Vincent C. S. Lee

A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter

The study of terrorism informatics utilizing the Twitter microblogging service has not been given apt attention in the past few years. Twitter has been identified as both a potential facilitator and also a powerful deterrent to terrorism. Based on observations of Twitter’s role in civilian response during the recent 2009 Jakarta and Mumbai terrorist attacks, we propose a structured framework to harvest civilian sentiment and response on Twitter during terrorism scenarios. Coupled with intelligent data mining, visualization, and filtering methods, this data can be collated into a knowledge base that would be of great utility to decision-makers and the authorities for rapid response and monitoring during such scenarios. Using synthetic experimental data, we demonstrated that the proposed framework has yielded meaningful graphical visualizations of information, to reveal potential response to terrorist threats. The novelty of this study is that microblogging has never been studied in the domain of terrorism informatics. This paper also contributes to the understanding of the capability of conjoint structured data and unstructured content mining in extracting deep knowledge from noisy twitter messages, through our proposed structured framework.

Exception rules in association rule mining.

Previously, exception rules have been defined as association rules with low support and high confidence. Exception rules are important in data mining, as they form rules that can be categorized as an exception. This is the opposite of general association rules in data mining, which focus on high support and high confidence. In this paper, a new approach to mining exception rules is proposed and evaluated. A relationship between exception and positive/negative association rules is considered, whereby the candidate exception rules are generated based on knowledge of the positive and negative association rules in the database. As a result, the exception rules exist in the form of negative, as well as positive, association. A novel exceptionality measure is proposed to evaluate the candidate exception rules. The candidate exceptions with high exceptionality form the final set of exception rules. Algorithms for mining exception rules are developed and evaluated using an exceptionality measurement, the desired performance of which has been proven.

A new multi-agent system framework for tacit knowledge management in manufacturing supply chains

Participating members in a manufacturing supply chain (MSC) usually make use of individual knowledge for making independent decisions. Recent research, however, indicates that there is a need to handle such distributed knowledge in an integrated manner, especially under uncertain and fast changing environments. A multiagent system (MAS), a branch of distributed artificial intelligence, is a contemporary modelling technique for a distributed system like MSCs in the manufacturing domain. However recent researches indicate that MAS approaches have not adequately addressed the role of sharing tacit knowledge (TK) on MSC performance. This paper, therefore, aims to propose a framework that utilizes MAS techniques with a corresponding TK sharing mechanism dedicated to MSCs. We performed some experiments to simulate the proposed approach. The results showed significant improvements when comparing the proposed approach with another conventional MAS model. The results establish a starting point for researchers interested in enhancing MSC performance using TK management approach, and for managers of MSC to focus on the essentials of sharing TK.

Privacy-Preserving Gradient-Descent Methods

Gradient descent is a widely used paradigm for solving many optimization problems. Gradient descent aims to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. In this paper, we propose a preliminary formulation of gradient descent with data privacy preservation. We present two approaches-stochastic approach and least square approach-under different assumptions. Four protocols are proposed for the two approaches incorporating various secure building blocks for both horizontally and vertically partitioned data. We conduct experiments to evaluate the scalability of the proposed secure building blocks and the accuracy and efficiency of the protocols for four different scenarios. The excremental results show that the proposed secure building blocks are reasonably scalable and the proposed protocols allow us to determine a better secure protocol for the applications for each scenario.

Resilient Identity Crime Detection

Identity crime is well known, prevalent, and costly; and credit application fraud is a specific case of identity crime. The existing nondata mining detection system of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real time, this paper proposes a new multilayered detection system complemented with two additional layers: communal detection (CD) and spike detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behavior, and remove the redundant attributes. Experiments were carried out on CD and SD with several million real credit applications. Results on the data support the hypothesis that successful credit application fraud patterns are sudden and exhibit sharp spikes in duplicates. Although this research is specific to credit application fraud detection, the concept of resilience, together with adaptivity and quality data discussed in the paper, are general to the design, implementation, and evaluation of all detection systems.

Twittering for earth: a study on the impact of microblogging activism on earth hour 2009 in Australia

The role of Twitter - a form of microblogging - as both influencer and reflector of real-world events is fast emerging in todays world of Web 2.0 and social media. In this investigation, we survey how the use of Twitter in Australia is linked to the real-world success of the Earth Hour 2009 campaign. The results of this research will give us an idea of the emergence of microblogging as a new medium of influencing human behavior and providing a source of collective intelligence in planning and decision making, specifically in the Australian context. We found that, from our observations, there is a correlation between the inter-state total energy reduction during this campaign with the amount of interstate online Twitter discussion. We also identified a link between the Twitter discussion frequency and the total real-life population of the locale in which the chatter takes place, which could be used as a yardstick to analyze the reach of online technologies in the real world.

Privacy-preservation for gradient descent methods

Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.

A Study on Detecting Patterns in Twitter Intra-topic User and Message Clustering

Timely detection of hidden patterns is the key for the analysis and estimating of driving determinants for mission critical decision making. This study applies Cheong and Lee’s “context-aware” content analysis framework to extract latent properties from Twitter messages (tweets). In addition, we incorporate an unsupervised Self-organizing Feature Map (SOM) as a machine learning-based clustering tool that has not been investigated in the context of opinion mining and sentimental analysis using microblogging. Our experimental results reveal the detection of interesting patterns for topics of interest which are latent and cannot be easily detected from the observed tweets without the aid of machine learning tools.

An empirical study of Genetic Programming generated trading rules in computerized stock trading service system

Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. The application of genetic programming (GP) as a means to automatically generate such trading rules on the stock markets has been studied. Computational results, based on historical pricing and transaction volume data, are reported for the thirty component stocks of the Dow Jones Industrial Average index. Statistical evidence shows that for the stocks that were studied, the use of GP based trading rules ensures a positive dollar return in all market scenarios. The performance of the GP based trading rules was also evaluated against the performance of the popularly used MACD technical indicator. In general, GP based trading rules offer greater returns over the simple buy and hold approach than the MACD trading signal.

A multivariate neuro-fuzzy system for foreign currency risk management decision making

Currency risk management decision involves deciding on when, how much and what hedging instrument (i.e., currency futures or options) should be used to hedge its risk exposure with the base currency. Intuitively the accuracy in forecasting the direction and magnitude of future exchange rate movements is central to currency risk management decision-making process. This research investigates the predictive performance of a hybrid multivariate model, using multiple macroeconomic and microstructure of foreign exchange market variables. Conceptually, the proposed system combines and exploits the merit of adaptive learning artificial neural network (ANN) and intuitive reasoning (fuzzy-logic inference) tools. An ANN is employed to forecast a foreign exchange rate movement which is followed by the intuitive reasoning of multi-period foreign currency returns using multi-value fuzzy logic for foreign currency risk management decision-making. Empirical tests with statistical and machine learning criteria reveal plausible performance of its predictive capability.