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Indexer++: Workload-Aware Online Index Tuning With Transformers And Reinforcement Learning, Vishal Sharma, Curtis Dyreson May 2022

Indexer++: Workload-Aware Online Index Tuning With Transformers And Reinforcement Learning, Vishal Sharma, Curtis Dyreson

Computer Science Student Research

With the increasing workload complexity in modern databases, the manual process of index selection is a challenging task. There is a growing need for a database with an ability to learn and adapt to evolving workloads. This paper proposes Indexer++, an autonomous, workload-aware, online index tuner. Unlike existing approaches, Indexer++ imposes low overhead on the DBMS, is responsive to changes in query workloads and swiftly selects indexes. Our approach uses a combination of text analytic techniques and reinforcement learning. Indexer++ consist of two phases: Phase (i) learns workload trends using a novel trend detection technique based on a pre-trained …

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A Content-Based File Identification Dataset: Collection, Construction, And Evaluation, Saja Dheyaa Khudhur, Hassan Awheed Jeiad May 2022

A Content-Based File Identification Dataset: Collection, Construction, And Evaluation, Saja Dheyaa Khudhur, Hassan Awheed Jeiad

Karbala International Journal of Modern Science

File-Type Identification (FTI) is one of the essential functions that can be performed by examining the data blocks' magic numbers. However, this examination leads to a challenge when a file is corrupt, or these magic numbers are missing. Content-based analytics is the best way for file type identification when the magic numbers are not available. This paper prepares and presents a content-based dataset for eight common types of files based on twelve features. We designed our dataset to be used for supervised and unsupervised machine learning models. It provides the ability to classify and cluster these types into two levels, …

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Nigella Sativa Methanol Extract Inhibits Pc-3 Cell Line Colonization, Induced Apoptosis, And Modulated Lc3-Based Autophagy, Yusuf Toy, Merve Kivrik, Marwa H. Alkhafaji, Aryan M. Faraj, Ibrahim H. Gecibesler, Can A. Ağca May 2022

Nigella Sativa Methanol Extract Inhibits Pc-3 Cell Line Colonization, Induced Apoptosis, And Modulated Lc3-Based Autophagy, Yusuf Toy, Merve Kivrik, Marwa H. Alkhafaji, Aryan M. Faraj, Ibrahim H. Gecibesler, Can A. Ağca

Karbala International Journal of Modern Science

Nigella sativa has various pharmacological properties and has been used throughout history for a variety of reasons. However, there is limited data about the effects of Nigella sativa (NS) on human cancer cells. This study aimed at observing the roles of methanolic extract of N. sativa on apoptosis and autophagy pathway in the Human PC3 (prostate cancer) cell line. The cell viability was checked by MTT assay. Clonogenic assay was performed to demonstrate clonogenicity and Western blot was used to check caspase-3, TIGAR, p53, and LC3 protein expression. The results demonstrated that PC3 cell proliferation was inhibited, caspase-3 and p53 …

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Using A Bert-Based Ensemble Network For Abusive Language Detection, Noah Ballinger May 2022

Using A Bert-Based Ensemble Network For Abusive Language Detection, Noah Ballinger

Computer Science and Computer Engineering Undergraduate Honors Theses

Over the past two decades, online discussion has skyrocketed in scope and scale. However, so has the amount of toxicity and offensive posts on social media and other discussion sites. Despite this rise in prevalence, the ability to automatically moderate online discussion platforms has seen minimal development. Recently, though, as the capabilities of artificial intelligence (AI) continue to improve, the potential of AI-based detection of harmful internet content has become a real possibility. In the past couple years, there has been a surge in performance on tasks in the field of natural language processing, mainly due to the development of …

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What About Generic "And"- And "Or"-Operations: From Levels Of Certainty (Philosophical-Physical-Mathematical) To A Natural Interpretation Of Quantum-Like Negative Degrees Of Certainty, Miroslav Svitek, Olga Kosheleva, Vladik Kreinovich May 2022

What About Generic "And"- And "Or"-Operations: From Levels Of Certainty (Philosophical-Physical-Mathematical) To A Natural Interpretation Of Quantum-Like Negative Degrees Of Certainty, Miroslav Svitek, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Fuzzy techniques -- techniques designed to convert imprecise human knowledge into precise computer-understandable terms -- have many successful applications. Traditional applications of fuzzy techniques use only important general features of human reasoning and, to make an implement more efficient, ignore subtle details, details which are not important for the corresponding application. But from the more fundamental viewpoint, it is desirable to understand, in all the detail, how people actually reason. In this paper, we use general ideas of fuzzy approach to answer this question. Interestingly -- and somewhat unexpectedly -- the resulting analysis leads to a natural explanation of existence …

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“Strict Moderation?” The Impact Of Increased Moderation On Parler Content And User Behavior, Nihal Kumarswamy May 2022

“Strict Moderation?” The Impact Of Increased Moderation On Parler Content And User Behavior, Nihal Kumarswamy

Computer Science and Engineering Theses - Archive

Social media platforms have brought people from different backgrounds, ethnicity, race, gender, etc together to form a platform to share ideas and opinions and discuss news events among other social events. Unfortunately, these platforms have also been a safe haven for abusive users who harass, bully other users or spread misinformation and disinformation. Social media platforms have a huge incentive to police these abusive users and keep them in check to allow other genuine users to use their platform. Social media platforms employ several different content moderation techniques to perform this task. These techniques vary across platforms, for example, Parler …

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Machine Learning Methods For Statistical Analysis And Representation Learning On Neuroimaging Data, Fan Yang May 2022

Machine Learning Methods For Statistical Analysis And Representation Learning On Neuroimaging Data, Fan Yang

Computer Science and Engineering Dissertations - Archive

With the recent advance and widespread adoption of imaging technological innovations, clinical practitioner and scientists can easily acquire and store a large amount of various neuroimaging modalities, such as Diffusion Tensor Imaging (DTI), Magnetic Resonance Imaging (MRI), resting-state functional MRI (rs-fMRI) and Positron Emission Tomography (PET), etc. These novel imaging data sources cover a rich amount of factors that influence patients' cognitive health, offer an objective view of patients at unprecedented multi-resolution for the understanding of brain structure and function, and have the significant potential to improve healthcare by aiding better decision-making in diagnosing, monitoring and treating diseases. Machine Learning …

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Female Students’ Perception Of Stem Identity At Utrgv, Kristen L. Hallas May 2022

Female Students’ Perception Of Stem Identity At Utrgv, Kristen L. Hallas

Honors Theses

Despite the increase in representation of women in science, technology, engineering, and mathematics (STEM) undergraduate programs, they are still underrepresented in the workforce. Recent statistics published by the National Science Foundation show that the rate of females and Hispanic/Latina females earning degrees does not match the rate in which they earn STEM occupations, especially advanced degrees. Moreover, out of early career doctorate holders, women are less likely to hold post-doctoral scholar positions and are more likely to hold an academic, non-tenure track position. In response to these disparities, I sought to identify the strongest motivators driving the ambition of female …

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Forecasting Electricity Load In New Jersey With Artificial Neural Networks, Erik W. Raab May 2022

Forecasting Electricity Load In New Jersey With Artificial Neural Networks, Erik W. Raab

Theses, Dissertations and Culminating Projects

Load forecasting is an important tool for both the energy and environmental sectors. It has progressed hand-in-hand with machine learning innovation, where recurrent neural networks, a type of artificial neural network, is primarily used. This thesis compares progressively complex, feed-forward artificial neural networks using a mix of weather and temporal data. We demonstrate that electrical load in New Jersey can be reliably predicted using memory-less algorithms with minimal predictors drawn from preexisting public data sources. The methods used in this thesis could be used to build competitive load forecasting models in other states, and if included in diverse model ensembles, …

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Twitter Account Classification Using Account Metadata: Organizationvs. Individual, Yusuf Mucahi̇t Çeti̇nkaya, Mesut Gürlek, İsmai̇l Hakki Toroslu, Pinar Karagöz May 2022

Twitter Account Classification Using Account Metadata: Organizationvs. Individual, Yusuf Mucahi̇t Çeti̇nkaya, Mesut Gürlek, İsmai̇l Hakki Toroslu, Pinar Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

Organizations present their existence on social media to gain followers and reach out to the crowds. Social media-related tasks and applications, such as social media graph construction, sentiment analysis, and bot detection, are required to identify the entities' account types. Some applications focus on personal accounts, whereas others only need nonpersonal accounts. This paper addresses the account classification problem using only minimum amount of data, which is the metadata of the account's profile. The proposed approach classifies accounts either as organization or individual, in a language-independent manner, without collecting the accounts' tweet content. The model uses a long short term …

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A Novel Deep Reinforcement Learning Based Stock Price Prediction Using Knowledge Graph And Community Aware Sentiments, Anil Berk Altuner, Zeynep Hi̇lal Ki̇li̇mci̇ May 2022

A Novel Deep Reinforcement Learning Based Stock Price Prediction Using Knowledge Graph And Community Aware Sentiments, Anil Berk Altuner, Zeynep Hi̇lal Ki̇li̇mci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the prediction of stock prices using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from …

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A New Speed Planning Method Based On Predictive Curvature Calculation For Autonomous Driving, Beki̇r Öztürk, Volkan Sezer May 2022

A New Speed Planning Method Based On Predictive Curvature Calculation For Autonomous Driving, Beki̇r Öztürk, Volkan Sezer

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner …

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Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang May 2022

Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang

Research Collection School Of Computing and Information Systems

Multiple-Choice Question Answering (MCQA) is one of the challenging tasks in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In OpenbookQA dataset [1], the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options …

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On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo May 2022

On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural …

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Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo May 2022

Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo

Research Collection School Of Computing and Information Systems

Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …

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Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao May 2022

Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao

Research Collection School Of Computing and Information Systems

Events are fundamental building blocks of realworld happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop …

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Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao May 2022

Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained Language Models (PLMs). It introduces two span selectors based on the prompt to select start/end tokens among input texts for each role. On the other hand, it captures argument interactions via multi-role prompts and conducts joint optimization with optimal span assignments via a bipartite matching loss. Also, with a flexible …

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Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang May 2022

Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang

Research Collection School Of Computing and Information Systems

Translate-train is a general training approach to multilingual tasks. The key idea is to use the translator of the target language to generate training data to mitigate the gap between the source and target languages. However, its performance is often hampered by the artifacts in the translated texts (translationese). We discover that such artifacts have common patterns in different languages and can be modeled by deep learning, and subsequently propose an approach to conduct translate-train using Translationese Embracing the effect of Artifacts (TEA). TEA learns to mitigate such effect on the training data of a source language (whose original and …

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Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua May 2022

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …

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Arseek: Identifying Api Resource Using Code And Discussion On Stack Overflow, Gia Kien Luong, Mohammad Hadi, Thung Ferdian, Fatemeh H. Fard, David Lo May 2022

Arseek: Identifying Api Resource Using Code And Discussion On Stack Overflow, Gia Kien Luong, Mohammad Hadi, Thung Ferdian, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a …

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An Exploratory Study On Code Attention In Bert, Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo May 2022

An Exploratory Study On Code Attention In Bert, Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformerbased Pre-trained Language Models (PLM) trained on code. Although these models achieve the state of the arts results in many downstream tasks such as code summarization and bug detection, they are based on Transformer and PLM, which are mainly studied in the Natural Language Processing (NLP) field. The current studies rely on the reasoning and practices from NLP for these models in code, despite the differences between natural languages and programming languages. There is also limited literature on explaining how code is modeled. …

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Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim May 2022

Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Most works on financial forecasting use information directly associated with individual companies (e.g., stock prices, news on the company) to predict stock returns for trading. We refer to such company-specific information as local information. Stock returns may also be influenced by global information (e.g., news on the economy in general), and inter-company relationships. Capturing such diverse information is challenging due to the low signal-to-noise ratios, different time-scales, sparsity and distributions of global and local information from different modalities. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. …

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Characterization And Mitigation Of False Information On The Web, Anu Shrestha May 2022

Characterization And Mitigation Of False Information On The Web, Anu Shrestha

Boise State University Theses and Dissertations

Social media and Web sources have made information available, accessible, and shareable any time and anywhere nearly without friction. This information can be truthful, falsified, or can only be the opinion of the writer as users in such platforms are both information creators and consumers. In any case, it has the power to affect the decision of an individual, the beliefs of the society, activities, and the economy of the whole country. Thus, it is imperative to identify false information and mitigate the effects of false information that are ubiquitous across the Web and social media. Therefore, the main goal …

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Lexglue: A Benchmark Dataset For Legal Language Understanding In English, Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras May 2022

Lexglue: A Benchmark Dataset For Legal Language Understanding In English, Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras

Research Collection Yong Pung How School Of Law

Lawsandtheirinterpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across …

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Chinese Idiom Understanding With Transformer-Based Pretrained Language Models, Minghuan Tan May 2022

Chinese Idiom Understanding With Transformer-Based Pretrained Language Models, Minghuan Tan

Dissertations and Theses Collection (Open Access)


In this dissertation, I study the understanding of Chinese idioms using transformer-based pretrained language models. By ``understanding", I confine the topics to word embeddings learning, contextualized word representations learning, multiple-choice cloze-test reading comprehension and conditional text generation. Chinese idioms are fixed phrases that have special meanings usually derived from an ancient story. The meanings of these idioms are oftentimes not directly related to their component characters, which makes it hard to model them compared with standard phrases whose meanings are compositional. We initiate the work with studying idiom representations derived from pretrained language models, in particular, BERT. We adopt probing-based …

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Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi May 2022

Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

Research Collection School Of Computing and Information Systems

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin. However, the performance drops dramatically when the input includes abbreviated pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters. We mitigate this issue with two strategies, including enriching the context with pinyin and optimizing the training process to …

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Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang May 2022

Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang

Research Collection School Of Computing and Information Systems

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual …

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Ptm4tag: Sharpening Tag Recommendation Of Stack Overflow Posts With Pre-Trained Models, Junda He, Bowen Xu, Zhou Yang, Donggyun Han, Chengran Yang, David Lo May 2022

Ptm4tag: Sharpening Tag Recommendation Of Stack Overflow Posts With Pre-Trained Models, Junda He, Bowen Xu, Zhou Yang, Donggyun Han, Chengran Yang, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is often viewed as one of the most influential Software Question & Answer (SQA) websites, containing millions of programming-related questions and answers. Tags play a critical role in efficiently structuring the contents in Stack Overflow and are vital to support a range of site operations, e.g., querying relevant contents. Poorly selected tags often introduce extra noise and redundancy, which raises problems like tag synonym and tag explosion. Thus, an automated tag recommendation technique that can accurately recommend high-quality tags is desired to alleviate the problems mentioned above.

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Analyzing Suicidal Text Using Natural Language Processing, Cassandra Barton May 2022

Analyzing Suicidal Text Using Natural Language Processing, Cassandra Barton

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Using Natural Language Processing (NLP), we are able to analyze text from suicidal individuals. This can be done using a variety of methods. I analyzed a dataset of a girl named Victoria that died by suicide. I used a machine learning method to train a different dataset and tested it on her diary entries to classify her text into two categories: suicidal vs non-suicidal. I used topic modeling to find out unique topics in each subset. I also found a pattern in her diary entries. NLP allows us to help individuals that are suicidal and their family members and close …

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Cost-Effective And Collaborative Methods To Author Video's Scene Description For Blind People, Rosiana Natalie May 2022

Cost-Effective And Collaborative Methods To Author Video's Scene Description For Blind People, Rosiana Natalie

Research Collection School Of Computing and Information Systems

The majority of online video content remains inaccessible for blind people due to the lack of audio descriptions. Content creators have traditionally relied on professionals to author audio descriptions, but their service is costly and not readily available. In this research, I introduce four threads of research that I will conduct for my Ph.D. dissertation, aimed to create methods and tools that are both time- and cost-effective in providing good quality audio descriptions. They are: (i) The development and evaluation of mixed-ability collaboration authoring tool, (ii) The formative study to uncover the feedback pattern from the reviewer, (iii) the evaluation …

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