Reconceiving the good life : the key to sustainable globalisation

Resource-intensive, high-carbon, Western lifestyles are frequently criticised as unsustainable and deeply unsatisfying. However, these lifestyles are still attractive to the majority of Westerners and to a high proportion of the developing world’s middle classes. This paper argues that the imminent threat of catastrophic climate change constitutes an immediate political, economic and ethical challenge for citizens of the developed world that cannot be tackled by appeals to asceticism or restraint. There can be no solution to climate change until sustainable conceptions of the good life are developed that those in the west want to live and which others might want to live. While the ultimate solution to climate change is the development of low carbon lifestyles, it is important that government initiatives, governance arrangements and economic incentives support rather than undermine that search. Like the global financial crisis, the climate change crisis also demonstrates what happens when weaknesses in national, corporate and professional governance are exacerbated by weaknesses in global governance. In tackling the latter, it is critical the mistakes now evidenced in the former are avoided – including a rethinking of carbon market and carbon tax alternatives. It is also critical that individuals must take responsibility for their actions as consumers, voters and investors.

Multi-modal summarization of key events and top players in sports tournament videos

To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information. Previous work has successfully extracted semantic from the time-stamped web match reports, which are synchronized with the video contents. However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments. This paper aims to address this limitation using a novel multimodal summarization framework that is based on sentiment analysis and players' popularity. It uses audiovisual contents, web articles, blogs, and commentators' speech to automatically annotate and visualize the key events and key players in a sports tournament coverage. The experimental results demonstrate that the automatically generated video summaries are aligned with the events identified from the official website match reports.

A circuit-breaker restrike diagnostic algorithm using ATP and wavelet transforms

Circuit breaker restrikes are unwanted occurrence, which can ultimately lead to breaker. Before 2008, there was little evidence in the literature of monitoring techniques based on restrike measurement and interpretation produced during switching of capacitor banks and shunt reactor banks. In 2008 a non-intrusive radiometric restrike measurement method, as well a restrike hardware detection algorithm was developed. The limitations of the radiometric measurement method are a band limited frequency response as well as limitations in amplitude determination. Current detection methods and algorithms required the use of wide bandwidth current transformers and voltage dividers. A novel non-intrusive restrike diagnostic algorithm using ATP (Alternative Transient Program) and wavelet transforms is proposed. Wavelet transforms are the most common use in signal processing, which is divided into two tests, i.e. restrike detection and energy level based on deteriorated waveforms in different types of restrike. A ‘db5’ wavelet was selected in the tests as it gave a 97% correct diagnostic rate evaluated using a database of diagnostic signatures. This was also tested using restrike waveforms simulated under different network parameters which gave a 92% correct diagnostic responses. The diagnostic technique and methodology developed in this research can be applied to any power monitoring system with slight modification for restrike detection.

An efficient public key management system : an application in vehicular ad hoc networks

The major purpose of Vehicular Ad Hoc Networks (VANETs) is to provide safety-related message access for motorists to react or make a life-critical decision for road safety enhancement. Accessing safety-related information through the use of VANET communications, therefore, must be protected, as motorists may make critical decisions in response to emergency situations in VANETs. If introducing security services into VANETs causes considerable transmission latency or processing delays, this would defeat the purpose of using VANETs to improve road safety. Current research in secure messaging for VANETs appears to focus on employing certificate-based Public Key Cryptosystem (PKC) to support security. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This paper proposes an efficient public key management system for VANETs: the Public Key Registry (PKR) system. Not only does this paper demonstrate that the proposed PKR system can maintain security, but it also asserts that it can improve overall performance and scalability at a lower cost, compared to the certificate-based PKC scheme. It is believed that the proposed PKR system will create a new dimension to the key management and verification services for VANETs.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.