With the growing importance of e-learning and increased competition among e learning providers, website designers must cater to users' needs more accurately. Interfaces need to provide the features users demand to experience an optimal learning environment. This empirical research investigates whether the function of specific e learning features are either basic, performance related, indifferent, or attractive. The Kano model is applied to examine the impact of 73 e learning features on satisfaction. 1,034 completed questionnaires from an online survey distributed to economics and business students are the basis for the assignment to the Kano factors. Results show that among others, basic features include learning statistics, sample exams, and videotaped lectures. Educational videos are seen as an attractive factor. In terms of different groups of learners, findings confirm that Bachelor students are more demanding than Master and Doctoral students. Additionally, importance ratings allow recommendations for an implementation sequence for the features examined.

The longitudinal relationship between borderline personality features, disordered eating behaviour, and the role of impulsivity were examined using a sample of 643 Canadian adolescents from the McMaster Teen Study. Participants were assessed annually, beginning in Grade 7 until Grade 12.Using path analysis, the results suggest that higher symptoms of impulsivity increase an adolescent’s risk of engaging in disordered eating behaviour, as well as developing borderline personality features in later years. Results also showed a bidirectional relationship between these variables, whereby borderline personality features and disordered eating influence one another throughout time. As well, disordered eating appeared as an antecedent for borderline personality features. The findings highlight the importance for clinicians to be aware of the high comorbidity of disordered eating, borderline personality features, and impulsivity, and that early interventions that target impulsivity and problematic eating behaviour may mitigate the risk of future borderline personality features. Clinical implications, limitations, and future directions are discussed.

The research focuses on solutions to problems experienced in distinguishing between tragedy and pathos. The tragic is always characterised by emotions, and 'narrative can only be regarded as tragic through its tragic theme' (Steiner, 1961:16). Aristotle was the first to define tragedy he regards dramas/narratives as tragic if the protagonists die at the end, and the emotions of pity and fear are aroused. The tragedies Aristotle refers to all display emotional intensity, but are quite dissimilar. In some, the central emotions are evoked by the death of the protagonist, but in others there are events that are more intensely emotive than the death itself. In this study, the researcher provides an in-depth definition of the key concept 'tragedy', the different tragic emotions experienced and related concepts. Phatudi'sTladi wa Dikgati (1958) and Rammala's Lukas Mot helet hele (1963) prove clearly that there are two different types of tragedies, one of which contains pathos and the other contains tragedy. The research emphasises that these Sepedi narratives contain tragedy rather than mere pathos. The focus of this study is Sepedi tragic narratives, which have not previously been investigated in depth (Mohatlane, 2002:17). Only M.L. Bopape, P.M. Makgamatha and P.S.M. Mokgobu concentrated on tragic narratives, and P.M. Kgatla and P.S. Groenewald commented briefly on tragic narratives. The adopted narratological model employed in this research focuses plot and language usage, which are only briefly touched on in this research, as they have already been dealt with by prior theorists whose explanations of these narrative levels assist in understading the arrangement of the plot in tragic narratives. The research methodologies employed in analysing the structure of Sepedi tragic narratives/pathos to distinguish tragedy from pathos are descriptive, discussive and comperative. The researcher found few narratives containing pathos and tragedy in African languages, especially in Sepedi. The discussion of tragedy focuses on an analysis of the foundations, description, types and main characters (protagonists) of tragedy. Sepedi tragic narratives and pathos were influenced by Western culture; for example, Serudu's drama aka la peloga le tlale (1990) and G.H. Frans's Maaberone (1940) were influenced by Shakespeare's Romeo and Juliet. In this study, pathos is defined as related to societal sympathy. Holman (1936:166) explains that the 'emotional events in pathos affect the main character, his/her family and relatives, and they are left alone in those miseries and are expected to solve those problems without help from society'. In texts containing pathos, the main character is neither a villain nor a model of perfection, but basically good and decent. Even though the protagonist is great, he dies, but not because of moral blindness or error. He receives empathy because of his good character. The research concludes that in a text containing pathos, the protagonist is portrayed as simple and perfect. The events are so tragic that they induce pity and fear, more than death itself. Concerning tragic narratives, the investigation focuses on narratives of morality and ethics. Groenewald (1993:37) lists seven types of moral narratives, including tragedy and pathos. The two books that were the focus for this study were Rammala's Lukas Mot helet hele and Phatudi's Tladi wa Dikgati. The discussion of Lukas Mot helet hele focuses on the concepts of the (a) topic, (b) characters, (c) protagonists/antagonists as the most distinctive elements in tragic narratives and pathos. Authors organise their work in line with themes, which also control the main ideas and supporting ideas of the plot from beginning to end, and the protagonist, who dies at the end. This character is a great person who dies because of flaws (pride, jealousy and failure to take advice from others). The mistake is not easily recognised, it is hidden; it arouses emotions of pity and fear in the spectators. The analysis of Lukas Mot helet hele looks at (a) love, (b) segregation and hatred, (c) the hooliganism of Lukas Junior, (d) the emotional pain of the wife of Lukas (senior), and (e) the character Albi. The literary style (writing techniques) is analysed. Phala (1999:78) describes technique as 'the elements of writing and other concerns of the development of the plot, message and narrative' which are noted after the theme/message. Phatudi's novella Tladi wa Dikgati is analysed focusing on the protagonists and events that make it a tragic novella. The summary exposes the theme, characters, events, and place. The emphasis is on characters and events as central to tragic narrative. Analysis of Tladi wa Dikgati focuses on the theme, techniques, plot and writing style. The arrangement of events depends on the aim of the author and his objectives. It was found that this type of narrative reveals the emotions of fear, and depicts tragedy, and that the protagonist arouses pain or pity in the reader through attraction and suspense as the main techniques. Events are arranged and language is used to evoke a sense of tragedy and pity. / Thesis (DLitt)--University of Pretoria, 2016. / African Languages / DLitt / Unrestricted

Social media has become an important place for modern people to conveniently share and exchange their ideas and opinions. However, not all content on the social media have positive impact. Hate speech is one kind of harmful content that people use abusive speech attacking or promoting hate towards a specific group or an individual. With online hate speech on the rise these day, people have explored ways to automatically recognize the hate speech, and among the ways people have studied, the Bert-based approach is promising and thus dominates SemEval-2019 Task 6, a hate speech detection competition. In this work, the method of fusion of sentiment features and Bert-based approach is proposed. The classic Bert architecture for hate speech detection is modified to fuse with additional sentiment features, provided by an extractor pre-trained on Sentiment140. The proposed model is compared with top-3 models in SemEval-2019 Task 6 Subtask A and achieves 83.1% F1 score that better than the models in the competition. Also, to see if additional sentiment features benefit the detectoin of hate speech, the features are fused with three kind of deep learning architectures respectively. The results show that the models with sentiment features perform better than those models without sentiment features. / Master of Science / Social media has become an important place for modern people to conveniently share and exchange their ideas and opinions. However, not all content on the social media have positive impact. Hate speech is one kind of harmful content that people use abusive speech attacking or promoting hate towards a specific group or an individual. With online hate speech on the rise these day, people have explored ways to automatically recognize the hate speech, and among the ways people have studied, Bert is one of promising approach for automatic hate speech recognition. Bert is a kind of deep learning model for natural language processing (NLP) that originated from Transformer developed by Google in 2017. The Bert has applied to many NLP tasks and achieved astonished results such as text classification, semantic similarity between pairs of sentences, question answering with given paragraph, and text summarization. So in this study, Bert will be adopted to learn the meaning of given text and distinguish the hate speech from tons of tweets automatically. In order to let Bert better capture hate speech, the approach in this work modifies Bert to take additional source of sentiment-related features for learning the pattern of hate speech, given that the emotion will be negative when people trying to put out abusive speech. For evaluation of the approach, our model is compared against those in SemEval-2019 Task 6, a famous hate speech detection competition, and the results show that the proposed model achieves 83.1\% F1 score better than the models in the competition. Also, to see if additional sentiment features benefit the detection of hate speech, the features are fused with three different kinds of deep learning architectures respectively, and the results show that the models with sentiment features perform better than those without sentiment features.

This study is an examination of how prosodic pitch and intensity compare in autistic individuals and neurotypical individuals. Ten-minute recordings of casual conversation were taken and analyzed. Participants included 11 autistic individuals and 11 neurotypical individuals with six males and five females in each group. The Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2; Lord et al., 2012) prosody rating scale was used to collect a perceptual evaluation of each participan's prosody, and Praat acoustic analysis software was used to collect measures of pitch and intensity over the 10-minute period to investigate how speech characteristics change with conversation partner familiarity. Results revealed significant prosodic differences between autistic and neurotypical individuals. Both mean speaking pitch and intensity were found to be lower in the autistic group compared to the neurotypical group. The ADOS-2 (2012) measure of prosody was found to be ineffective in accurately capturing all individuals in the study who were autistic. A more comprehensive rating scale was suggested in order to adequately identify autistic individuals according to their prosodic characteristics. Results showed significant differences across sex in pitch and intensity, with males having a lower mean speaking pitch than females, as was expected. Remarkable differences were also observed between autistic male speakers and neurotypical male speakers. A lower pitch variability and lower pitch range were discovered in the autistic male speakers compared to neurotypical male speakers. Male speakers demonstrated greater intensity variability than female speakers. No changes were found in pitch or intensity for either neurological group as conversation partner familiarity increased. This may have been due to the nature of the conversation, which was structured as an interview in a single session. The findings of this study have clinical implications and are hoped to be helpful in understanding prosodic features of autistic adults. This can lead to better assessment and treatment of autistic individuals, supporting them in their daily functioning and ability to form and maintain relationships.

The aim of this thesis is to investigate the use of hyperspectral reflectance signals for the discrimination of cogongrass (Imperata cylindrica) from other subtly different vegetation species. Receiver operating characteristics (ROC) curves are used to determine which spectral bands should be considered as candidate features. Multivariate statistical analysis is then applied to the candidate features to determine the optimum subset of spectral bands. Linear discriminant analysis (LDA) is used to compute the optimum linear combination of the selected subset to be used as a feature for classification. Similarly, for comparison purposes, ROC analysis, multivariate statistical analysis, and LDA are utilized to determine the most advantageous discrete wavelet coefficients for classification. The overall system was applied to hyperspectral signatures collected with a handheld spectroradiometer (ASD) and to simulated satellite signatures (Hyperion). A leave-one-out testing of a nearest mean classifier for the ASD data shows that cogongrass can be detected amongst various other grasses with an accuracy as high as 87.86% using just the pure spectral bands and with an accuracy of 92.77% using the Haar wavelet decomposition coefficients. Similarly, the Hyperion signatures resulted in classification accuracies of 92.20% using just the pure spectral bands and with an accuracy of 96.82% using the Haar wavelet decomposition coefficients. These results show that hyperspectral reflectance signals can be used to reliably detect cogongrass from subtly different vegetation.

No description available.

No description available.

The clinical signs that a physician can identify from the appearance of a patient represent an important source of information, upon which the diagnostic decision is nominally based. Most of the research in medical education emphasizes the organization of medical knowledge or the reasoning processes based on these signs. This emphasis carries the implicit assumptions that identifying features is not the major problem and that evaluation of the clinical signs occurs largely independently of consideration of the diagnosis. However, there is accumulating evidence to suggest that the identification of these clinical signs can be influenced by the diagnosis being evaluated. The studies in this thesis contribute to this body of research by investigating the underlying processes by which the diagnosis being considered influences feature identification. Participants in these experiments were asked to identify the clinical signs from photographs of patients or electrocardiogram strips after having been biased towards the correct or an alternate diagnosis. It was found that the availability of a diagnosis served both to change the probability of reporting relevant clinical signs as well as to influence the identification of ambiguous signs. Manipulating the credibility of the suggested diagnosis, subsequently suggesting a second diagnosis, or decreasing the size of the pool of alternatives available to the diagnostician had a large impact on diagnostic conclusions, but produced relatively small effects on the features reported. These results suggest that changing the degree of focus that a clinician places on the suggested diagnosis has a small effect on the identification of the features by comparison to the substantial effect of merely suggesting a diagnosis. Furthermore, it was found that the subsequent suggestion of a competing diagnosis did not lead to a reinterpretation of the data. This indicates that once clinicians have seen the evidence one way, they are unlikely to see and label it differently. The implication of these findings for research on medical decision making, the mental organization of medical categories, as well as medical education are discussed. / Thesis / Doctor of Philosophy (PhD)

The proliferation of renewables, electric vehicles, and power electronic devices calls for innovative approaches to learn, optimize, and plan the power system. The uncertain and volatile nature of the integrated components necessitates using swift and probabilistic solutions. Gaussian process regression is a machine learning paradigm that provides closed-form predictions with quantified uncertainties. The key property of Gaussian processes is the natural ability to integrate the sensitivity of the labels with respect to features, yielding improved accuracy. This dissertation tailors Gaussian process regression for three applications in power systems. First, a physics-informed approach is introduced to infer the grid dynamics using synchrophasor data with minimal network information. The suggested method is useful for a wide range of applications, including prediction, extrapolation, and anomaly detection. Further, the proposed framework accommodates heterogeneous noisy measurements with missing entries. Second, a learn-to-optimize scheme is presented using Gaussian process regression that predicts the optimal power flow minimizers given grid conditions. The main contribution is leveraging sensitivities to expedite learning and achieve data efficiency without compromising computational efficiency. Third, Bayesian optimization is applied to solve a bi-level minimization used for strategic investment in electricity markets. This method relies on modeling the cost of the outer problem as a Gaussian process and is applicable to non-convex and hard-to-evaluate objective functions. The designed algorithm shows significant improvement in speed while attaining a lower cost than existing methods. / Doctor of Philosophy / The proliferation of renewables, electric vehicles, and power electronic devices calls for innovative approaches to learn, optimize, and plan the power system. The uncertain and volatile nature of the integrated components necessitates using swift and probabilistic solutions. This dissertation focuses on three practically important problems stemming from the power system modernization. First, a novel approach is proposed that improves power system monitoring, which is the first and necessary step for the stable operation of the network. The suggested method applies to a wide range of applications and is adaptable to use heterogeneous and noisy measurements with missing entries. The second problem focuses on predicting the minimizers of an optimization task. Moreover, a computationally efficient framework is put forth to expedite this process. The third part of this dissertation identifies investment portfolios for electricity markets that yield maximum revenue and minimum cost.