interactivity of a web site would have a significant effect on how consumers evaluate, perceive and develop attitudes to the web site in question. This thesis helps fill a gap in academic literature in understanding the role of interactivity in consumer behaviour online. It also provides managerial contributions with the interactive features assessment tool, identification of the discrepancy between structural and perceived interactivity, and specific findings on consumer response variables, as these help managers better understand consumers' interactions with their web sites, and develop online branding strategies utilising the interactivity of the online medium and Internet technologies to better effect

INVESTIGATING SEARCH PROCESSES IN COLLABORATIVE EXPLORATORY WEB SEARCH

People are often engaged in collaboration in workplaces or daily life due to the complexity of tasks. In the information seeking and retrieval environment, the task can be as simple as fact-finding or a known-item search, or as complex as exploratory search. Given the complex nature of the information needs, exploratory searches may require the collaboration among multiple people who share the same search goal. For instance, students may work together to search for information in a collaborative course project; friends may search together while planning a vacation. There are demands for collaborative search systems that could support this new format of search (Morris, 2013). Despite the recognized importance of understanding search process for designing successful search system (Bates, 1990; M. Hearst, 2009), it is particularly difficult to study collaborative search process because of the complex interactions involved. In this dissertation, I propose and demonstrate a framework of investigating search processes in the collaborative exploratory search. I designed a laboratory-based user study to collect the data, compared two search conditions: individual search and collaborative search as well as two task types through the study. I first applied a novel Hidden Markov Model approach to analyze the search states in the collaborative search process, the results of which provide a holistic picture of the collaborative search process. I then investigated two important components in the collaborative search process – query behaviors and communications. The findings reveal the characteristics of query and communication patterns in the collaborative search. It also suggests that although the collaboration between two people on search did not achieve a higher performance than two individuals, the collaboration indeed make people feel more satisfied with their performance and less stressed. The results of this study not only provide implications for designing effective collaborative search systems, but also show valuable research directions and methodologies for other researchers

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Search engines that learn from their users

More than half the world's population uses web search engines, resulting in over half a billion queries every single day. For many people, web search engines such as Baidu, Bing, Google, and Yandex are among the first resources they go to when a question arises. Moreover, for many search engines have become the most trusted route to information, more so even than traditional media such as newspapers, news websites or news channels on television. What web search engines present people with greatly influences what they believe to be true and consequently it influences their thoughts, opinions, decisions, and the actions they take. With this in mind two things are important, from an information retrieval research perspective. First, it is important to understand how well search engines (rankers) perform and secondly this knowledge should be used to improve them. This thesis is about these two topics: evaluation of search engines and learning search engines. In the first part of this thesis we investigate how user interactions with search engines can be used to evaluate search engines. In particular, we introduce a new online evaluation paradigm called multileaving that extends upon interleaving. With multileaving, many rankers can be compared at once by combining document lists from these rankers into a single result list and attributing user interactions with this list to the rankers. Then we investigate the relation between A/B testing and interleaved comparison methods. Both studies lead to much higher sensitivity of the evaluation methods, meaning that fewer user interactions are required to arrive at reliable conclusions. This has the important implication that fewer users need to be exposed to the results from possibly inferior search engines. In the second part of this thesis we turn to online learning to rank. We learn from the evaluation methods introduced and extended upon in the first part. We learn the parameters of base rankers based on user interactions. Then we use the multileaving methods as feedback in our learning method, leading to much faster convergence than existing methods. Again, the important implication is that fewer users need to be exposed to possibly inferior search engines as they adapt more quickly to changes in user preferences. The last part of this thesis is of a different nature than the earlier two parts. As opposed to the earlier chapters, we no longer study algorithms. Progress in information retrieval research has always been driven by a combination of algorithms, shared resources, and evaluation. In the last part we focus on the latter two. We introduce a new shared resource and a new evaluation paradigm. Firstly, we propose Lerot. Lerot is an online evaluation framework that allows us to simulate users interacting with a search engine. Our implementation has been released as open source software and is currently being used by researchers around the world. Secondly we introduce OpenSearch, a new evaluation paradigm involving real users of real search engines. We describe an implementation of this paradigm that has already been widely adopted by the research community through challenges at CLEF and TREC.</jats:p