Combining the power of online crowdsourcing with creative writing user interfaces

The goal of this work was to provide professional and amateur writers with a new way of enhancing their productivity and mental well-being, by helping them overcoming writers block and being able to achieve a state of optimal experience while writing. Our approach is based on bringing together different components to create what we call a creative moment. A creative moment is composed by an image, a text, a mood, a location and a color. The color presented in the creative moment varied according to the mood that was associated to the creative moment. With the creative moments we hoped that our users could have a way to easily trigger their creativity and have a kick start in their work. The prototyping of a web crowdsourcing platform, named CreativeWall, and a Microsoft Word Add-In, that was used on the user study performed, is described and their implementations are discussed. The user study reveals that our approach does have a positive influence in the productivity of the participants when compared with another existing approach. The study also revealed that our approach can ease the process of achieving a state of optimal experience by enhancing one of the dimensions presented on the Flow Theory. At the end we present what we consider would be some possible future developments for the concept created during the development of this work.

Combining the power of online crowdsourcing with creative writing user interfaces

The goal of this work was to provide professional and amateur writers with a new way of enhancing their productivity and mental well-being, by helping them overcoming writers block and being able to achieve a state of optimal experience while writing. Our approach is based on bringing together different components to create what we call a creative moment. A creative moment is composed by an image, a text, a mood, a location and a color. The color presented in the creative moment varied according to the mood that was associated to the creative moment. With the creative moments we hoped that our users could have a way to easily trigger their creativity and have a kick start in their work. The prototyping of a web crowdsourcing platform, named CreativeWall, and a Microsoft Word Add-In, that was used on the user study performed, is described and their implementations are discussed. The user study reveals that our approach does have a positive influence in the productivity of the participants when compared with another existing approach. The study also revealed that our approach can ease the process of achieving a state of optimal experience by enhancing one of the dimensions presented on the Flow Theory. At the end we present what we consider would be some possible future developments for the concept created during the development of this work.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

Investigation into a best practice model for providing an integrated user experience with mobile cloud applications

Mobile Cloud Computing promises to overcome the physical limitations of mobile devices by executing demanding mobile applications on cloud infrastructure. In practice, implementing this paradigm is difficult; network disconnection often occurs, bandwidth may be limited, and a large power draw is required from the battery, resulting in a poor user experience. This thesis presents a mobile cloud middleware solution, Context Aware Mobile Cloud Services (CAMCS), which provides cloudbased services to mobile devices, in a disconnected fashion. An integrated user experience is delivered by designing for anticipated network disconnection, and low data transfer requirements. CAMCS achieves this by means of the Cloud Personal Assistant (CPA); each user of CAMCS is assigned their own CPA, which can complete user-assigned tasks, received as descriptions from the mobile device, by using existing cloud services. Service execution is personalised to the user's situation with contextual data, and task execution results are stored with the CPA until the user can connect with his/her mobile device to obtain the results. Requirements for an integrated user experience are outlined, along with the design and implementation of CAMCS. The operation of CAMCS and CPAs with cloud-based services is presented, specifically in terms of service description, discovery, and task execution. The use of contextual awareness to personalise service discovery and service consumption to the user's situation is also presented. Resource management by CAMCS is also studied, and compared with existing solutions. Additional application models that can be provided by CAMCS are also presented. Evaluation is performed with CAMCS deployed on the Amazon EC2 cloud. The resource usage of the CAMCS Client, running on Android-based mobile devices, is also evaluated. A user study with volunteers using CAMCS on their own mobile devices is also presented. Results show that CAMCS meets the requirements outlined for an integrated user experience.

Structuring free-form tagging in online news

Tagging has become one of the key activities in next generation websites which allow users selecting short labels to annotate, manage, and share multimedia information such as photos, videos and bookmarks. Tagging does not require users any prior training before participating in the annotation activities as they can freely choose any terms which best represent the semantic of contents without worrying about any formal structure or ontology. However, the practice of free-form tagging can lead to several problems, such as synonymy, polysemy and ambiguity, which potentially increase the complexity of managing the tags and retrieving information. To solve these problems, this research aims to construct a lightweight indexing scheme to structure tags by identifying and disambiguating the meaning of terms and construct a knowledge base or dictionary. News has been chosen as the primary domain of application to demonstrate the benefits of using structured tags for managing the rapidly changing and dynamic nature of news information. One of the main outcomes of this work is an automatically constructed vocabulary that defines the meaning of each named entity tag, which can be extracted from a news article (including person, location and organisation), based on experts suggestions from major search engines and the knowledge from public database such as Wikipedia. To demonstrate the potential applications of the vocabulary, we have used it to provide more functionalities in an online news website, including topic-based news reading, intuitive tagging, clipping and sharing of interesting news, as well as news filtering or searching based on named entity tags. The evaluation results on the impact of disambiguating tags have shown that the vocabulary can help to significantly improve news searching performance. The preliminary results from our user study have demonstrated that users can benefit from the additional functionalities on the news websites as they are able to retrieve more relevant news, clip and share news with friends and families effectively.

Capital music : personal expression with a public display of song choice

Using information and communication technology devices in public urban places can help to create a personalised space. Looking at a mobile phone screen or listening to music on an MP3 player is a common practice avoiding direct contact with others e.g. whilst using public transport. However, such devices can also be utilised to explore how to build new meaningful connections with the urban space and the collocated people within. We present findings of work-in-progress on Capital Music, a mobile application enabling urban dwellers to listen to music songs as usual, but also allowing them to announce song titles and discover songs currently being listened to by other people in the vicinity. We study the ways that this tool can change or even enhance people’s experience of public urban spaces. Our first user study also found changes in choosing different songs. Anonymous social interactions based on users’ music selection are implemented in the first iteration of the prototype that we studied.

Remixing social media for location sharing on public urban screens

This paper introduces Sapporo World Window (hereafter SWW), an interactive social media mash-up deployed in a newly built urban public underground space utilising ten public displays and urban dwellers’ mobile phones. SWW enables users to share their favourite locations with fellow citizens and visitors through integrating various social media contents to a coherent whole. The system aims to engage citizens in socio-cultural and technological interactions, turning the underground space into a creative and lively social space. We present first insight from an initial user study in a real world setting.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.

Modeling player-like behaviour for game Al design

The design of artificial intelligence in computer games is an important component of a player's game play experience. As games are becoming more life-like and interactive, the need for more realistic game AI will increase. This is particularly the case with respect to AI that simulates how human players act, behave and make decisions. The purpose of this research is to establish a model of player-like behavior that may be effectively used to inform the design of artificial intelligence to more accurately mimic a player's decision making process. The research uses a qualitative analysis of player opinions and reactions while playing a first person shooter video game, with recordings of their in game actions, speech and facial characteristics. The initial studies provide player data that has been used to design a model of how a player behaves.

Impact of region-of-interest video coding on perceived quality in mobile video

Effective streaming of video can be achieved by providing more bits to the most important region in the frame at the cost of reduced bits in the less important regions. This strategy can be beneficial for delivering high quality videos in mobile devices, especially when the availability of bandwidth is usually low and limited. While the state-of-the-art video codecs such as H.264 may have been optimised for perceived quality, it is hypothesised that users will give more attention to interesting region/object when watching videos. Therefore, giving a higher quality to region of interest (ROI)while reducing quality of other areas may result in improving the overall perceived quality without necessarily increasing the bitrate. In this paper, the impact of ROI-based encoded video on perceived quality is investigated by conducting a user study for varous target bitrates. The results from the user study demonstrate that ROI-based video coding has superior perceived quality compared to normal encoded video at the same bitrate in the lower bitrate range.

Human–computer interaction : the impact of users’ cognitive styles on query reformulation behaviour during web searching

This paper discusses users’ query reformulation behaviour while searching information on the Web. Query reformulations have emerged as an important component of Web search behaviour and human-computer interaction (HCI) because a user’s success of information retrieval (IR) depends on how he or she formulates queries. There are various factors, such as cognitive styles, that influence users’ query reformulation behaviour. Understanding how users with different cognitive styles formulate their queries while performing Web searches can help HCI researchers and information systems (IS) developers to provide assistance to the users. This paper aims to examine the effects of users’ cognitive styles on their query reformation behaviour. To achieve the goal of the study, a user study was conducted in which a total of 3613 search terms and 872 search queries were submitted by 50 users who engaged in 150 scenario-based search tasks. Riding’s (1991) Cognitive Style Analysis (CSA) test was used to assess users’ cognitive style as wholist or analytic, and verbaliser or imager. The study findings show that users’ query reformulation behaviour is affected by their cognitive styles. The results reveal that analytic users tended to prefer Add queries while all other users preferred New queries. A significant difference was found among wholists and analytics in the manner they performed Remove query reformulations. Future HCI researchers and IS developers can utilize the study results to develop interactive and user-cantered search model, and to provide context-based query suggestions for users.

Relationship between the nature of the search task types and query reformulation behaviour

Success of query reformulation and relevant information retrieval depends on many factors, such as users’ prior knowledge, age, gender, and cognitive styles. One of the important factors that affect a user’s query reformulation behaviour is that of the nature of the search tasks. Limited studies have examined the impact of the search task types on query reformulation behaviour while performing Web searches. This paper examines how the nature of the search tasks affects users’ query reformulation behaviour during information searching. The paper reports empirical results from a user study in which 50 participants performed a set of three Web search tasks – exploratory, factorial and abstract. Users’ interactions with search engines were logged by using a monitoring program. 872 unique search queries were classified into five query types – New, Add, Remove, Replace and Repeat. Users submitted fewer queries for the factual task, which accounted for 26%. They completed a higher number of queries (40% of the total queries) while carrying out the exploratory task. A one-way MANOVA test indicated a significant effect of search task types on users’ query reformulation behaviour. In particular, the search task types influenced the manner in which users reformulated the New and Repeat queries.

Spotting the difference : identifying player opponent preferences in FPS games

This paper describes a study designed to understand player responses to artificially intelligent opponents in multi-player First Person Shooter games. It examines the player's ability to tell the difference between artificially intelligent opponents and other human players, and investigates the players' perceptions of these opponents. The study examines player preferences in this regard and identifies the significance of the cues and signs players use to categorise an opponent as artificial or human.

EatChaFood : challenging technology design to slice food waste production

This paper presents work in progress of EatChaFood – a prototype app designed to increase user knowledge of the currently available domestic supply and location of food, with a view to reducing expired household food waste. In order to reap the benefits that EatChaFood can provide we explore ways to overcome manual data entry as a barrier to use. Our user study has to recognise the limitations of the prototype app, and conduct an evaluation of the interaction design built into the app to promote behaviour change. Innovations in the near future such as the automatic scanning of barcodes on food items or photo-recognition will close the gap between perceived prototype usability and usefulness.

Modelling users' contextual querying behaviour for web image searching

The rapid growth of visual information on Web has led to immense interest in multimedia information retrieval (MIR). While advancement in MIR systems has achieved some success in specific domains, particularly the content-based approaches, general Web users still struggle to find the images they want. Despite the success in content-based object recognition or concept extraction, the major problem in current Web image searching remains in the querying process. Since most online users only express their needs in semantic terms or objects, systems that utilize visual features (e.g., color or texture) to search images create a semantic gap which hinders general users from fully expressing their needs. In addition, query-by-example (QBE) retrieval imposes extra obstacles for exploratory search because users may not always have the representative image at hand or in mind when starting a search (i.e. the page zero problem). As a result, the majority of current online image search engines (e.g., Google, Yahoo, and Flickr) still primarily use textual queries to search. The problem with query-based retrieval systems is that they only capture users’ information need in terms of formal queries;; the implicit and abstract parts of users’ information needs are inevitably overlooked. Hence, users often struggle to formulate queries that best represent their needs, and some compromises have to be made. Studies of Web search logs suggest that multimedia searches are more difficult than textual Web searches, and Web image searching is the most difficult compared to video or audio searches. Hence, online users need to put in more effort when searching multimedia contents, especially for image searches. Most interactions in Web image searching occur during query reformulation. While log analysis provides intriguing views on how the majority of users search, their search needs or motivations are ultimately neglected. User studies on image searching have attempted to understand users’ search contexts in terms of users’ background (e.g., knowledge, profession, motivation for search and task types) and the search outcomes (e.g., use of retrieved images, search performance). However, these studies typically focused on particular domains with a selective group of professional users. General users’ Web image searching contexts and behaviors are little understood although they represent the majority of online image searching activities nowadays. We argue that only by understanding Web image users’ contexts can the current Web search engines further improve their usefulness and provide more efficient searches. In order to understand users’ search contexts, a user study was conducted based on university students’ Web image searching in News, Travel, and commercial Product domains. The three search domains were deliberately chosen to reflect image users’ interests in people, time, event, location, and objects. We investigated participants’ Web image searching behavior, with the focus on query reformulation and search strategies. Participants’ search contexts such as their search background, motivation for search, and search outcomes were gathered by questionnaires. The searching activity was recorded with participants’ think aloud data for analyzing significant search patterns. The relationships between participants’ search contexts and corresponding search strategies were discovered by Grounded Theory approach. Our key findings include the following aspects: - Effects of users' interactive intents on query reformulation patterns and search strategies - Effects of task domain on task specificity and task difficulty, as well as on some specific searching behaviors - Effects of searching experience on result expansion strategies A contextual image searching model was constructed based on these findings. The model helped us understand Web image searching from user perspective, and introduced a context-aware searching paradigm for current retrieval systems. A query recommendation tool was also developed to demonstrate how users’ query reformulation contexts can potentially contribute to more efficient searching.