Flexible Process Support for Student Project Management

Student projects m higher education are used to help students prepare themselves for meeting the challenges they face in real world projects. However, the dynamic nature of a project had caused it to have ill-defined tasks during the planning process. The use of emails, face-to-face meetings and project reports to manage student projects do not appear efficient. The implementation of X CHIPS (a cooperative hypermedia system with flexible process support) in the EXTERNAL project has suggested that this system is able to provide efficient project management. This dissertation aims to answer the research question: "How a cooperative hypermedia based flexible process support approach is able to support project supervisors and students in managing student project". A case study approach has been adopted to investigate this phenomenon. In order to provide compelling evidence to support the answer to our research question, data from different sources was collected. In addition, triangulation was used to increase the reliability of the study. Our findings from the case study demonstrated that the cooperative hypermedia based flexible process support approach can support project supervisors and students to create, monitor and adapt project plans cooperatively. Project supervisors and students can identify emerging problems from the project plan and discuss issues in project meetings. Furthermore, this approach also supports meeting process modifications and unplanned meeting process. Project meetings between project supervisors and students are facilitated by synchronous and asynchronous cooperation. Therefore, this approach provides flexible ways of solving emergent problems in a timely manner. No previous studies on using cooperative hypermedia based flexible process support in supporting student project management have been found. Therefore, this study can be taken as a pilot study that is able to provide invaluable knowledge to researchers who are interested in this field of study

Aspect-based sentiment analysis for social recommender systems.

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To enhance recommendation performance, having an effective representation of products is essential. Social content such as product reviews contain experiential knowledge in the form of user opinions centred on product aspects. Making sense of these for recommender systems requires the capability to reason with text. However, Natural Language Processing (NLP) toolkits trained on formal text documents encounter challenges when analysing product reviews, due to their informal nature. This calls for novel methods and algorithms to capitalise on textual content in product reviews together with other knowledge resources. In this thesis, methods to utilise user purchase preference knowledge - inferred from the viewed and purchased product behaviour - are proposed to overcome the challenges encountered in analysing textual content. This thesis introduces three major methods to improve the performance of social recommender systems. First, an effective aspect extraction method that combines strengths of both dependency relations and frequent noun analysis is proposed. Thereafter, this thesis presents how extracted aspects can be used to structure opinionated content enabling sentiment knowledge to enrich product representations. Second, a novel method to integrate aspect-level sentiment analysis and implicit knowledge extracted from users' product purchase preferences analysis is presented. The role of sentiment distribution and threshold analysis on the proposed integration method is also explored. Third, this thesis explores the utility of feature selection techniques to rank and select relevant aspects for product representation. For this purpose, this thesis presents how established dimensionality reduction approaches from text classification can be employed to select a subset of aspects for recommendation purposes. Finally, a comprehensive evaluation of all the proposed methods in this thesis is presented using a computational measure of 'better' and Mean Average Precision (MAP) with seven real-world datasets

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems.

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach: The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings: Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications: The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value: This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches

Architecture of a GPS-based road management system

Malfunctioning traffic lights, potholes and roads in bad condition are only a few of the innumerable common thoroughfare problems that occasionally contribute to accidents. People tend to ignore reporting those issues as the channels for making a complaint is inconvenient. Accuracy of complaints is also at doubt as it tends to be general eg. Pothole at Ampang Road, in front of a police station. This paper presents the architecture of a Global Positioning System (GPS) based approach for reporting thoroughfare problems via Global System for Mobile Communications (GSM) for road maintenance management environment. To increase accuracy and efficiency, GPS can be used as it enables the tracking and tracing of the three figures of a GPS receiver’s coordinates namely longitude, latitude and altitude. Data like location, date and time will be optimized by mapping the site of where the thoroughfare problem exists in a map, with the intention that the relevant authorities could identify the spot and have the problems resolved responsively. The proposed system will serve as a handier and convenient alternative means for road users to send complaints to the relevant authorities, in addition to the existing channels, so that these issues could be addressed in a timely manner

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p

Preference and Sentiment Guided Social Recommendations with Temporal Dynamics

Capturing users’ preference that change over time is a great challenge in recommendation systems. What makes a product feature interesting now may become the accepted standard in the future. Social recommender systems that harness knowledge from user expertise and interactions to provide recommendation have great potential in capturing such trending information. In this paper, we model our recommender system using sentiment rich user generated product reviews and temporal information. Specifically we integrate these two resources to formalise a novel aspect-based sentiment ranking that captures temporal distribution of aspect sentiments and so the preferences of the users over time. We demonstrate the utility of our proposed model by conducting a comparative analysis on data extracted from Amazon.com and Cnet. We show that considering the temporal preferences of users leads to better recommendation and that user preferences change over time.This research has been partially supported by AGAUR Scholarship (2013FI-B 00034), ACIA mobility scholarship and Project Cognitio TIN2012-38450-C03-03

The rumour spectrum

<div><p>Rumour is an old social phenomenon used in politics and other public spaces. It has been studied for only hundred years by sociologists and psychologists by qualitative means. Social media platforms open new opportunities to improve quantitative analyses. We scanned all scientific literature to find relevant features. We made a quantitative screening of some specific rumours (in French and in English). Firstly, we identified some sources of information to find them. Secondly, we compiled different reference, rumouring and event datasets. Thirdly, we considered two facets of a rumour: the way it can spread to other users, and the syntagmatic content that may or may not be specific for a rumour. We found 53 features, clustered into six categories, which are able to describe a rumour message. The spread of a rumour is multi-harmonic having different frequencies and spikes, and can survive several years. Combinations of words (n-grams and skip-grams) are not typical of expressivity between rumours and news but study of lexical transition from a time period to the next goes in the sense of transmission pattern as described by Allport theory of transmission. A rumour can be interpreted as a speech act but with transmission patterns.</p></div

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, &quot;select and shrink for summary statistics&quot; (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.N

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

10.1038/s41431-021-00987-7EUROPEAN JOURNAL OF HUMAN GENETICS303349-36

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, select and shrink for summary statistics (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs