Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

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.

Effectiveness of pilot car operations in reducing speeds in a long-term rural highway work zone

Pilot cars are used in one-lane two-way work zones to guide traffic and keep their speeds within posted limits. While many studies have examined the effectiveness of measures to reduce vehicle speeds in work zones, little is known about the reductions achievable through the use of pilot cars. This paper examines the effectiveness of a pilot car in reducing travel speeds in a rural highway work zone in Queensland, Australia. Analysis of speed data covering a period of five days showed that a pilot car reduced average speeds at the treatment location, but not downstream. The proportion of vehicles speeding through the activity area was also reduced, particularly those traveling at 10 km/h or more above the posted limit. Motorists were more likely to speed during the day, under a 40 kh/h limit, when traffic volumes were higher and when there were fewer vehicles in the traffic stream. Medium vehicles were less likely to speed in the presence of a pilot car than light vehicles. To maximize these benefits, it is necessary to ensure that the pilot car itself is not speeding.

The development of a carrying capacity assessment model for the Australian socio-environmental context

A key aim of this research was to highlight how society's understanding of constraints to the productive capacity of its resource base is vital to its long-term survival. This was achieved through the development of an online model, the Carrying Capacity Dashboard. The Dashboard was developed to estimate how much land Australian populations require for the production of their food, textiles, timber and liquid fuel. Findings reveal that Australia's estimated carrying capacity is currently over 40 million people but longer-term and more regional analyses suggest a much smaller number. Carrying capacity assessment also indicates that optimal resource security is to be found in balancing both small and large-scale self-sufficiency.

Simulating cooperative systems applications : a new complete architecture

For a decade, embedded driving assistance systems were mainly dedicated to the management of short time events (lane departure, collision avoidance, collision mitigation). Recently a great number of projects have been focused on cooperative embedded devices in order to extend environment perception. Handling an extended perception range is important in order to provide enough information for both path planning and co-pilot algorithms which need to anticipate events. To carry out such applications, simulation has been widely used. Simulation is efficient to estimate the benefits of Cooperative Systems (CS) based on Inter-Vehicular Communications (IVC). This paper presents a new and modular architecture built with the SiVIC simulator and the RTMaps™ multi-sensors prototyping platform. A set of improvements, implemented in SiVIC, are introduced in order to take into account IVC modelling and vehicles’ control. These 2 aspects have been tuned with on-road measurements to improve the realism of the scenarios. The results obtained from a freeway emergency braking scenario are discussed.

Pre-service teachers’ epistemic perspectives about philosophy in the classroom : it is not a bunch of ‘hippie stuff’

Using epistemic perspectives as a theoretical framework, this study investigated Australian pre-service teachers’ perspectives about knowing, knowledge and children’s learning, as they engaged in a semester-long unit on philosophy in the classroom. During the field experience component of the unit, pre-service teachers were required to teach at least one philosophy lesson. Pre-service teachers completed the Personal Epistemological Beliefs Survey at the beginning and end of the unit. They were also interviewed in focus groups at the end of the semester to investigate their views about children’s learning. Paired sample t-tests were used to explore changes in epistemic beliefs over time. Significant differences were found for only some individual items on the survey. However, when interviewed, pre-service teachers indicated that field experiences helped them consider children as competent ‘thinkers’ who were capable of engaging in philosophy in the classroom. They reported predominantly student-centred perspectives of children’s learning, although a process of adjudication (exploring disagreements and evidence for responses) was lacking in these responses.

Design of an integrated platform for V2X wireless communications and positioning supporting C-ITS safety applications

In this paper, an integrated inter-vehicles wireless communications and positioning system supporting alternate positioning techniques is proposed to meet the requirements of safety applications of Cooperative Intelligent Transportation Systems (C-ITS). Recent advances have repeatedly demonstrated that road safety problems can be to a large extent addressed via a range of technologies including wireless communications and positioning in vehicular environments. The novel communication stack utilizing a dedicated frequency spectrum (e.g. at 5.9 GHz band), known as Dedicated Short-Range Communications (DSRC), has been particularly designed for Wireless Access in Vehicular Environments (WAVE) to support safety applications in highly dynamic environments. Global Navigation Satellite Systems (GNSS) is another essential enabler to support safety on rail and roads. Although current vehicle navigation systems such as single frequency Global Positioning System (GPS) receivers can provide route guidance with 5-10 meters (road-level) position accuracy, positioning systems utilized in C-ITS must provide position solutions with lane-level and even in-lane-level accuracies based on the requirements of safety applications. This article reviews the issues and technical approaches that are involved in designing a vehicular safety communications and positioning architecture; it also provides technological solutions to further improve vehicular safety by integrating the DSRC and GNSS-based positioning technologies.

Aesthetics of mundane interactions

John Dewey’s pragmatist aesthetics is used as a conceptual basis for designing new technologies that support staff-members’ mundane social interactions in an academic department. From this perspective, aesthetics is seen as a broader phenomenon that encompasses experiential aspects of staffmembers’ everyday lives and not only a look-&-feel aspect.

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.

Benefit of accident reduction considering the improvement of travel time reliability

Traffic accidents often cause lane closure, and diminish stability of travel time as well as the level of road services. On the other hand, research on the implementation of ITS services aiming at the reduction of traffic accidents has made considerable progress lately. However there has been little discussion on the benefits obtained by traffic accident reduction from the view point of travel time reliability. Therefore, in this research, relationships between traffic accidents and travel time reliability are examined, and the benefit of traffic accident reduction is calculated based on the scheduling model under travel time uncertainties. The results show the significance of traffic accident reduction for the improvement of travel time reliability.

Pragmatic two-way communication : a practitioner perspective on dialogue in public relations

Public relations has traditionally claimed a close association with dialogue, but this research demonstrates that formal notions of dialogue have little relevance to the real world of public relations practice. Instead, practitioners undertake pragmatic forms of two-way communication, because the constraints within which they work mean dialogue is difficult if not impossible to carry out. This qualitative research project shows that although the label of 'dialogue' has been co-opted in both the theory and practice of public relations, this claimed connection is not supported by empirical evidence.