Multi-topic information filtering with a single user profile

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

A dynamic feature information model for integrated manufacturing planning and optimization

This paper presents a new, dynamic feature representation method for high value parts consisting of complex and intersecting features. The method first extracts features from the CAD model of a complex part. Then the dynamic status of each feature is established between various operations to be carried out during the whole manufacturing process. Each manufacturing and verification operation can be planned and optimized using the real conditions of a feature, thus enhancing accuracy, traceability and process control. The dynamic feature representation is complementary to the design models used as underlining basis in current CAD/CAM and decision support systems. © 2012 CIRP.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.

Mathematical modelling and optimal multivariable control of chemical processes

This work reports the developnent of a mathenatical model and distributed, multi variable computer-control for a pilot plant double-effect climbing-film evaporator. A distributed-parameter model of the plant has been developed and the time-domain model transformed into the Laplace domain. The model has been further transformed into an integral domain conforming to an algebraic ring of polynomials, to eliminate the transcendental terms which arise in the Laplace domain due to the distributed nature of the plant model. This has made possible the application of linear control theories to a set of linear-partial differential equations. The models obtained have well tracked the experimental results of the plant. A distributed-computer network has been interfaced with the plant to implement digital controllers in a hierarchical structure. A modern rnultivariable Wiener-Hopf controller has been applled to the plant model. The application has revealed a limitation condition that the plant matrix should be positive-definite along the infinite frequency axis. A new multi variable control theory has emerged fram this study, which avoids the above limitation. The controller has the structure of the modern Wiener-Hopf controller, but with a unique feature enabling a designer to specify the closed-loop poles in advance and to shape the sensitivity matrix as required. In this way, the method treats directly the interaction problems found in the chemical processes with good tracking and regulation performances. Though the ability of the analytical design methods to determine once and for all whether a given set of specifications can be met is one of its chief advantages over the conventional trial-and-error design procedures. However, one disadvantage that offsets to some degree the enormous advantages is the relatively complicated algebra that must be employed in working out all but the simplest problem. Mathematical algorithms and computer software have been developed to treat some of the mathematical operations defined over the integral domain, such as matrix fraction description, spectral factorization, the Bezout identity, and the general manipulation of polynomial matrices. Hence, the design problems of Wiener-Hopf type of controllers and other similar algebraic design methods can be easily solved.

WSRF-based modeling of clinical trial information for collaborative cancer research

The CancerGrid consortium is developing open-standards cancer informatics to address the challenges posed by modern cancer clinical trials. This paper presents the service-oriented software paradigm implemented in CancerGrid to derive clinical trial information management systems for collaborative cancer research across multiple institutions. Our proposal is founded on a combination of a clinical trial (meta)model and WSRF (Web Services Resource Framework), and is currently being evaluated for use in early phase trials. Although primarily targeted at cancer research, our approach is readily applicable to other areas for which a similar information model is available.

An integrated view of data quality in earth observation

Data quality is a difficult notion to define precisely, and different communities have different views and understandings of the subject. This causes confusion, a lack of harmonization of data across communities and omission of vital quality information. For some existing data infrastructures, data quality standards cannot address the problem adequately and cannot full all user needs or cover all concepts of data quality. In this paper we discuss some philosophical issues on data quality. We identify actual user needs on data quality, review existing standards and specification on data quality, and propose an integrated model for data quality in the eld of Earth observation. We also propose a practical mechanism for applying the integrated quality information model to large number of datasets through metadata inheritance. While our data quality management approach is in the domain of Earth observation, we believe the ideas and methodologies for data quality management can be applied to wider domains and disciplines to facilitate quality-enabled scientific research.

A comparative study of bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection.

Early design verification of complex assembly variability using a hybrid - model based and physical testing - methodology

Design verification in the digital domain, using model-based principles, is a key research objective to address the industrial requirement for reduced physical testing and prototyping. For complex assemblies, the verification of design and the associated production methods is currently fragmented, prolonged and sub-optimal, as it uses digital and physical verification stages that are deployed in a sequential manner using multiple systems. This paper describes a novel, hybrid design verification methodology that integrates model-based variability analysis with measurement data of assemblies, in order to reduce simulation uncertainty and allow early design verification from the perspective of satisfying key assembly criteria.

IT support for managing aircraft spares in a closed-loop supply chain

This paper reports on the development of elements of an e-supply chain management system for managing maintenance, repair and overhaul (MRO) relationships in the aerospace industry. A standard systems development methodology has been followed to produce a process model (i.e. the AMSCR model); an information model (i.e. business rules) and a computerised information management capability (i.e. automated optimisation). The proof of concept for this web-based MRO supply chain system has been established through the collaboration with a sample of the different types of supply chain members. The proven benefit is a reduction in the stock-holding costs for the whole supply chain whilst also minimising non-flying time of the aircraft that the supply chain supports. This type of system is now vital in an industry that has continuously decreasing profit margins, which in turn means pressure to reduce servicing times and increase the interval between maintenance actions.

Designing a support system for aerospace maintenance supply chains

Purpose - To develop a systems strategy for supply chain management in aerospace maintenance, repair and overhaul (MRO). Design/methodology/approach - A standard systems development methodology has been followed to produce a process model (i.e. the AMSCR model); an information model (i.e. business rules) and a computerised information management capability (i.e. automated optimisation). Findings - The proof of concept for this web-based MRO supply chain system has been established through collaboration with a sample of the different types of supply chain members. The proven benefits comprise new potential to minimise the stock holding costs of the whole supply chain whilst also minimising non-flying time of the aircraft that the supply chain supports. Research limitations/implications - The scale of change needed to successfully model and automate the supply chain is vast. This research is a limited-scale experiment intended to show the power of process analysis and automation, coupled with strategic use of management science techniques, to derive tangible business benefit. Practical implications - This type of system is now vital in an industry that has continuously decreasing profit margins; which in turn means pressure to reduce servicing times and increase the mean time between them. Originality/value - Original work has been conducted at several levels: process, information and automation. The proof-of-concept system has been applied to an aircraft MRO supply chain. This is an area of research that has been neglected, and as a result is not well served by current systems solutions. © Emerald Group Publishing Limited.

Effect of mobility on violence in a bi-communal population

We develop a multi-agent based model to simulate a population which comprises of two ethnic groups and a peacekeeping force. We investigate the effects of different strategies for civilian movement to the resulting violence in this bi-communal population. Specifically, we compare and contrast random and race-based migration strategies. Race-based migration leads the formation of clusters. Previous work in this area has shown that same-race clustering instigates violent behavior in otherwise passive segments of the population. Our findings confirm this. Furthermore, we show that in settings where only one of the two races adopts race-based migration it is a winning strategy especially in violently predisposed populations. On the other hand, in relatively peaceful settings clustering is a restricting factor which causes the race that adopts it to drift into annihilation. Finally, we show that when race-based migration is adopted as a strategy by both ethnic groups it results in peaceful co-existence even in the most violently predisposed populations.

An empirical investigation of the effect of rights issue announcements on share returns and the determinants of abnormal returns on the Kuala Lumpur Stock Exchange

This thesis examines the effect of rights issue announcements on stock prices by companies listed on the Kuala Lumpur Stock Exchange (KLSE) between 1987 to 1996. The emphasis is to report whether the KLSE is semi strongly efficient with respect to the announcement of rights issues and to check whether the implications of corporate finance theories on the effect of an event can be supported in the context of an emerging market. Once the effect is established, potential determinants of abnormal returns identified by previous empirical work and corporate financial theory are analysed. By examining 70 companies making clean rights issue announcements, this thesis will hopefully shed light on some important issues in long term corporate financing. Event study analysis is used to check on the efficiency of the Malaysian stock market; while cross-sectional regression analysis is executed to identify possible explanators of the rights issue announcements' effect. To ensure the results presented are not contaminated, econometric and statistical issues raised in both analyses have been taken into account. Given the small amount of empirical research conducted in this part of the world, the results of this study will hopefully be of use to investors, security analysts, corporate financial managements, regulators and policy makers as well as those who are interested in capital market based research of an emerging market. It is found that the Malaysian stock market is not semi strongly efficient since there exists a persistent non-zero abnormal return. This finding is not consistent with the hypothesis that security returns adjust rapidly to reflect new information. It may be possible that the result is influenced by the sample, consisting mainly of below average size companies which tend to be thinly traded. Nevertheless, these issues have been addressed. Another important issue which has emerged from the study is that there is some evidence to suggest that insider trading activity existed in this market. In addition to these findings, when the rights issue announcements' effect is compared to the implications of corporate finance theories in predicting the sign of abnormal returns, the signalling model, asymmetric information model, perfect substitution hypothesis and Scholes' information hypothesis cannot be supported.