<p>  </p> <p>Despite some criticism, peace journalism has received considerable attention over the last three decades as a normative approach to promote a more conscientious, contextualized, and proactive journalistic approach to reporting conflict. This study aims at examining the media coverage of the social protests of Colombia in 2021 using peace journalism as a theoretical framework. A sample of 210 news stories from April 28 to June 10 of 2021 were analyzed to see how two Colombian newspapers reflected Galtung’s framework of peace journalism and see variations over time and between media sources. Given that most of the research on the operationalization of peace journalism has examined militaristic armed conflicts, this analysis seeks to expand the research exploring war/peace journalism media framing in the context of social protests. The results cannot fully confirm previous studies that have identified a predominance of war journalism framing in media coverage; although at the individual indicator level there is a clear inclination towards war journalism, considering a holistic assessment, there is a tendency towards peace journalism. These results suggest theoretical and practical implications that must be addressed for the refinement of peace journalism as a journalistic practice.  </p>

The Development, presentation, and application of technique for interpreting examples of so-called muskeg terrain in Northern Canada. The emphasis is with a view to utilitarian aspects, not necessarily botanical implications, though the medium for the work is vegetal coverage. Illustrated with photographs. / Thesis / Bachelor of Arts (BA)

The Department of Homeland Security reports that over 90% of cyberattacks stem from security vulnerabilities in software, costing the U.S. $109 billion dollars in damages in 2016 alone according to The White House. As NIST estimates that today's software contains 25 bugs for every 1,000 lines of code, the prompt discovery of security flaws is now vital to mitigating the next major cyberattack. Over the last decade, the software industry has overwhelmingly turned to a lightweight defect discovery approach known as fuzzing: automated testing that uncovers program bugs through repeated injection of randomly-mutated test cases. Academic and industry efforts have long exploited the semantic richness of open-source software to enhance fuzzing with fast and fine-grained code coverage feedback, as well as fuzzing-enhancing code transformations facilitated through lightweight compiler-based instrumentation. However, the world's increasing reliance on closed-source software (i.e., commercial, proprietary, and legacy software) demands analogous advances in automated security vetting beyond open-source contexts. Unfortunately, the semantic gaps between source code and opaque binary code leave fuzzing nowhere near as effective on closed-source targets. The difficulty of balancing coverage feedback speed and precision in binary executables leaves fuzzers frequently bottlenecked and orders-of-magnitude slower at uncovering security vulnerabilities in closed-source software. Moreover, the challenges of analyzing and modifying binary executables at scale leaves closed-source software fuzzing unable to fully leverage the sophisticated enhancements that have long accelerated open-source software vulnerability discovery. As the U.S. Cybersecurity and Infrastructure Security Agency reports that closed-source software makes up over 80% of the top routinely exploited software today, combating the ever-growing threat of cyberattacks demands new practical, precise, and performant fuzzing techniques unrestricted by the availability of source code. This thesis answers the following research questions toward enabling fast, effective fuzzing of closed-source software: 1. Can common-case fuzzing insights be exploited to more achieve low-overhead, fine-grained code coverage feedback irrespective of access to source code? 2. What properties of binary instrumentation are needed to extend performant fuzzing-enhancing program transformation to closed-source software fuzzing? In answering these questions, this thesis produces the following key innovations: A. The first code coverage techniques to enable fuzzing speed and code coverage greater than source-level fuzzing for closed-source software targets. (chapter 3) B. The first instrumentation platform to extend both compiler-quality code transformation and compiler-level speed to closed-source fuzzing contexts (chapter 4) / Doctor of Philosophy / The Department of Homeland Security reports that over 90% of cyberattacks stem from security vulnerabilities in software, costing the U.S. $109 billion dollars in damages in 2016 alone according to The White House. As NIST estimates that today's software contains 25 bugs for every 1,000 lines of code, the prompt discovery of security flaws is now vital to mitigating the next major cyberattack. Over the last decade, the software industry has overwhelmingly turned to lightweight defect discovery through automated testing, uncovering program bugs through the repeated injection of randomly-mutated test cases. Academic and industry efforts have long exploited the semantic richness of open-source software (i.e., software whose full internals are publicly available, interpretable, and changeable) to enhance testing with fast and fine-grained exploration feedback; as well as testing-enhancing program transformations facilitated during the process by which program executables are generated. However, the world's increasing reliance on closed-source software (i.e., software whose internals are opaque to anyone but its original developer) like commercial, proprietary, and legacy programs demands analogous advances in automated security vetting beyond open-source contexts. Unfortunately, the challenges of understanding programs without their full source information leaves testing nowhere near as effective on closed-source programs. The difficulty of balancing exploration feedback speed and precision in program executables leaves testing frequently bottlenecked and orders-of-magnitude slower at uncovering security vulnerabilities in closed-source software. Moreover, the challenges of analyzing and modifying program executables at scale leaves closed-source software testing unable to fully leverage the sophisticated enhancements that have long accelerated open-source software vulnerability discovery. As the U.S. Cybersecurity and Infrastructure Security Agency reports that closed-source software makes up over 80% of the top routinely exploited software today, combating the ever-growing threat of cyberattacks demands new practical, precise, and performant software testing techniques unrestricted by the availability of programs' source code. This thesis answers the following research questions toward enabling fast, effective fuzzing of closed-source software: 1. Can common-case testing insights be exploited to more achieve low-overhead, fine-grained exploration feedback irrespective of access to programs' source code? 2. What properties of program modification techniques are needed to extend performant testing-enhancing program transformations to closed-source programs? In answering these questions, this thesis produces the following key innovations: A. The first techniques enabling testing of closed-source programs with speed and exploration higher than on open-source programs. (chapter 3) B. The first platform to extend high-speed program transformations from open-source programs to closed-source ones (chapter 4)

Crash response planning is a vital component of emergency management and highway emergency response planning. Evaluation of coverage of medical facilities is required to determine adequate access from crash sites to medical facilities. This study proposes a proof of concept for a planning tool that evaluates fatal and serious injury crash response coverage from crash sites to medical facilities in the Commonwealth of Virginia. Calculated travel times from fatal crash sites to medical facilities are compared with reported travel times to better estimate travel time modification factors. The modified travel times are used to determine coverage areas and evaluate serious injury crash response coverage of medical facilities in Virginia. A geo grid approach is used to demonstrate the proof of concept for a crash response planning tool. A risk grid is developed based on the aggregate number of fatal and serious injury crashes. This study includes serious injury crash response coverage because the number of serious injuries and serious injury rate are now included as reportable safety performance measures for state highway safety agencies. A mitigation grid is developed based on the travel time to the closest facility. Finally, a planning grid that combines risk and mitigation factors based on a decision matrix is presented. The resulting tool serves as a proof of concept for developing a crash response planning tool which enables planners to identify areas that do not have timely access from crash sites to medical facilities. / Master of Science / An objective of emergency responders is to safely transport crash victims from crash sites to medical facilities. Ensuring adequate access is an important goal of highway safety professionals. This study proposes a proof of concept for a planning tool that evaluates this access in the Commonwealth of Virginia. This study focuses on serious injury crash sites because the number of serious injuries and serious injury rate are now included as reportable safety performance measures for state highway safety agencies. Travel times from serious injury crash sites to medical facilities are used to identify areas that do not have timely access. Risk and mitigation assessments are performed by dividing the study area into equal sized cells. Risk and mitigation assessments are based on number of crashes and response travel times to the closest medical facility, respectively. These assessments are used to generate a proof of concept for a crash response planning tool which enables planners to identify areas that do not have timely access from crash sites to medical facilities.

Active learning is a practical field of machine learning as labeling data or determining which data to label can be a time consuming and inefficient task. Active learning automates the process of selecting which data to label, but current methods are heavily model reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing Combinatorial Coverage to overcome these issues. The proposed methods are data-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to different models and has a competitive sampling bias compared to benchmark methods. / Master of Science / Machine learning (ML) models are being used frequently in a variety of applications. For the model to be able to learn, data is required. Processing this data is often one of the most, if not the most, time consuming aspects of utilizing ML. One especially burdensome aspect of data processing is data labeling, or determining what each data point corresponds to in terms of real world class. For example, determining if a data point that is an image contains a plane or bird. This way the ML model can learn from the data. Active learning is a sub-field of machine learning which aims to ease this burden by allowing the model to select which data would be most beneficial to label, so that the entirety of the dataset does not need to be labeled. The issue with current active learning methods is that they are highly model dependent. In machine learning deployment the model being used may change while data stays the same, so this model dependency can cause for data points we label with respect to one model to not be ideal for another model. This model dependency has led to sampling bias issues as well; points which are chosen to be labeled may all be similar or not representative of all data resulting in the ML model not being as knowledgeable as possible. Relevant work has focused on the sampling bias issue, and several methods have been proposed to combat this issue. Few of the methods are applicable to any type of ML model though. The issue of sampled points not generalizing to different models has been studied but no solutions have been proposed. In this work we present active learning methods using Combinatorial Coverage. Combinatorial Coverage is a statistical technique from the field of Design of Experiments, and has commonly been used to design test sets. The extension of Combinatorial Coverage to ML is newer, and provides a way to focus on the data. We show that this data focused approach to active learning achieves a better performance when the sampled data is used for a different model and that it achieves a competitive sampling bias compared to benchmark methods.

Dissertation submitted in fulfilment of the requirements for the Masters Degree of Technology: Public Relations Management, Durban University of Technology, 2010. / During the Apartheid era in South Africa, women were marginalised from sport. The ability to participate in sport in South Africa is intrinsically linked to the political history of the country. Sport played a dynamic role in the struggle against the diabolical system of apartheid in South Africa and has a powerful role to play in the transformation and nation building of South Africa. Women have made great strides in sport in recent years in South Africa and the way in which media portray these sporting achievements is critically important to dislodging negative stereotypes and overcoming discrimination of women in sport. The unfair coverage of women’s sport displays gender based attitudes which systematically disadvantage women’s position in society. Women’s participation in sport has grown dramatically but despite this growth coverage of women in sport remains inferior to that given to men in sport across all media. Media need to acknowledge, promote and celebrate the wealth of talent that there is to be enjoyed by society. Newspaper reports are seldom challenged for not being accurate or truthful. But readers have a right to truthful information and reporting, and being trustworthy to the reader is the basis of good journalism. Media need to collectively reflect a diversity of content to ensure public access to a variety of viewpoints and they should expect societal intervention if the media fail to meet professional standards. The print media has a fundamental role to play in shaping public opinion and undoubtedly have an influence in portraying women in sport in South Africa. This study critically analyses the influence of the print media in portraying women in sport.

With the longstanding popularity of American team sports having now lead us to a point where athletes are multi‐million dollar investments capable of impacting culture beyond the playing field, the teams and leagues that provide a platform for these athletes to perform are more concerned with image and brand management now more than ever. Consequently, star athletes have become an increasingly vital part of building and sustaining league and team brands. The public relations methods utilized by these groups, especially the teams, when a star athlete has committed a PR blunder have varied over time. This content analysis uncovers whether the public relations tactics used by teams when a star athlete is accused of sexual assault has any impact on fan support. The study also seeks to find the public relations best practices that teams use in this situation, and whether or not there is any relationship in methods used by leagues and their corresponding teams and front offices. Newspaper articles pertaining to the sexual assault accusations of Kobe Bryant, Ben Roethlisberger and Johan Santana were used in this analysis, with two major news publications from each athlete’s home city providing the content. / Department of Journalism

Ling, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

This study was designed to determine whether Time, Newsweek and U.S. News & World Report news magazines expressed. opinions in their coverage of four topics concerning the 1979 energy crisis: United States government, OPEC, oil companies, and consumers. A content analysis of all stories in the three magazines from May to December 1979 indicated that Time was the most opinionated, U.S. News & World Report was second, and Newsweek was most neutral in coverage of the energy crisis. The percentage of article space allotted had no apparent effect upon the magazines' handling of those topics.

<p>Software testing is complex and time consuming. One way to reduce the effort associated with testing is to generate test data automatically. This thesis is divided into three parts. In the first part a mixed-integer constraint solver developed by Gupta et. al is studied. The solver, referred to as the Unified Numerical Approach (una), is an important part of their generator and it is responsible for solving equation systems that correspond to the program path currently under test.</p><p>In this thesis it is shown that, in contrast to traditional optimization methods, the una is not bounded by the size of the solved equation system. Instead, it depends on how the system is composed. That is, even for very simple systems consisting of one variable we can easily get more than a thousand iterations. It is also shown that the una is not complete, that is, it does not always find a mixed-integer solution when there is one. It is found that a better approach is to use a traditional optimization method, like the simplex method in combination with branch-and-bound and/or a cutting-plane algorithm as a constraint solver.</p><p>The second part explores a specification-based approach for generating tests developed by Meudec. Tests are generated by partitioning the specification input domain into a set of subdomains using a rule-based automatic partitioning strategy. An important step of Meudec’s method is to reduce the number of generated subdomains and find a minimal partition. This thesis shows that Meudec’s minimal partition algorithm</p><p>is incorrect. Furthermore, two new efficient alternative algorithms are developed. In addition, an algorithm for finding the upper and lower bound on the number of subdomains in a partition is also presented.</p><p>Finally, in the third part, two different designs of automatic testing tools are studied. The first tool uses a specification as an oracle. The second tool, on the other hand, uses a reference program. The fault-detection effectiveness of the tools is evaluated using both randomly and systematically generated inputs.</p>