The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

/pdf/political-discourse-content-analysis-a-critical-overview-of-3ij9zach6y.pdf

Political discourse content analysis: a critical overview of a computerized text analysis program linguistic inquiry and word count (liwc)

Software defined networks

Software defined cloud

Software-Defined Networks (SDNs) are emerging as one of the most promising new era network technologies with its centralized and easily programmable nature. Many security issues with legacy networks could easily be resolved using SDNs central management and control; at the same time, security vulnerabilities of this technology are still the biggest concern of researchers and industries for adapting this technology. In this paper, a comprehensive review on security aspects of SDN is presented, considering how researchers are utilizing its features for providing security as well as their concerns about its security. Further, a critical comparison of existing countermeasures against SDN security including distributed denial of service (DDoS) is presented. This analysis includes the research works that have been done since the origin of OpenFlow protocol till 2015, including the contributions made by industries as well as universities towards securing the SDN framework. We have also compared our survey with existing surveys. The motivation behind this survey is to identify security concerns of SDN and ongoing research in this field. We have focused on DDoS in SDN mainly, which has not been much targeted by research surveys. By highlighting the current state of the art in this domain, we could facilitate researchers with a broad overview of advancement of research in SDN security and DDoS. Copyright © 2017 John Wiley & Sons, Ltd.

Research Trends in Security and DDoS in SDN

Composable architecture for rack scale big data computing

An increasingly complex landscape of financial products, in combination with a falling provision of social security, increased life expectancy, and persisting financial illiteracy, calls for a reevaluation of personal financial planning. Currently, personal financial advice is offered predominantly from an institution's point of view, neglecting the personal point of view. We introduce a comprehensive, integrated, and interactive application for financial planning, which defines a person's life in terms of financial items and highlights the interactions among individual financial items and their impact on personal wealth. As individuals make decisions, their financial items interact according to a set of rules. Over time, events occur and global influences determine the growth rates of financial items. Our financial life model, which is accompanied by an interactive visualization and comprehensive analytics, enables financial planning in terms of life choices. Consequently, financial planning will occur from a personal point of view rather than a corporate perspective. This paper introduces our Smarter Financial Life project as well as an early gamified prototype, which has been presented at an Australian conference.

Smarter Financial Life: Rethinking personal financial planning

In recent years, several algorithms for mining frequent and emerging substring patterns from databases of string data (such as proteins and natural language texts) have been discovered, all of which traverse an enhanced suffix array data structure. All of these algorithms lie at either extreme of the efficiency spectrum; they are either fast and use enormous amounts of space, or they are compact and orders of magnitude slower. In this paper, we present an algorithm that achieves the best of both these extremes, having runtime comparable to the fastest published algorithms while using less space than the most space efficient ones. This excellent practical performance is underpinned by theoretical guarantees. Our main mechanism for keeping memory usage low is to build the enhanced suffix array incrementally, in blocks. Once built, a block is traversed to output patterns with required support before its space is reclaimed to be used for the next block.

Practical Efficient String Mining

The suffix array is a sorted array of all the suffixes in a string. This remarkably simple data structure is fundamental for string processing and lies at the heart of efficient algorithms for pattern matching, pattern mining, and data compression. In many applications suffix array construction, or equivalently suffix sorting, is a computational bottleneck and so has been the focus of intense research in the last 20 years. This paper outlines several suffix array construction algorithms that have emerged since the survey due to Puglisi, Smyth and Turpin [ACM Computing Surveys 39, 2007]. These algorithms have tended to strive for small working space (RAM), often at the cost of runtime, and make use of compressed data structures or secondary memory (disk) to achieve this goal. We provide a high-level description of each algorithm, avoiding implementation details as much as possible, and outline directions that could benefit from further research.

Trends in suffix sorting: a survey of low memory algorithms

In recent years, author identification has become an active research area, where the major differences are caused by paper or online medium, mode of entry and target audience. Much research has been devoted to analyzing writing styles in handwritten, word-processed and online social networks (OSN) texts. Word processing editors that typically include spell and grammar checkers may influence the writing style as it allows an individual to edit a piece of text to perfection. Thus, similarities may exist between OSN and word-processed texts. Moreover, none of the studies to date have made a detailed comparison of the writing styles across multidisciplinary factors. This paper attempts to close the gap between the writing styles in pre- and post-Internet periods as well as provide an in-depth comparison of the writing styles in OSN texts across three major factors: demographics, personality & behavior, and cybersecurity. The aim is to learn from past literature as we advance these techniques to OSN texts. Thus, in this paper, we also propose a novel machine learning prediction model based on tense morphology, to classify age and gender from English blogs, and the PAN 2013 dataset. This model achieves an accuracy of 94%-98% and 95%-97% for age and gender, respectively.

Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature

The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.Even though statistical tests have been utilized to conduct case-control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann-Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann-Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data. 

/pdf/leveraging-mann-whitney-u-test-on-large-scale-genetic-1tds034m.pdf

Jasbir Dhaliwal

Papers

Smarter Financial Life: Rethinking personal financial planning

Practical Efficient String Mining

Trends in suffix sorting: a survey of low memory algorithms

Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature

Leveraging Mann–Whitney U test on large-scale genetic variation data for analysing malaria genetic markers