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Multimodal Detection Of Cyberbullying On Twitter, Jiabao Qiu
Multimodal Detection Of Cyberbullying On Twitter, Jiabao Qiu
Master's Projects
Cyberbullying detection is one of the trending topics of research in recent years, due to the popularity of social media and the lack of limitations about using electronic communications. Detection of cyberbullying may prevent some bullying behaviors online. This paper introduced a Multimodal system that makes use of Convolutional Neural Network (CNN), Tensor Fusion Network, VGG-19 Network, and Multi-Layer Perceptron model, for the purpose of cyberbullying detection. This system can not only analyze the messages sent but also the extra information related to the messages (meta-information) and the images contained in the messages. The proposed system was trained and tested …
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Turkish Journal of Electrical Engineering and Computer Sciences
The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …
A Fuzzy Expert System For Predicting The Mortality Of Covid'19, Monika Mangla, Nonita Sharma, Poonam Mittal
A Fuzzy Expert System For Predicting The Mortality Of Covid'19, Monika Mangla, Nonita Sharma, Poonam Mittal
Turkish Journal of Electrical Engineering and Computer Sciences
The COVID-19 pandemic has had a widespread impact on health and economy across the globe. It is leading to a huge number of deaths per day. Few researchers have been attracted to analyzing the mortality rate of COVID-19 from various perspectives. During the research, it has become evident that these fatalities are not only caused by COVID19, but they are also affected by some other factors. The authors of this paper aim to encompass three important types of factors viz. risk factors, clinical factors, and miscellaneous factors that influence the mortality of COVID-19. This manuscript presents a rule-based model under …
กรอบงานการตอบคำถามภาษาไทยโดยใช้แบบจำลองประเภททรานส์ฟอร์เมอร์พร้อมด้วยข้อมูลเสริม, ภูริ ฟักมงคล
กรอบงานการตอบคำถามภาษาไทยโดยใช้แบบจำลองประเภททรานส์ฟอร์เมอร์พร้อมด้วยข้อมูลเสริม, ภูริ ฟักมงคล
Chulalongkorn University Theses and Dissertations (Chula ETD)
การตอบคำถาม (Question Answering) เป็นหนึ่งในงานด้านการประมวลผลภาษาธรรมชาติ ที่สามารถให้อุปกรณ์เข้าใจบทความและสามารถตอบคำถามที่ให้ได้ มีงานศึกษาทางด้านตอบคำถามในภาษาอังกฤษซึ่งมีจำนวนของชุดข้อมูลที่มีจำนวนมาก แต่ภาษาไทยเป็นภาษาที่มีจำนวนของชุดข้อมูลที่น้อยกว่า ทำให้ประสิทธิภาพของแบบจำลองการตอบคำถามภาษาไทยมีคะแนนเอฟวันเพียง 70% ในขณะที่แบบจำลองการตอบคำถามภาษาอังกฤษ สามารถมีคะแนนเอฟวันสูงถึง 90% ดังนั้น ในงานวิจัยชิ้นนี้ต้องการเพิ่มประสิทธิภาพของแบบจำลองการตอบคำถามภาษาไทย โดยการสร้างชุดข้อมูลเพิ่มด้วยแบบจำลอง Multilingual Text-to-Text Transfer Transformer (mT5) และการประมวลผลข้อมูลที่เหมาะสมกับภาษาไทย วิธีนี้ สามารถสร้างชุดข้อมูลการตอบคำถามได้มากกว่าหนึ่งแสนชุดจากบทความ Wikipedia ภาษาไทย นอกจากนี้ งานวิจัยชิ้นนี้ยังศึกษารูปแบบวิธีการฝึกสอนในรูปแบบต่าง ๆ โดยใช้ชุดข้อมูลที่สร้างขึ้น เพื่อเพิ่มประสิทธิภาพให้กับแบบจำลองการตอบคำถามให้ได้มากที่สุด อีกทั้ง ในงานวิจัยนี้ได้เสนอการวัดผลแบบจำลองการตอบคำถามภาษาไทยโดยใช้คะแนนเอฟวันระดับพยางค์ ซึ่งมีความเหมาะสมกับภาษาไทยมากกว่าการวัดผลด้วยวิธีการดั้งเดิม โดยใช้คะแนนความถูกต้องและคะแนนเอฟวันระดับคำ ผู้วิจัยได้ทำการทดลองบนชุดข้อมูลการตอบคำถามภาษาไทยทั้ง 2 ชุด ได้แก่ Thai Wiki QA และ iApp Wiki QA พบว่าผลการทดลองโดยใช้วิธีการสร้างชุดข้อมูลของงานวิจัยชิ้นนี้ สามารถให้ประสิทธิภาพของแบบจำลองสูงขึ้นบนชุดข้อมูลทั้ง 2 ชุด โดยใช้แบบจำลองประเภท ทรานส์ฟอร์เมอร์ ได้แก่ Roberta และ mT5
Incorporating Context Into Non-Autoregressive Model Using Contextualized Ctc For Sequence Labelling, Burin Naowarat
Incorporating Context Into Non-Autoregressive Model Using Contextualized Ctc For Sequence Labelling, Burin Naowarat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Connectionist Temporal Classification (CTC) loss has become widely used in sequence modeling tasks such as Automatic Speech Recognition (ASR) and Handwritten Text Recognition (HTR) due to its ease of use. CTC itself has no architecture constraints, but it is commonly used with recurrent models that predict letters based on histories in order to relax the conditional independent assumption. However, recent sequence models that incorporate CTC loss have been focusing on speed by removing recurrent structures, hence losing important context information. This thesis presents Contextualized Connectionist Temporal Classification (CCTC) loss, which induces prediction dependencies in non-recurrent and non-autoregressive neural networks for …
Voice Impersonation For Thai Speech Using Cyclegan Over Prosody, Chatri Chuanngulueam
Voice Impersonation For Thai Speech Using Cyclegan Over Prosody, Chatri Chuanngulueam
Chulalongkorn University Theses and Dissertations (Chula ETD)
No abstract provided.
Classification Of Abusive Thai Messages In Social Networks Using Deep Learning, Ruangsung Wanasukapunt
Classification Of Abusive Thai Messages In Social Networks Using Deep Learning, Ruangsung Wanasukapunt
Chulalongkorn University Theses and Dissertations (Chula ETD)
Social media has improved on traditional news sources by allowing increased access to information. However, the anonymity social media provides can lead to abusive and hateful speech without detection or repercussion from individuals with malicious intentions. This research develops a binomial and a multinomial classification model for classifying Thai social media text for five categories of abusive content detection in social media that include Rude, Figurative, Dirty, Offensive and Non-Abusive. The experiments demonstrated that DistilBERT achieved the highest F1 score with 0.8510 for the binomial model and 0.9067 for the multinomial model. BiLSTM performed second best with an F1 score …
Designing Answer Sets For Thai Advisory Chatbot With Different Talking Styles For Covid-19 Pandemic, Sakolwan Peetaneelavat
Designing Answer Sets For Thai Advisory Chatbot With Different Talking Styles For Covid-19 Pandemic, Sakolwan Peetaneelavat
Chulalongkorn University Theses and Dissertations (Chula ETD)
This research focuses on designing a set of answers for Thai advisory chatbot using a different conversational approach from conventional chatbot for COVID-19 pandemic in order to improve user’s chatbot experience. The research studies how to modify the underlying interactive chatbot language to suit those extrovert and introvert personalities. To evaluate the correctness of answer set design, the author builds two classification models based on Extroversion Text Classification Model (ETCM). Both models utilize the same Logistic Regression algorithm, but different feature selection techniques. The first model relies solely on Term Frequency-Inverse Document Frequency (TF-IDF) property. In contrast, the second model …
Benefits Of Technology Integration In A Small Behavioral Health Organization, Tremaria Sweet
Benefits Of Technology Integration In A Small Behavioral Health Organization, Tremaria Sweet
Walden Dissertations and Doctoral Studies
The current study sought to understand the significance of information systems (IS) planning in small-to-medium enterprise (SME) behavioral health agencies and the direct impact it has on organizational growth and performance. The research was guided by the Baldrige Excellence Framework. The study was based on a sample of one senior leader from one agency licensed to practice in three states in the eastern region of the United States. This exploratory case study involved using semi-structured interviews and internal archival data during the process of data collection. Information technology (IT) competence, skills, leadership, infrastructure, and awareness were emergent themes identified in …
Implementation Strategies For Modeling And Simulation In Military Organizations, Cody Lynn Taylor
Implementation Strategies For Modeling And Simulation In Military Organizations, Cody Lynn Taylor
Walden Dissertations and Doctoral Studies
Some IT project managers working for U.S. military organizations are struggling to implement modern modeling and simulation (M&S) technology. Implementation strategies are needed to help IT practitioners deliver meaningful simulations and models that ultimately help senior leaders make logical and science-based decisions. Grounded in the extended technology acceptance model, the purpose of this qualitative multiple-case study was to explore strategies some IT project managers supporting U.S. military organizations use to implement modern M&S technology. The participants included 10 civil servants who successfully implemented modeling and simulation technology for military organizations located in the United States eastern region. Data was collected …
Ranked List Fusion And Re-Ranking With Pre-Trained Transformers For Arqmath Lab, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Ranked List Fusion And Re-Ranking With Pre-Trained Transformers For Arqmath Lab, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Computer Science Faculty Publications
This paper elaborates on our submission to the ARQMath track at CLEF 2021. For our submission this year we use a collection of methods to retrieve and re-rank the answers in Math Stack Exchange in addition to our two-stage model which was comparable to the best model last year in terms of NDCG’. We also provide a detailed analysis of what the transformers are learning and why is it hard to train a math language model using transformers. This year’s submission to Task-1 includes summarizing long question-answer pairs to augment and index documents, using byte-pair encoding to tokenize formula and …
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …
"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth
"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth
Publications
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to …
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Publications
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …
Interactive Learning Approach For Arabic Target-Based Sentiment Analysis, Husamelddin Balla, Marisa Llorens, Sarah Jane Delany
Interactive Learning Approach For Arabic Target-Based Sentiment Analysis, Husamelddin Balla, Marisa Llorens, Sarah Jane Delany
Conference papers
Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IALSTM model uses an interactive attentionbased mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right and left contexts, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: …
Zero-Shot Action Recognition With Knowledge Enhanced Generative Adversarial Networks, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever
Zero-Shot Action Recognition With Knowledge Enhanced Generative Adversarial Networks, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever
Conference papers
Zero-Shot Action Recognition (ZSAR) aims to recognise action classes in videos that have never been seen during model training. In some approaches, ZSAR has been achieved by generating visual features for unseen classes based on the semantic information of the unseen class labels using generative adversarial networks (GANs). Therefore, the problem is converted to standard supervised learning since the unseen visual features are accessible. This approach alleviates the lack of labelled samples of unseen classes. In addition, objects appearing in the action instances could be used to create enriched semantics of action classes and therefore, increase the accuracy of ZSAR. …
Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles
Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles
Computer Science Faculty Publications
Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …
Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Computer Science Faculty Publications
Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few …
Extractive Research Slide Generation Using Windowed Labeling Ranking, Athar Sefid, Prasenjit Mitra, Jian Wu, C. Lee Giles
Extractive Research Slide Generation Using Windowed Labeling Ranking, Athar Sefid, Prasenjit Mitra, Jian Wu, C. Lee Giles
Computer Science Faculty Publications
Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods …
Reliable Data Collection: A Tool For Data Integrity In Nigeria, Stella Tonye Whyte
Reliable Data Collection: A Tool For Data Integrity In Nigeria, Stella Tonye Whyte
Walden Dissertations and Doctoral Studies
Unreliable and poor-quality data is a significant threat to governmental institutions because of its devastating impact on nations' social and economic well-being. Managers in government organizations require reliable data to inform economic planning and decision-making. Grounded in the theory of total quality management, the purpose of this qualitative multiple case study was to explore strategies information technology (IT) managers in sub-Saharan African countries use to ensure the reliability of data. The participants were 12 IT managers in three government establishments in Port Harcourt, Rivers State, Nigeria, responsible for ensuring the data reliability for economic planning and decision-making. The data collection …
Mental Health And The Covid-19 Pandemic: Analysis Of Twitter Discourse, Omar El-Gayar, Abdullah Wahbeh, Tareq Nasralah, Ahmed El Noshokaty, Mohammad A. Al-Ramahi
Mental Health And The Covid-19 Pandemic: Analysis Of Twitter Discourse, Omar El-Gayar, Abdullah Wahbeh, Tareq Nasralah, Ahmed El Noshokaty, Mohammad A. Al-Ramahi
Computer Information Systems Faculty Publications (Archived)
This study analyzed Twitter discourse to understand the association of the COVID-19 pandemic with mental health. The study compared tweets’ volume over time, tweets’ volume per mental health category, emotions, and the top hashtags on mental health before and after November 2019, the month on which the first COVID-19 case was reported. We analyzed a total of 273 million English tweets on mental health collected from 56 million unique users. Results and analysis showed a significant shift in trend for the volume of tweets on mental health over time. There was also a notable increase in the volume of tweets …
The Value Of Humanization In Customer Service, Yang Gao, Huaxia Rui, Shujing Sun
The Value Of Humanization In Customer Service, Yang Gao, Huaxia Rui, Shujing Sun
Research Collection School Of Computing and Information Systems
As algorithm-based agents become increasingly capable of handling customer service queries, customers are often uncertain whether they are served by humans or algorithms, and managers are left to question the value of human agents once the technology matures. The current paper studies this question by quantifying the impact of customers' enhanced perception of being served by human agents on customer service interactions. Our identification strategy hinges on the abrupt implementation by Southwest Airlines of a signature policy, which requires the inclusion of an agent's first name in responses on Twitter, thereby making the agent more humanized in the eyes of …
Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui
Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui
Research Collection School Of Computing and Information Systems
Despite that social media has become a promising alternative to traditional call centers, managers hesitate to fully harness its power because they worry that active service intervention may encourage excessive use of the channel by disgruntled customers. This paper sheds light on such a concern by examining the dynamics between brand-level customer complaints and service interventions on social media. Using details of customer-brand interactions of 40 airlines on Twitter, we find that more service interventions indeed cause more customer complaints, accounting for the online customer population and service quality. However, the increased complaints are primarily driven by the awareness enhancement …
Rapid Transition Of A Technical Course From Face-To-Face To Online, Swapna Gottipatti, Venky Shankaraman
Rapid Transition Of A Technical Course From Face-To-Face To Online, Swapna Gottipatti, Venky Shankaraman
Research Collection School Of Computing and Information Systems
Just like most universities around the world, the senior management at Singapore Management University decided to move all courses to a virtual, online, synchronous mode, giving instructors a very short notice period—one week—to make this transition. In this paper, we describe the challenges, practical solutions adopted, and the lessons learnt in rapidly transitioning a face-to-face Master’s degree course in Text Analytics and Applications into a virtual, online, course format that could deliver a quality learning experience.
Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri
Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri
All Works
The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …
Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri
Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri
Doctoral Dissertations
"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …
Using Eye-Gaze To Evaluate Neural Attention, Shahansha Salim
Using Eye-Gaze To Evaluate Neural Attention, Shahansha Salim
Master’s Dissertations
The ability to selectively concentrate on areas of interest while ignoring the rest is termed as attention in human beings. This ability has played a key role in survival as well as information processing. Neural Attention is said to be an effort to bring similar action of selectively concentrating areas of relevance in deep neural networks. This simple yet powerful concept has attracted a lot of research in recent years, yielding breakthrough results in Natural Language Processing (NLP) problems and main stream Computer Vision problems such as Image Caption Generation, Neural Machine Translation (NMT), Visual Question Answering (VQA), Action Recognition, …
Metalearning By Exploiting Granular Machine Learning Pipeline Metadata, Brandon J. Schoenfeld
Metalearning By Exploiting Granular Machine Learning Pipeline Metadata, Brandon J. Schoenfeld
Theses and Dissertations
Automatic machine learning (AutoML) systems have been shown to perform better when they use metamodels trained offline. Existing offline metalearning approaches treat ML models as black boxes. However, modern ML models often compose multiple ML algorithms into ML pipelines. We expand previous metalearning work on estimating the performance and ranking of ML models by exploiting the metadata about which ML algorithms are used in a given pipeline. We propose a dynamically assembled neural network with the potential to model arbitrary DAG structures. We compare our proposed metamodel against reasonable baselines that exploit varying amounts of pipeline metadata, including metamodels used …
Hategan: Adversarial Generative-Based Data Augmentation For Hate Speech Detection, Rui Cao, Roy Ka-Wei Lee
Hategan: Adversarial Generative-Based Data Augmentation For Hate Speech Detection, Rui Cao, Roy Ka-Wei Lee
SCIS Student Publications
Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods’ poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our …
On The Generation, Structure, And Semantics Of Grammar Patterns In Source Code Identifiers, Christian D. Newman,, Reem S. Alsuhaibani, Michael J. Decker, Anthony Peruma, Dishant Kaushik, Mohamed Wiem Mkaouer, Emily Hill
On The Generation, Structure, And Semantics Of Grammar Patterns In Source Code Identifiers, Christian D. Newman,, Reem S. Alsuhaibani, Michael J. Decker, Anthony Peruma, Dishant Kaushik, Mohamed Wiem Mkaouer, Emily Hill
Articles
Identifier names are the atoms of program comprehension. Weak identifier names decrease developer productivity and degrade the performance of automated approaches that leverage identifier names in source code analysis; threatening many of the advantages which stand to be gained from advances in artificial intelligence and machine learning. Therefore, it is vital to support developers in naming and renaming identifiers. In this paper, we extend our prior work, which studies the primary method through which names evolve: rename refactorings. In our prior work, we contextualize rename changes by examining commit messages and other refactorings. In this extension, we further consider data …