Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

Which procurement system? Towards a universal procurement selection technique

Two approaches are described, which aid the selection of the most appropriate procurement arrangements for a building project. The first is a multi-attribute technique based on the National Economic Development Office procurement path decision chart. A small study is described in which the utility factors involved were weighted by averaging the scores of five 'experts' for three hypothetical building projects. A concordance analysis is used to provide some evidence of any abnormal data sources. When applied to the study data, one of the experts was seen to be atypical. The second approach is by means of discriminant analysis. This was found to provide reasonably consistent predictions through three discriminant functions. The analysis also showed the quality criteria to have no significant impact on the decision process. Both approaches provided identical and intuitively correct answers in the study described. Some concluding remarks are made on the potential of discriminant analysis for future research and development in procurement selection techniques.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Universal active and reactive power control of electronically interfaced distributed generation sources in virtual power plants operating in gridconnected and islanding modes

The control paradigms of the distributed generation (DG) sources in the smart grid are realised by either utilising virtual power plant (VPP) or by employing MicroGrid structures. Both VPP and MicroGrid are presented with the problem of control of power flow between their comprising DG sources. This study depicts this issue for VPP and proposes a novel and improved universal active and reactive power flow controllers for three-phase pulse width modulated voltage source inverters (PWM-VSI) operating in the VPP environment. The proposed controller takes into account all cases of R-X relationship, thus allowing it to function in systems operating at high, medium (MV) and low-voltage (LV) levels. Also proposed control scheme for the first time in an inverter control takes into account the capacitance of the transmission line which is an important factor to accurately represent medium length transmission lines. This allows the proposed control scheme to be applied in VPP structures, where DG sources can operate at MV LV levels over a short/medium length transmission line. The authors also conducted small signal stability analysis of the proposed controller and compared it against the small signal study of the existing controllers.

Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

Stereotypes of age differences in personality traits : universal and accurate?

Age trajectories for personality traits are known to be similar across cultures. To address whether stereotypes of age groups reflect these age-related changes in personality, we asked participants in 26 countries (N = 3,323) to rate typical adolescents, adults, and old persons in their own country. Raters across nations tended to share similar beliefs about different age groups; adolescents were seen as impulsive, rebellious, undisciplined, preferring excitement and novelty, whereas old people were consistently considered lower on impulsivity, activity, antagonism, and Openness. These consensual age group stereotypes correlated strongly with published age differences on the five major dimensions of personality and most of 30 specific traits, using as criteria of accuracy both self-reports and observer ratings, different survey methodologies, and data from up to 50 nations. However, personal stereotypes were considerably less accurate, and consensual stereotypes tended to exaggerate differences across age groups.

The CLIMATE schools combined study : a cluster randomised controlled trial of a universal Internet-based prevention program for youth substance misuse, depression and anxiety

Background Anxiety, depressive and substance use disorders account for three quarters of the disability attributed to mental disorders and frequently co-occur. While programs for the prevention and reduction of symptoms associated with (i) substance use and (ii) mental health disorders exist, research is yet to determine if a combined approach is more effective. This paper describes the study protocol of a cluster randomised controlled trial to evaluate the effectiveness of the CLIMATE Schools Combined intervention, a universal approach to preventing substance use and mental health problems among adolescents. Methods/design Participants will consist of approximately 8400 students aged 13 to 14-years-old from 84 secondary schools in New South Wales, Western Australia and Queensland, Australia. The schools will be cluster randomised to one of four groups; (i) CLIMATE Schools Combined intervention; (ii) CLIMATE Schools - Substance Use; (iii) CLIMATE Schools - Mental Health, or (iv) Control (Health and Physical Education as usual). The primary outcomes of the trial will be the uptake and harmful use of alcohol and other drugs, mental health symptomatology and anxiety, depression and substance use knowledge. Secondary outcomes include substance use related harms, self-efficacy to resist peer pressure, general disability, and truancy. The link between personality and substance use will also be examined. Discussion Compared to students who receive the universal CLIMATE Schools - Substance Use, or CLIMATE Schools - Mental Health or the Control condition (who received usual Health and Physical Education), we expect students who receive the CLIMATE Schools Combined intervention to show greater delays to the initiation of substance use, reductions in substance use and mental health symptoms, and increased substance use and mental health knowledge

On Chinese Modal Particle A (啊) : A Pragmatic and Semantic Study

Chinese modal particles feature prominently in Chinese people’s daily use of the language, but their pragmatic and semantic functions are elusive as commonly recognised by Chinese linguists and teachers of Chinese as a foreign language. This book originates from an extensive and intensive empirical study of the Chinese modal particle a (啊), one of the most frequently used modal particles in Mandarin Chinese. In order to capture all the uses and the underlying meanings of the particle, the author transcribed the first 20 episodes, about 20 hours in length, of the popular Chinese TV drama series Kewang ‘Expectations’, which yielded a corpus data of more than 142’000 Chinese characters with a total of 1829 instances of the particle all used in meaningful communicative situations. Within its context of use, every single occurrence of the particle was analysed in terms of its pragmatic and semantic contributions to the hosting utterance. Upon this basis the core meanings were identified which were seen as constituting the modal nature of the particle.

Gender stereotypes of personality : universal and accurate?

Numerous studies have documented subtle but consistent sex differences in self-reports and observer-ratings of five-factor personality traits, and such effects were found to show well-defined developmental trajectories and remarkable similarity across nations. In contrast, very little is known about perceived gender differences in five-factor traits in spite of their potential implications for gender biases at the interpersonal and societal level. In particular, it is not clear how perceived gender differences in five-factor personality vary across age groups and national contexts and to what extent they accurately reflect assessed sex differences in personality. To address these questions, we analyzed responses from 3,323 individuals across 26 nations (mean age = 22.3 years, 31% male) who were asked to rate the five-factor personality traits of typical men or women in three age groups (adolescent, adult, and older adult) in their respective nations. Raters perceived women as slightly higher in openness, agreeableness, and conscientiousness as well as some aspects of extraversion and neuroticism. Perceived gender differences were fairly consistent across nations and target age groups and mapped closely onto assessed sex differences in self- and observer-rated personality. Associations between the average size of perceived gender differences and national variations in sociodemographic characteristics, value systems, or gender equality did not reach statistical significance. Findings contribute to our understanding of the underlying mechanisms of gender stereotypes of personality and suggest that perceptions of actual sex differences may play a more important role than culturally based gender roles and socialization processes.

Semantic querying of business process models

Determining similarity between business process models has recently gained interest in the business process management community. So far similarity was addressed separately either at semantic or structural aspect of process models. Also, most of the contributions that measure similarity of process models assume an ideal case when process models are enriched with semantics - a description of meaning of process model elements. However, in real life this results in a heavy human effort consuming pre-processing phase which is often not feasible. In this paper we propose an automated approach for querying a business process model repository for structurally and semantically relevant models. Similar to the search on the Internet, a user formulates a BPMN-Q query and as a result receives a list of process models ordered by relevance to the query. We provide a business process model search engine implementation for evaluation of the proposed approach.

The prevention of childhood anxiety and promotion of resilience among preschool-aged children : a universal school based trial

This study is the first to examine the effectiveness of the Fun FRIENDS programme, a school-based, universal preventive intervention for early childhood anxiety and promotion of resilience delivered by classroom teachers. Participants (N = 488) included children aged 4–7 years attending 1 of 14 Catholic Education schools in Brisbane, Australia. The schools were randomly allocated to one of three groups, the intervention, active comparison and waitlist control group. Parents completed standardized measures of anxiety and behavioural inhibition (BI), resilience, social and emotional functioning and behaviour difficulties in addition to parental stress and anxiety, at pre- and post- and 12-month follow-up. Teachers also completed a parallel measure of social and emotional strength at the three time points. Comparable results were obtained for the intervention and comparison groups; however, the intervention group (IG) achieved greater reductions in BI, child behavioural difficulties and improvements in social and emotional competence. In addition, significant improvements in parenting distress and parent–child interactions were found for the IG, with gains maintained at 12-month follow-up. Teacher reports revealed more significant improvement in social and emotional competence for the IG. Clinical implications of the findings are discussed, along with limitations and directions for future research.

Semantic search as inference : applications in health informatics

This thesis developed new search engine models that elicit the meaning behind the words found in documents and queries, rather than simply matching keywords. These new models were applied to searching medical records: an area where search is particularly challenging yet can have significant benefits to our society.

Investigating subspace distances in semantic spaces

Semantic space models of word meaning derived from co-occurrence statistics within a corpus of documents, such as the Hyperspace Analogous to Language (HAL) model, have been proposed in the past. While word similarity can be computed using these models, it is not clear how semantic spaces derived from different sets of documents can be compared. In this paper, we focus on this problem, and we revisit the proposal of using semantic subspace distance measurements [1]. In particular, we outline the research questions that still need to be addressed to investigate and validate these distance measures. Then, we describe our plans for future research.

Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.