Computational Analysis of 3D Protein Structures

Ph.DDOCTOR OF PHILOSOPH

AuthorRank: A New Scheme for Identifying Field-Specific Key Researchers

When navigating into a new research field, it is important to identify papers with greatest impact and prominent authors which we can refer to. This work is motivated by the need to identify key authors in research fields. Traditional indices such as h-index only show the overall performance of an author. However, researchers generally contribute to more than one fields of research in their career, which makes it impractical to use h-index for identifying a key researcher in a research field. In this paper we propose a new PageRank-based scheme named “AuthorRank” for identifying key researchers in a specific field. We show that the proposed ranking system performs better than h-index does

Ensemble prediction model with expert selection for electricity price forecasting

Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets

Cross-Linguistic Twitter Analysis of Discussion Themes before, during and after Ramadan

© 2019 IEEE. This study represents the first comprehensive analysis of Twitter data for the United Arab Emirates using both Arabic and English texts. Particular attention is given to the impact of the holy period of Ramadan on the thematic content of Twitter discourse. We examine users\u27 tweet frequency, tweet length and tweet content for different languages (English/Arabic) using statistical methods and topic modeling. The results indicate that Arabic language tweets, during the Ramadan period, included more religious themes than did English tweets. Also, relative to English, Arabic tweets showed greater consistency of content during the three months of the study (before, during and after Ramadan). English content varied significantly over the three months with notable fluctuations in the frequency of content centering on the music, shopping, and health categories. These results suggest that such analytic methods applied to social media data can provide a useful indicator of societal discussion themes. Further research is merited with larger datasets over longer timeframes

Security for Complex Cyber-Physical and Industrial Control Systems: Current Trends, Limitations, and Challenges

Today’s society relies upon the smooth and secure functioning of the mission-critical infrastructures and their services. Much of this critical infrastructure relies on the complex cyber-physical systems and the industrial control systems. In recent years, securing these two types of systems has been a top priority due to a significant increase in number of attacks. Most of these systems are often several decades old, and they were developed without significant consideration of the security requirements. As such, there is an urgent need to protect these cyber-physical and industrial systems from external vulnerabilities. In this paper, we present a survey of the cyber-physical and industrial control systems, and explore the possibility and necessity for security of such systems. We discuss the various types of cyber-physical and industrial control systems currently being used, assess the vulnerabilities of such systems, discuss the literature on the cyber-physical and industrial control systems, and examine some works that propose security standards and models for these types of systems

Arabia Felix 2.0: a cross-linguistic Twitter analysis of happiness patterns in the United Arab Emirates

© 2019, The Author(s). The global popularity of social media platforms has given rise to unprecedented amounts of data, much of which reflects the thoughts, opinions and affective states of individual users. Systematic explorations of these large datasets can yield valuable information about a variety of psychological and sociocultural variables. The global nature of these platforms makes it important to extend this type of exploration across cultures and languages as each situation is likely to present unique methodological challenges and yield findings particular to the specific sociocultural context. To date, very few studies exploring large social media datasets have focused on the Arab world. This study examined social media use in Arabic and English across the United Arab Emirates (UAE), looking specifically at indicators of subjective wellbeing (happiness) across both languages. A large social media dataset, spanning 2013 to 2017, was extracted from Twitter. More than 17 million Twitter messages (tweets), written in Arabic and English and posted by users based in the UAE, were analyzed. Numerous differences were observed between individuals posting messages (tweeting) in English compared with those posting in Arabic. These differences included significant variations in the mean number of tweets posted, and the mean size of users networks (e.g. the number of followers). Additionally, using lexicon-based sentiment analytic tools (Hedonometer and Valence Shift Word Graphs), temporal patterns of happiness (expressions of positive sentiment) were explored in both languages across all seven regions (Emirates) of the UAE. Findings indicate that 7:00 am was the happiest hour, and Friday was the happiest day for both languages (the least happy day varied by language). The happiest months differed based on language, and there were also significant variations in sentiment patterns, peaks and troughs in happiness, associated with events of sociopolitical and religio-cultural significance for the UAE

P3: Privacy preservation protocol for appliance control application

Abstract—To address recently emerging concerns on privacy violations, this paper investigates possible sensitive information leakages in the appliance control, which is one of the handiest and most visible applications in smart grids. Without a consistent privacy preservation mechanism, the appliance control system can capture, model and divulge customers’ behavior, activities, and personal information at almost every level of society. We investigated a privacy threat model for the appliance control application and further design and implement a protection protocol. Experiment results demonstrate that our protocol merely incurs a substantially light overhead on the appliance control application, but is able to address and solve the formidable challenges both customers and utility companies are facing. Keywords-Data Privacy, Privacy Preservation, Smart Grid; I

Detecting click fraud in online advertising: A data mining approach

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding InitiativeSubmit request for dataset at https://larc.smu.edu.sg/buzzcity-mobile-advertisement-dataset</p