No description available.

The open source structure of Android applications introduces security vulnerabilities that can be readily exploited by third-party applications. We address certain vulnerabilities at both installation and runtime using machine learning. Effective classification techniques with neural networks can be used to verify the application categories on installation. We devise a novel application category verification methodology that involves machine learning the application permissions and estimating the likelihoods of different categories. To detect malicious patterns in runtime, we present a Hidden Markov Model (HMM) method to analyze the activity usage by tracking Intent log information. After applying our technique to nearly 1,700 popular third-party Android applications and malware, we report that a major portion of the category declarations were judged correctly. This demonstrates the effectiveness of neural network decision engines in validating Android application categories. The approach, using HMM to analyze the Intent log for the detection of malicious runtime behavior, is new. The test results show promise with a limited input dataset (69.7% accuracy). To improve the performance, further work will be carried out to: increase the dataset size by adding game applications, to optimize Baum-Welch algorithm parameters, and to balance the size of the Intent sequence. To better emulate the participant's usage, some popular applications can be selected in advance, and the remainder can be randomly chosen. / Master of Science

The aim of this study was to determine the relationship between office politics and selected performance outcomes namely stress, turnaround intent and work engagement, as perceived by employees working in law firms. A literature study indicated the relationship between perceptions of office politics and these selected job outcomes. As an empirical analysis, a measuring instrument consisting of five structured questionnaires was distributed via a non-probability, convenience sampling technique. Spearman’s correlation coefficient indicated the relationships between the variables. The results indicated a positive relationship between perceptions of office politics, job stress, burnout and turnaround intent but a negative relationship between perceptions of office politics and work engagement as well as its antecedents. / MBA, North-West University, Potchefstroom Campus, 2015

The aim of this study was to determine the relationship between office politics and selected performance outcomes namely stress, turnaround intent and work engagement, as perceived by employees working in law firms. A literature study indicated the relationship between perceptions of office politics and these selected job outcomes. As an empirical analysis, a measuring instrument consisting of five structured questionnaires was distributed via a non-probability, convenience sampling technique. Spearman’s correlation coefficient indicated the relationships between the variables. The results indicated a positive relationship between perceptions of office politics, job stress, burnout and turnaround intent but a negative relationship between perceptions of office politics and work engagement as well as its antecedents. / MBA, North-West University, Potchefstroom Campus, 2015

“Contributory intent” refers to the situation where, besides the defendant being at fault and causing harm to the plaintiff, the plaintiff also intentionally causes harm to him- or herself. “Contributory intent” can have the effect of either excluding the defendant’s liability (on the ground that the plaintiff's voluntary assumption of risk or intent completely cancels the defendant's negligence and therefore liability), or limiting the defendant’s liability (where both parties intentionally cause the plaintiff's loss thereby resulting in the reduction of the defendant’s liability). Under our law the "contributory intent" of the plaintiff, can either serve as a complete defence in terms of common law or it can serve to limit the defendant's liability in terms of the Apportionment of Damages Act 34 of 1956. The “Apportionment of Loss Bill 2003” which has been prepared to replace the current Act provides for the applicability of “contributory intent” as a defence limiting liability, but it is yet to be promulgated. / Criminal and Procedural Law

Contemporary American Criminal Law, as represented by the American Law Institute's Model Penal Code, defines the structure of criminal offenses in a manner that establishes certain psychological processes of the defendant as necessary conditions for criminal liability. In order to convict a defendant, the state must prove all offense elements including the voluntary act and culpability requirements. These provisions involve the actor's psychological processes, but neither the exact nature of these requirements nor the relationship between them is clearly understood. Certain general defenses, such as automatism and insanity, also address the defendant's psychological processes. It has been notoriously difficult, however, to develop a satisfactory formulation of either of these defenses or of the relationship between them and the system of offense elements. This dissertation presents a conceptual framework that grounds the Model Penal Code's structure of offense elements in philosophical action theory. On this interpretation, the offense requirements that involve the defendant's psychological processes can be understood as part of an integrated attempt to establish the criminal law as a behavior guiding institution that is uniquely appropriate to those who have the capacity to direct their conduct through a process of practical reasoning. The key offense requirements are designed to limit criminal liability to those behaviors that are appropriately attributed to the offender as a practical reasoner. Certain general defenses, including insanity, exculpate defendants when their behavior is not attributable to them as practical reasoners as a result of certain types of impairment that are not addressed by the offense elements. This conceptual framework provides a consistent interpretation of the relevant offense elements and defenses as part of an integrated system that limits criminal liability to those acts that are appropriately attributable to the defendant in his capacity as a practical reasoner. In addition, this dissertation contends that this system reflects a defensible conception of personal responsibility.

The aim of this essay was to examine adult-child power structures connected to the main character in Lois Lowry´s novel Number the Stars, to see if they could be found in different levels, and layers of the text. With the focus to see if the novel´s content might correlate to any educational purposes if used when teaching English as a second language in the Swedish upper secondary school. The analysis showed that the novel displayed a didactic intent from the author to introduce ideological social values belonging to the Danish society and the resistance movement in Denmark during the German occupation, between the years of 1940-1945. Furthermore, presumed transference of American values from the author were found in the narrative. The portrayals of these social values in the narrative are done with a display of adult power over children in the narrative, both in the story and towards the novel´s intended readers. The content of the novel could then be used for educational purposes to uncover and discuss aspects of social power through ideology, human values, and human rights, correlating to the English syllabus in Lgr11 regarding "relations and ethical questions", as well as the curricula’s aim to develop the students ability´s to "reflect over living conditions, social and cultural phenomena in different contexts and parts of the world where English is used" (Skolverket 32, 34).

Abstract Federal legislation and educational programs such as No Child Left Behind (2001) and Race to the Top (2009) identify school leaders as one of the major catalysts to improving academic achievement. Increasing accountability demands call for replacement of the principal when adequate gains in student achievement are not met, yet research indicates that it takes at least five years to affect change (Fullan, 2006). Why then would any principal remain in an appointment as principal in a chronically low-performing school? New principals generally stay no more than five to ten years in any one position (Dancy, 2007; NAESP, 1998). In several states, the average tenure rate for a new principal is just 4.5 years (Fuller, 2009). One of the key reasons principals leave is the stress related to the job responsibilities (Groff, 2001; National Association of Elementary School Principals, 2007; Ponder & Crow, 2005). Moreover, principal vacancies are expected to increase vastly within the next three to five years as more than a third of our nation’s teachers and school leaders are ready for retirement(U. S. Department of Education, 2010. With looming principal shortages, regular job turnover, and threat of replacement for current principals, who will lead the nation’s lowest-performing schools and what are the characteristics of those who intentionally seek to do so? The purpose of this study is to examine the impact of four factors associated with Krumboltz’s (1996) social learning theory of career decision making-- (1) personal characteristics, (2) work environment, (3) learning experience, and (4) task skills – on principals’ intent to stay or leave the profession of principalship when employed in a low-performing school. This study used data from 135 school administrators throughout the state of Louisiana who currently serve in schools considered “failing” by state standards in order to answer the following general questions: To what extent do the four factors of Krumboltz’s social learning theory of career decision making (personal characteristics, environment, formal learning experiences and task skills) combine to predict principals’ intent to stay in the role of principal in a low-performing school in Louisiana? What is the relative contribution of each of these factors in predicting principals’ intent to stay? A quantitative, correlational survey design was used to assess the factors that influence principals’ intent to leave or stay in the position of principal in low-performing schools throughout Louisiana. A modified version of the Principal Shortage Survey utilized in a previous study to analyze the principal shortage in Massachusetts (2006) was used. The surveys were administered electronically. Multiple regression was used to analyze results, using SPSS version 19.0. In general, the study supported Krumboltz’s theory, with district training a significant predictor of principal’s intent to stay. Principals who perceived their professional development as most effective were more likely to indicate a desire to remain in the principalship. Implications for accountability, principal training, and leadership in low-performing schools are discussed.

Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in teaching materials. This challenge causes a semantic gap. Learners lack sufficient knowledge about the domain they are trying to learn about, so are unable to assemble effective keywords that identify what they wish to learn. This problem presents an intent gap. The work presented in this thesis focuses on addressing the semantic and intent gaps that learners face during an e-Learning recommendation task. The semantic gap is addressed by introducing a method that automatically creates background knowledge in the form of a set of rich learning-focused concepts related to the selected learning domain. The knowledge of teaching experts contained in e-Books is used as a guide to identify important domain concepts. The concepts represent important topics that learners should be interested in. An approach is developed which leverages the concept vocabulary for representing learning materials and this influences retrieval during the recommendation of new learning materials. The effectiveness of our approach is evaluated on a dataset of Machine Learning and Data Mining papers, and our approach outperforms benchmark methods. The results confirm that incorporating background knowledge into the representation of learning materials provides a shared vocabulary for experts and learners, and this enables the recommendation of relevant materials. We address the intent gap by developing an approach which leverages the background knowledge to identify important learning concepts that are employed for refining learners' queries. This approach enables us to automatically identify concepts that are similar to queries, and take advantage of distinctive concept terms for refining learners' queries. Using the refined query allows the search to focus on documents that contain topics which are relevant to the learner. An e-Learning recommender system is developed to evaluate the success of our approach using a collection of learner queries and a dataset of Machine Learning and Data Mining learning materials. Users with different levels of expertise are employed for the evaluation. Results from experts, competent users and beginners all showed that using our method produced documents that were consistently more relevant to learners than when the standard method was used. The results show the benefits in using our knowledge driven approaches to help learners find relevant learning materials.

abstract: Virtual digital assistants are automated software systems which assist humans by understanding natural languages such as English, either in voice or textual form. In recent times, a lot of digital applications have shifted towards providing a user experience using natural language interface. The change is brought up by the degree of ease with which the virtual digital assistants such as Google Assistant and Amazon Alexa can be integrated into your application. These assistants make use of a Natural Language Understanding (NLU) system which acts as an interface to translate unstructured natural language data into a structured form. Such an NLU system uses an intent finding algorithm which gives a high-level idea or meaning of a user query, termed as intent classification. The intent classification step identifies the action(s) that a user wants the assistant to perform. The intent classification step is followed by an entity recognition step in which the entities in the utterance are identified on which the intended action is performed. This step can be viewed as a sequence labeling task which maps an input word sequence into a corresponding sequence of slot labels. This step is also termed as slot filling. In this thesis, we improve the intent classification and slot filling in the virtual voice agents by automatic data augmentation. Spoken Language Understanding systems face the issue of data sparsity. The reason behind this is that it is hard for a human-created training sample to represent all the patterns in the language. Due to the lack of relevant data, deep learning methods are unable to generalize the Spoken Language Understanding model. This thesis expounds a way to overcome the issue of data sparsity in deep learning approaches on Spoken Language Understanding tasks. Here we have described the limitations in the current intent classifiers and how the proposed algorithm uses existing knowledge bases to overcome those limitations. The method helps in creating a more robust intent classifier and slot filling system. / Dissertation/Thesis / Masters Thesis Computer Science 2018