Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Iterative intersections

Iterative Intersectioning is a body of art works that comes out of the collaboration between author and electronic artist Jen Seevinck and a community of print artists, most particularly Elizabeth Saunders (EJ) and Robert Oakman. The work shown here is concerned with the creative process of collaboration, specifically as this informs visual forms. This is through our focus on process. This process has facilitated a 'conversational' exchange between all artists and a corresponding evolution in the artworks. In each case the dialogue is either between the author, Jen and EJ or between Jen and Robert. It consists of passing work between parties, interpreting it and working into it, before passing it back. The result is a series of art works including those shown here. The concept evolves in parallel to this. Importantly, at each of her iterations of creative work, the author Jen determines a similar 'treatment' or 'interpretation' across both print artists works at that time. A synthesis of EJ and Robert's creative interpretation -- at a high level -- occurs. In this sense the concept and works can be understood to intersect with one another.

Protecting web 2.0 services from botnet exploitations

Recently, botnet, a network of compromised computers, has been recognized as the biggest threat to the Internet. The bots in a botnet communicate with the botnet owner via a communication channel called Command and Control (C & C) channel. There are three main C & C channels: Internet Relay Chat (IRC), Peer-to-Peer (P2P) and web-based protocols. By exploiting the flexibility of the Web 2.0 technology, the web-based botnet has reached a new level of sophistication. In August 2009, such botnet was found on Twitter, one of the most popular Web 2.0 services. In this paper, we will describe a new type of botnet that uses Web 2.0 service as a C & C channel and a temporary storage for their stolen information. We will then propose a novel approach to thwart this type of attack. Our method applies a unique identifier of the computer, an encryption algorithm with session keys and a CAPTCHA verification.

UbiOpticon : participatory sousveillance with urban screens and mobile phone cameras

In many cities around the world, surveillance by a pervasive net of CCTV cameras is a common phenomenon in an attempt to uphold safety and security across the urban environment. Video footage is being recorded and stored, sometimes live feeds are being watched in control rooms hidden from public access and view. In this study, we were inspired by Steve Mann’s original work on sousveillance (surveillance from below) to examine how a network of camera equipped urban screens could allow the residents of Oulu in Finland to collaborate on the safekeeping of their city. An agile, rapid prototyping process led to the design, implementation and ‘in the wild’ deployment of the UbiOpticon screen application. Live video streams captured by web cams integrated at the top of 12 distributed urban screens were broadcast and displayed in a matrix arrangement on all screens. The matrix also included live video streams of two roaming mobile phone cameras. In our field study we explored the reactions of passers-by and users of this screen application that seeks to inverse Bentham’s original panopticon by allowing the watched to be watchers at the same time. In addition to the original goal of participatory sousveillance, the system’s live video feature sparked fun and novel user-led apprlopriations.

Vote with your feet : local community polling on urban screens

Falling prices have led to an ongoing spread of public displays in urban areas. Still, they mostly show passive content such as commercials and digital signage. At the same time, technological advances have enabled the creation of interactive displays potentially increasing their attractiveness for the audience, e.g. through providing a platform for civic discourse. This poses considerable challenges, since displays need to communicate the opportunity to engage, motivate the audience to do so, and be easy to use. In this paper we present Vote With Your Feet, a hyperlocal public polling tool for urban screens allowing users to express their opinions. Similar to vox populi interviews on TV or polls on news websites, the tool is meant to reflect the mindset of the community on topics such as current affairs, cultural identity and local matters. It is novel in that it focuses on a situated civic discourse and provides a tangible user interface, tackling the mentioned challenges. It shows one Yes/No question at a time and enables users to vote by stepping on one of two tangible buttons on the ground. This user interface was introduced to attract people’s attention and to lower participation barriers. Our field study showed that Vote With Your Feet is perceived as inviting and that it can spark discussions among co-located people.

Invisible connections : investigating older people's emotions and social relations around objects

The advent of the Internet of Things creates an interest in how people might interrelate through and with networks of internet enabled objects. With an emphasis on fostering social connection and physical activity among older people, this preliminary study investigated objects that people over the age of 65 years viewed as significant to them. We conducted contextual interviews in people's homes about their significant objects in order to understand the role of the objects in their lives, the extent to which they fostered emotional and social connections and physical activity, and how they might be augmented through internet connection. Discussion of significant objects generated considerable emotion in the participants. We identified objects of comfort and routine, objects that exhibited status, those that fostered independence and connection, and those that symbolized relationships with loved ones. These findings lead us to consider implications for the design of interconnected objects.

Evaluating the use of ambient and tangible interaction approaches for personal indoor climate preferences [Extended Abstract]

In this paper we describe the preliminary results of a field study which evaluated the use of MiniOrb, a system that employs ambient and tangible interaction mechanisms to allow inhabitants of office environments to report on subjectively perceived office comfort levels. The purpose of this study was to explore the role of ubiquitous computing in the individual control of indoor climate and specifically answer the question to what extent ambient and tangible interaction mechanisms are suited for the task of capturing individual comfort preferences in a non-obtrusive manner. We outline the preliminary results of an in-situ trial of the system.

A rating aggregation method for generating product reputations

Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items. Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms state-of-the-art methods over different sparsity datasets.

Exploring the effect of achievements on students attending university orientation

University orientation is a key event for new students that aids in the transition from a school to a university environment. A smartphone orientation application was built to aid students attending the event. Achievements were added to the application in an attempt to engage students further with the orientation activities and application. An exploratory field study was undertaken to evaluate the effect of the achievement system on participants attending orientation. Forty-six new students were recruited to test the orientation application. Twenty-six participants used a gamified version of the orientation application and twenty participants used a non-gamified version. While the gamification was generally well received, no impact on user experience was evident. Some effect on engagement with orientation activities was shown. Participants who used the gamified system reported the game elements as fun, but some negative issues arose, such as cheating.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming