155 research outputs found
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
Genetics of growth and development in cattle / by Raphael Abiodun Afolayan
No full text"February, 2003"Includes 5 papers co-authored by the author at end of textIncludes bibliographical references (leaves 146-179)xv, 179, [31] leaves : ill. ; 30 cm.Thesis (Ph.D.)--University of Adelaide, Dept. of Animal Science, 200
MasakhaneNLP Machine Translation Models
No full textThis repository contains reusable Machine Translation (MT) by the MasakhaneNLP Community.
MASAKHANE is an research effort for NLP for African languages that is OPEN SOURCE, CONTINENT-WIDE, DISTRIBUTED and ONLINE. This repository houses the models for Machine Translation for African languages.
See masakhane-mt-current-models.csv for current model information.
This repository was created by the Masakhane Web Translate Team. You can see some of the models in action on http://translate.masakhane.io
Book Review: African Philosophy: Reflections on Yoruba Metaphysics and Jurisprudence
No full textBook Title: African Philosophy: Reflections on Yoruba Metaphysics and JurisprudenceBook Author: Oladele Abiodun BalogunPublisher: Xcel Publishers. Pages: 387. Year of Publication: 201
Reformation of Slums
No full textThe world keeps getting better in every aspect including housing and infrastructure and the growing technology keeps improving affordable housing, but the chances of completely eradicating slums will remain slim because there will always be people unable to afford better housing than slums have to offer. Aside from the fact that a slum is known as being the residential environment with the poorest living conditions, it is also known for various negative activities and a relatively high crime rate. The notion that an environment greatly influences an individual holds out the necessity to create better living conditions that will in time nurture and improve the individual. To this end, the reformation of slums should be a priority. In as much as these slums cannot be eradicated completely, physical upgrading of slums with improved street networks, better building materials, better air quality, easy access to basic municipal services, improves natural ventilation, natural lighting and better drainages will prove to make positive changes economically, socially and reduce crime rates in many cities. It will also improve the physical general wellbeing of communities. In conclusion, a community, no matter how small has the ability to influence the general well-being of an entire nation. Paying a little more attention to the physical reformation of slums will positively affect the world at large in the long run
Environmental Justice, Politics and the Grassroots Movement
No full textIt's been a while since the term "environmental racism" was coined by Dr. Benjamin Chavis, Jr. to describe the discrimination suffered by people of color concerning hazardous waste siting. Since then, minority communities have become more aware of the dangers of hazardous waste and are fighting against polluters. However, the most effective line of resistance is still formed in the most affluent neighborhoods, and communities of color continue to suffer a disproportionate share of toxic pollution. The environmental justice movement within communities of color is faced with diverse problems of mobilization, organization, and effective resistance. The purpose of this paper is to analyze grassroots mobilization efforts within the environmental justice movement in light of environmental politics
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
Minimizing Energy Consumption in Wireless Ad hoc Networks with Meta heuristics
No full textAbstractThe objective of this study is to describe an energy function model base on Geographic Adaptive Fidelity (GAF), which is one of the best known topology management schemes used in saving energy consumption in ad-hoc wireless networks. In wireless ad-hoc network, the nodes responsible for the transmission of data are battery-operated and as a result, there is a need for energy to be conserved in order to prolong the battery lifespan. Genetic Algorithm (GA) and Simulated Annealing (SA) metaheuristics are compared to minimize the energy consumption in ad-hoc wireless networks modelled by rectangular GAF. Results show that GA and SA meta-heuristics are useful optimization techniques for minimizing the energy consumption in ad-hoc wireless networks
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