46 research outputs found
A study of the stylistic markers of the language of cartoons in Nigeria
No full textThis paper discusses the stylistic characteristics of the language of cartoons in some Nigerian newspapers. The analysis focuses on printing styles, stylistic registers, and textual features. The author concludes that the informal style (exemplified by the occurrence of Pidgin English, colloquial forms, loan blends, ellipsis, and telegraphic sentences) characterizes the language of the cartoons. Thus, cartoonists use language as an artistic medium in which various options of language are explored for effective communication
Regularized Deep Neural Network for Post-Authorship Attribution
No full textA thesis submitted to the Faculty of Science, in fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science, School of Science, University of the Witwatersrand, Johannesburg, 2024Post-authorship attribution is the computational process of determining the legitimate author of an online text snippet, such as an email, blog, forum post, or chat log, by employing stylometric features. The process consists of analysing various linguistic and writing patterns, such as vocabulary, sentence structure, punctuation usage, and even the use of specific words or phrases. By comparing these features to a known set of writing pieces from potential authors, investigators can make educated hypotheses about the true authorship of a text snippet. Additionally, post-authorship attribution has applications in fields like forensic linguistics and cybersecurity, where determining the source of a text can be crucial for investigations or identifying potential threats. Furthermore, in a verification procedure to proactively uncover misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks, finding a suitable text representation to adequately symbolise and capture an author’s distinctive writing from a computational linguistics perspective is typically known as a stylometric analysis. Additionally, most of the posts on social media or online are generally rife with ambiguous terminologies that could potentially compromise and influence the precision of the early proposed authorship attribution model. The majority of extracted stylistic elements in words are idioms, onomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy, which are fundamentally difficult to interpret by most existing natural language processing (NLP) systems. These difficulties make it difficult to correctly identify the true author of a given text. Therefore, further advancements in NLP systems are necessary to effectively handle these complex linguistic elements and improve the accuracy of authorship attribution models.
In this thesis, we introduce a regularised deep neural network (RDNN) model to solve the challenges that come with figuring out who wrote what after the fact. The proposed method utilises a convolutional neural network, a bidirectional long short-term memory encoder, and a distributed highway network to effectively address the post-authorship attribution problem. The neural network was utilised to generate lexical stylometric features, which were then fed into the bidirectional encoder to produce a syntactic feature vector representation. The feature vector was then fed into the distributed high-speed networks for regularisation to reduce network generalisation errors. The regularised feature vector was then given to the bidirectional decoder to learn the author’s writing style. The feature classification layer is made up of a fully connected network and a SoftMax function for prediction. The RDNN method outperformed the existing state-of-the-art methods in terms of accuracy, precision, and recall on the majority of the benchmark datasets. These results highlight the potential of the proposed method to significantly improve classification performance in various domains. Again, the introduction of an interactive system to visualise the performance of the proposed method would further enhance its usability and effectiveness in quantifying the contribution of the author’s writing characteristics in both online text snippets and literary documents. It is useful in processing the evidence that is needed to support claims or draw conclusions about the author’s writing style or intent during the pre-trial investigation by the law enforcement agent in the court of law. The incorporation of this method into the pretrial stage greatly
strengthens the credibility and validity of the findings presented in court and has the potential to revolutionise the field of authorship attribution and enhance the accuracy of forensic investigations. Furthermore, it ensures a fair and just legal process for all parties involved by providing concrete evidence to support or challenge claims.
We are also aware of the limitations of the proposed methods and recognise the need for additional research to overcome these constraints and improve the overall reliability and applicability of post-authorship attribution of online text snippets and literary documents for forensic investigations. Even though the proposed methods have revealed some unusual differences in author writing style, such as how influential writers, regular people, and suspected authors use language, the evidence from the results with the features extracted from the texts has shown promise for identifying authorship patterns and aiding in forensic analyses. However, much work remains to be done to validate the methodologies’ usefulness and dependability as effective authorship attribution procedures. Further research is needed to determine the extent to which external factors, such as the context in which the text was written or the author’s emotional state, may impact the identified authorship patterns. Additionally, it is crucial to establish a comprehensive dataset that includes a diverse range of authors and writing styles to ensure the generalizability of the findings and enhance the reliability of forensic analyses.
Furthermore, the dataset used in this thesis does not include a diverse variety of authors and writing styles, such as impostors attempting to impersonate another author, which limits the generalizability of the conclusions and undermines the credibility of forensic analysis. More studies can be conducted to broaden the proposed strategy for detecting and distinguishing impostors’ writing styles from those of authentic authors when committing crimes on both online and literary documents. It is conceivable for numerous criminals to collaborate to perpetrate a crime, which could aid in improving the proposed methods for detecting the
existence of multiple impostors or the contribution of each criminal writing style based on the person or individual they are attempting to mimic. The likelihood of numerous offenders working together complicates the investigation and necessitates advanced procedures for identifying their individual contributions, as well as both authentic and manufactured impostor contents within the text. This is especially difficult on social media, where fake accounts and anonymous profiles can make it difficult to determine the true identity of those involved, which can come from a variety of sources, including text, WhatsApps, chat images, videos, and
so on, and can lead to the spread of misinformation and manipulation. As a result, promoting a hybrid approach that goes beyond text as evidence could help address some of the concerns raised above. For example, integrating audio and visual data may provide a more complete perspective of the scenario. As a result, such an approach exacerbates the restrictions indicated in the distribution of data and may necessitate more storage and analytical resources. However, it can also lead to a more accurate and nuanced analysis of the situationMM202
The Effect of HIV on the Association of Hyperglycaemia and Active Tuberculosis in Zambia, a Case–Control Study
No full textAbstract Objectives To determine if HIV modifies the association between hyperglycaemia and active tuberculosis in Lusaka, Zambia. Methods A case–control study among newly—diagnosed adult tuberculosis cases and population controls in three areas of Lusaka. Hyperglycaemia is determined by random blood glucose (RBG) concentration measured at the time of recruitment; active tuberculosis disease by clinical diagnosis, and HIV status by serological result. Multivariable logistic regression is used to explore the primary association and effect modification by HIV. Results The prevalence of RBG concentration ≥ 11.1 mmol/L among 3843 tuberculosis cases was 1.4% and among 6977 controls was 1.5%. Overall, the adjusted odds ratio of active tuberculosis was 1.60 (95% CI 0.91–2.82) comparing those with RBG concentration ≥ 11.1– < 11.1 mmol/L. The corresponding adjusted odds ratio among those with and without HIV was 5.47 (95% CI 1.29–23.21) and 1.17 (95% CI 0.61–2.27) respectively; p-value for effect modification by HIV = 0.042. On subgroup analysis, the adjusted odds ratio of smear/Xpert-positive tuberculosis was 2.97 (95% CI 1.49–5.90) comparing RBG concentration ≥ 11.1– < 11.1 mmol/L. Conclusions Overall, no evidence of association between hyperglycaemia and active tuberculosis was found, though among those with HIV and/or smear/Xpert-positive tuberculosis there was evidence of association. Differentiation of hyperglycaemia caused by diabetes mellitus and stress-induced hyperglycaemia secondary to tuberculosis infection is important for a better understanding of these findings
Making a case for an interdisciplinary approach to water education towards sustainable development
No full textTraditional engineering education has focused on design principles and the integrity of products. It includes the designs for water supply and sanitation systems, water resources management, hydraulic structures, to name a few. However, this approach has led to a gap in design and implementation, leading to challenges in product uptake and sustainability. Water is central to several Sustainable Development Goals (SDGs), creating a web between the technical, environmental, social, financial, policy, and governance subsectors. Water engineers are critical to attaining the SDG goals; therefore, they must be trained to acquire the skills required for the task. For example, the WASH humanitarian sector has reiterated that the lack of the right expertise has been a bane to the delivery of interventions. Sustainable water projects must incorporate techno-centric, eco-centric, and Socio-centric concerns. A redirection of the water syllabus to include all the necessary aspects is the step towards transferring and acquiring the required skills. Content review is critical when water education sits within a civil engineering program. This paper presents calls for interdisciplinary engineering education from literature. It highlights the benefits of interdisciplinary water engineering education. The paper also provides approaches taken by the author to design and deliver interdisciplinary water modules and feedback from the students on each of the approaches. Overall, advocates for educators, universities, and training institutes to produce graduates who understand water engineers' dependence on other sectors for successful and more-sustainable solutions for society
It's Good to Be the King
No full textEpidemiological and animal studies often find that higher social status is associated with better physical health outcomes, but these findings are by design correlational and lack mediational explanations. In two studies, we examine neurobiological reactivity to test the hypothesis that higher social status leads to salutary short-term psychological, physiological, and behavioral responses. In Study 1, we measured police officers' subjective social status and had them engage in a stressful task during which we measured cardiovascular and neuroendocrine reactivity. In Study 2, we manipulated social status and examined physiological reactivity and performance outcomes to explore links among status, performance, and physiological reactivity. Results indicated that higher social status (whether measured or manipulated) was associated with approach-oriented physiology (Studies 1 and 2) and better performance (Study 2) relative to lower status. These findings point to acute reactivity as one possible causal mechanism to better physical health among those higher in social status. © The Author(s) 2013
Post-authorship attribution using regularized deep neural network
No full textPost-authorship attribution is a scientific process of using stylometric features to identify
the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has
useful applications in manifold domains, for instance, in a verification process to proactively detect
misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The
process assumes that texts can be characterized by sequences of words that agglutinate the functional
and content lyrics of a writer. However, defining an appropriate characterization of text to capture the
unique writing style of an author is a complex endeavor in the discipline of computational linguistics.
Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy
of authorship attribution. The vocabularies include idioms, onomatopoeias, homophones, phonemes,
synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network
(RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution.
It is based on a convolutional neural network, bidirectional long short-term memory encoder,
and distributed highway network. The neural network was used to extract lexical stylometric features
that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The
feature vector was then supplied as input to the distributed high networks for regularization to
minimize the network-generalization error. The regularized feature vector was ultimately passed
to the bidirectional decoder to learn the writing style of an author. The feature-classification layer
consists of a fully connected network and a SoftMax function to make the prediction. The RDNN
method was tested against thirteen state-of-the-art methods using four benchmark experimental
datasets to validate its performance. Experimental results have demonstrated the effectiveness of the
method when compared to the existing state-of-the-art methods on three datasets while producing
comparable results on one dataset.The Department of Science and Technology (DST) and the Council for Scientific and Industrial Research (CSIR).https://www.mdpi.com/journal/applsciam2023Computer Scienc
Post-Authorship Attribution Using Regularized Deep Neural Network
No full textPost-authorship attribution is a scientific process of using stylometric features to identify the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has useful applications in manifold domains, for instance, in a verification process to proactively detect misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The process assumes that texts can be characterized by sequences of words that agglutinate the functional and content lyrics of a writer. However, defining an appropriate characterization of text to capture the unique writing style of an author is a complex endeavor in the discipline of computational linguistics. Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy of authorship attribution. The vocabularies include idioms, onomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network (RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution. It is based on a convolutional neural network, bidirectional long short-term memory encoder, and distributed highway network. The neural network was used to extract lexical stylometric features that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The feature vector was then supplied as input to the distributed high networks for regularization to minimize the network-generalization error. The regularized feature vector was ultimately passed to the bidirectional decoder to learn the writing style of an author. The feature-classification layer consists of a fully connected network and a SoftMax function to make the prediction. The RDNN method was tested against thirteen state-of-the-art methods using four benchmark experimental datasets to validate its performance. Experimental results have demonstrated the effectiveness of the method when compared to the existing state-of-the-art methods on three datasets while producing comparable results on one dataset
Consistency Over Flattery
No full textNegative social feedback is often a source of distress. However, self-verification theory provides the counterintuitive explanation that negative feedback leads to less distress when it is consistent with chronic self-views. Drawing from this work, the present study examined the impact of receiving self-verifying feedback on outcomes largely neglected in prior research: implicit responses (i.e., physiological reactivity, facial expressions) that are difficult to consciously regulate and downstream behavioral outcomes. In two experiments, participants received either positive or negative feedback from interviewers during a speech task. Regardless of self-views, positive compared to negative feedback elicited lower self-reported negative affect. Implicit responses to negative feedback, however, depended on chronic self-views with more negative self-views associated with lower blood pressure reactivity, lower facial negativity, and enhanced creativity. These findings point at the role self-verification may play in long-term coping and stress regulation. © The Author(s) 2013
The impact of a combined TB/HIV intervention on the incidence of TB infection among adolescents and young adults in the HPTN 071 (PopART) trial communities in Zambia and South Africa.
No full textBACKGROUND: HPTN071 (PopART) was a cluster randomized trial conducted in Zambian and South African (SA) communities, between 2013-2018. The PopART intervention (universal HIV-testing and treatment (UTT) combined with population-level TB symptom screening) was implemented in 14 communities. The TREATS study (2017-2021) was conducted to evaluate the impact of the PopART intervention on TB outcomes. We report on the impact of the combined TB/HIV intervention on the incidence of TB infection in a cohort of adolescents and young adults (AYA) aged 15-24 years.
METHODS: A random sample of AYA was enrolled between July 2018 and July 2019 in 7 intervention vs 7 standard-of-care communities. We collected questionnaire data on risk factors for TB, and blood for measuring TB infection using QuantiFERON (QFT) Plus. AYA were seen at months 12 and 24 with all procedures repeated. Primary outcome was incidence of TB infection comparing intervention and standard-of-care communities. An incident case was defined as a participant with QFT interferon-gamma response of = 0.7 IU/ml ('positive') at follow up.
RESULTS: We enrolled 4,648 AYA, 2,223 (47.8%) had a negative QFT-plus result at baseline, 1,902 (85.6%) had a follow up blood sample taken at 12 months or 24 months. Among the 1,902 AYA, followed for 2,987 person-years, 213 had incident TB infection giving (7.1 per 100 person-years). TB infection incidence rates were 8.7 per 100 person-years in intervention communities compared to 6.0 per 100 person-years in standard-of-care communities. There was no evidence the intervention reduced the transmission of TB (incidence-rate-ratio of 1.45, 95%CI 0.97-2.15, p = 0.063).
CONCLUSION: In our trial setting, we found no evidence that UTT combined with TB active case finding reduced the incidence of TB infection at population level. Our data will inform future modelling work to better understand the population level dynamics of HIV and TB
Prevalence and risk factors of M tuberculosis infection in young people across 14 communities in Zambia and South Africa.
No full textBACKGROUND: From 2018-2021 the TB Reduction through Expanded Antiretroviral Treatment and TB Screening (TREATS) project took place in 21 Zambian and South African communities. The TREATS Incidence of TB Infection Cohort Study was conducted in adolescents and young people (AYP), aged 15-24 years in 14 communities. We describe the baseline prevalence and risk factors of Mycobacterium tuberculosis (M. tuberculosis) infection among this cohort and explore the quantitative QFT-Plus interferon gamma (IFN-γ) responses.
METHODS AND FINDINGS: A random sample of approximately 300 AYP per community were recruited and information on TB/HIV risk factors, TB symptoms and social mixing patterns collected. QuantiFERON TB Gold Plus assay (QFT-Plus) was used to detect M. tuberculosis infection, following manufacturer's instructions. Logistic regression was used to determine factors associated with infection. 5577 eligible AYP were invited to participate across both countries, with 4648 enrolled. QFT-Plus results were available for 4529: 2552(Zambia) and 1977(South Africa). Overall, 47.6% (2156/4529) AYP had positive QFT-Plus results, the prevalence of infection in South Africa being twice that in Zambia (64.7% (1280/1977) vs 34.3% (867/2552) p0.6 IU/ml) of the assay at baseline showed no evidence of association with recent TB exposure.
CONCLUSION: The high prevalence of infection in AYP warrants urgent action to address TB control, especially in South Africa. Further research is required to delineate antigen tube responses of the QFT-Plus assay more precisely to fully realise the benefit of the additional TB2 tube in high TB/HIV burden settings
