Can supervise: YES
Hao, Q, Jia, G, Wei, W, Vinu, A, Wang, Y, Arandiyan, H & Ni, BJ 2020, 'Graphitic carbon nitride with different dimensionalities for energy and environmental applications', Nano Research, vol. 13, no. 1, pp. 18-37.View/Download from: UTS OPUS or Publisher's site
© 2019, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature. As a metal-free semiconductor, graphitic carbon nitride (g-C3N4) has received extensive attention due to its high stability, nontoxicity, facile and low-cost synthesis, appropriate band gap in the visible spectral range and wide availability of resources. The dimensions of g-C3N4 can influence the regime of the confinement of electrons, and consequently, g-C3N4 with various dimensionalities shows different properties, making them available for many stimulating applications. Although there are some reviews focusing on the synthesis strategy and applications of g-C3N4, there is still a lack of comprehensive review that systemically summarises the synthesis and application of different dimensions of g-C3N4, which can provide an important theoretical and practical basis for the development of g-C3N4 with different dimensionalities and maximises their potential in diverse applications. By reviewing the latest progress of g-C3N4 studies, we aim to summarise the preparation of g-C3N4 with different dimensionalities using various structural engineering strategies, discuss the fundamental bottlenecks of currently existing methods and their solution strategies, and explore their applications in energy and environmental applications. Furthermore, it also puts forward the views on the future research direction of these unique materials. [Figure not available: see fulltext.].
Wu, L, Peng, L, Wei, W, Wang, D & Ni, BJ 2020, 'Nitrous oxide production from wastewater treatment: The potential as energy resource rather than potent greenhouse gas', Journal of Hazardous Materials, vol. 387.View/Download from: Publisher's site
© 2019 Elsevier B.V. Nitrous oxide (N2O), produced from wastewater treatment, is a potent greenhouse gas and has become a global concern in recent years. However, N2O has also been commonly used as a powerful oxidant for energy generation. As such, an increasing effort has been devoted to explore the energy potential of N2O from wastewater treatment processes recently. Nevertheless, the holistic knowledge on energy recovery from nitrogen in wastewater is still lacking for facilitating its further development. Striving for sustainable wastewater treatment, this review paper aimed to give the up-to-date status on several essential aspects regarding the N2O recovery as an energy resource rather than emission as a greenhouse gas, including energy production via N2O decomposition, main biotic N2O production sources, the potential bioprocesses used for N2O recovery, and the possible N2O harvesting strategies. We then put forward perspectives for N2O recovery and future challenges to improve our understanding of the energy generation, microbial processes involved and harvesting approaches in order to potentially achieve sustainable wastewater treatment via N2O recovery.
Zheng, Z, Wei, W, Liu, C, Cao, W, Cao, L & Bhatia, M 2016, 'An effective contrast sequential pattern mining approach to taxpayer behavior analysis', World Wide Web, vol. 19, no. 4, pp. 633-651.View/Download from: UTS OPUS or Publisher's site
Data mining for client behavior analysis has become increasingly important in business, however further analysis on transactions and sequential behaviors would be of even greater value, especially in the financial service industry, such as banking and insurance, government and so on. In a real-world business application of taxation debt collection, in order to understand the internal relationship between taxpayers’ sequential behaviors (payment, lodgment and actions) and compliance to their debt, we need to find the contrast sequential behavior patterns between compliant and non-compliant taxpayers. Contrast Patterns (CP) are defined as the itemsets showing the difference/discrimination between two classes/datasets (Dong and Li, 1999). However, the existing CP mining methods which can only mine itemset patterns, are not suitable for mining sequential patterns, such as time-ordered transactions in taxpayer sequential behaviors. Little work has been conducted on Contrast Sequential Pattern (CSP) mining so far. Therefore, to address this issue, we develop a CSP mining approach, e C S P, by using an effective CSP-tree structure, which improves the PrefixSpan tree (Pei et al., 2001) for mining contrast patterns. We propose some heuristics and interestingness filtering criteria, and integrate them into the CSP-tree seamlessly to reduce the search space and to find business-interesting patterns as well. The performance of the proposed approach is evaluated on three real-world datasets. In addition, we use a case study to show how to implement the approach to analyse taxpayer behaviour. The results show a very promising performance and convincing business value.
Li, J, Wang, C, Wei, W, Li, M & Liu, C 2013, 'Efficient mining of contrast patterns on large scale imbalanced real-life data', Lecture Notes in Computer Science, vol. 7818, no. 1, pp. 62-73.View/Download from: UTS OPUS or Publisher's site
Contrast pattern mining has been studied intensively for its strong discriminative capability. However, the state-of-the-art methods rarely consider the class imbalanced problem, which has been proved to be a big challenge in mining large scale data. This paper introduces a novel pattern, i.e. converging pattern, which refers to the itemsets whose supports contrast sharply from the minority class to the majority one. A novel algorithm, ConvergMiner, which adopts T*-tree and branch bound pruning strategies to mine converging patterns efficiently, is proposed. Substantial experiments in online banking fraud detection show that the ConvergMiner greatly outperforms the existing cost-sensitive classification methods in terms of predicative accuracy. In particular, the efficiency improves with the increase of data imbalance.
Wei, W, Li, J, Cao, L, Ou, Y & Chen, J 2013, 'Effective Detection of Sophisticated Online Banking Fraud in Extremely Imbalanced Data', World Wide Web, vol. 16, no. 4, pp. 449-475.View/Download from: UTS OPUS or Publisher's site
Sophisticated online banking fraud reflects the integrative abuse of resources in social, cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology. However, there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment, which makes the instant and effective detection become more and more important and challenging. In this paper, we propose an effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques. By building a contrast vector for each transaction based on its customerâs historical behavior sequence, we profile the differentiating rate of each current transaction against the customerâs behavior preference. A novel algorithm, ContrastMiner, is introduced to efficiently mine contrast patterns and distinguish fraudulent from genuine behavior, followed by an effective pattern selection and risk scoring that combines predictions from different models. Results from experiments on large-scale real online banking data demonstrate that our system can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods.
Xu, J, Wei, W & Cao, L 2017, 'Copula-based high dimensional cross-market dependence modeling', Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, IEEE International Conference on Data Science and Advanced Analytics, IEEE, Tokyo, Japan, pp. 734-743.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Dependence across multiple financial markets, such as stock and foreign exchange rate markets, is high-dimensional, contains various relationships, and often presents complicated dependence structures and characteristics such as asymmetrical dependence. Modeling such dependence structures is very challenging. Although copula has been demonstrated to be effective in describing dependence between variables in recent studies, building effective dependence structures to address the above complexities significantly challenges existing copula models. In this paper, we propose a new D vine-based model with a bottom-up strategy to construct high-dimensional dependence structures. The new modeling outcomes are applied to trade 15 stock market indices and 10 currency rates over 16 years as a case study. Extensive experimental results show that this model and its intrinsic design significantly outperform typical models and industry baselines, as shown by the log-likelihood and Vuong test, and Value at Risk - a widely used industrial benchmark. Our model provides interpretable knowledge and profound insights into the high-dimensional dependence structures across data sources.
Wei, W, Yin, J, Li, J & Cao, L 2014, 'Modelling Asymmetry and Tail Dependence among Multiple Variables by Using Partial Regular Vine', Proceedings of the 2014 SIAM International Conference on Data Mining, SIAM International Conference on Data Mining, SIAM, Philadelphia, USA, pp. 776-784.View/Download from: UTS OPUS or Publisher's site
Modeling high-dimensional dependence is widely studied to explore deep relations in multiple variables particularly useful for financial risk assessment. Very often, strong restrictions are applied on a dependence structure by existing high-dimensional dependence models. These restrictions disabled the detection of sophisticated structures such as asymmetry, upper and lower tail dependence between multiple variables. The paper proposes a partial regular vine copula model to relax these restrictions. The new model employs partial correlation to construct the regular vine structure, which is algebraically independent. This model is also able to capture the asymmetric characteristics among multiple variables by using two-parametric copula with flexible lower and upper tail dependence. Our method is tested on a cross-country stock market data set to analyse the asymmetry and tail dependence. The high prediction performance is examined by the Value at Risk, which is a commonly adopted evaluation measure in financial market.
Wei, W, Li, J, Cao, L, Sun, J, Liu, C & Li, M 2013, 'Optimal Allocation of High Dimensional Assets through Canonical Vines', Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Gold Coast, Australia, pp. 366-377.View/Download from: UTS OPUS or Publisher's site
Canonical Vine, Mean Variance Criterion, Financial Return.
Yin, J, Zheng, Z, Cao, L, Song, Y & Wei, W 2013, 'Efficiently Mining Top-K High Utility Sequential Patterns', 2013 IEEE 13th International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE, Dallas, TX, USA, pp. 1259-1264.View/Download from: UTS OPUS or Publisher's site
High utility sequential pattern mining is an emerging topic in the data mining community. Compared to the classic frequent sequence mining, the utility framework provides more informative and actionable knowledge since the utility of a sequence indicates business value and impact. However, the introduction of "utility" makes the problem fundamentally different from the frequency-based pattern mining framework and brings about dramatic challenges. Although the existing high utility sequential pattern mining algorithms can discover all the patterns satisfying a given minimum utility, it is often difficult for users to set a proper minimum utility. A too small value may produce thousands of patterns, whereas a too big one may lead to no findings. In this paper, we propose a novel framework called top-k high utility sequential pattern mining to tackle this critical problem. Accordingly, an efficient algorithm, Top-k high Utility Sequence (TUS for short) mining, is designed to identify top-k high utility sequential patterns without minimum utility. In addition, three effective features are introduced to handle the efficiency problem, including two strategies for raising the threshold and one pruning for filtering unpromising items. Our experiments are conducted on both synthetic and real datasets. The results show that TUS incorporating the efficiency-enhanced strategies demonstrates impressive performance without missing any high utility sequential patterns
Wei, W, Fan, X, Li, J & Cao, L 2012, 'Model the Complex Dependence Structures of Financial Variables by Using Canonical Vine', The 21st ACM International Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, Springer, Maui, Hawaii, USA, pp. 1382-1391.View/Download from: UTS OPUS or Publisher's site
Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships forming complicated dependence structures. Modeling and mining of these structures is challenging due to their own high structural complexities as well as the stylized facts of the market data. This paper introduces a new canonical vine dependence model to identify the asymmetric and non-linear dependence structures of asset returns without any prior independence assumptions. To simplify the model while maintaining its merit, a partial correlation based method is proposed to optimize the canonical vine. Compared with the original canonical vine, the new model can still maintain the most important dependence but many unimportant nodes are removed to simplify the canonical vine structure. Our model is applied to construct and analyze dependence structures of European stocks as case studies. Its performance is evaluated by measuring portfolio of Value at Risk, a widely used risk management measure. In comparison to a very recent canonical vine model and the `full' model, our experimental results demonstrate that our model has a much better quality of Value at Risk, providing insightful knowledge for investors to control and reduce the aggregation risk of the portfolio.