Digital Commons Network^™

Comparing Tagging Suggestion Models On Discrete Corpora, Bojan Bozic, Andre Rios, Sarah Jane Delany

Articles

This paper aims to investigate the methods for the prediction of tags on a textual corpus that describes diverse data sets based on short messages; as an example, the authors demonstrate the usage of methods based on hotel staff inputs in a ticketing system as well as the publicly available StackOverflow corpus. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry.

Analysis Of Automatic Annotations Of Real Video Surveillance Images, Diana Guevara Flores, Fernando Pérez Téllez, David Pinto Avendaño

Articles

The results of the analysis of the automatic annotations of real video surveillance sequences are presented. The annotations of the frames of surveillance sequences of the parking lot of a university campus are generated. The purpose of the analysis is to evaluate the quality of the descriptions and analyze the correspondence between the semantic content of the images and the corresponding annotation. To perform the tests, a fixed camera was placed in the campus parking lot and video sequences of about 20 minutes were obtained, later each frame was annotated individually and a text repository with all the annotations was …

The Ai Author In Litigation, Yvette Joy Liebesman, Julie Cromer Young

All Faculty Scholarship

Many scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.

However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on …

Using Data Analytics To Filter Insincere Posts From Online Social Networks. A Case Study: Quora Insincere Questions, Mohammad A. Al-Ramahi, Izzat Alsmadi

Computer Information Systems Faculty Publications (Archived)

The internet in general and Online Social Networks (OSNs) in particular continue to play a significant role in our life where information is massively uploaded and exchanged. With such high importance and attention, abuses of such media of communication for different purposes are common. Driven by goals such as marketing and financial gains, some users use OSNs to post their misleading or insincere content. In this context, we utilized a real-world dataset posted by Quora in Kaggle.com to evaluate different mechanisms and algorithms to filter insincere and spam contents. We evaluated different preprocessing and analysis models. Moreover, we analyzed the …

Fine-Grained Sentiment Analysis For Measuring Customer Satisfaction Using An Extended Set Of Fuzzy Linguistic Hedges, Asad Khattak, Waqas Tariq Paracha, Muhammad Zubair Asghar, Nosheen Jillani, Umair Younis, Furqan Khan Saddozai, Ibrahim A. Hameed

All Works

© 2020 The Authors. Published by Atlantis Press SARL. In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals and businesses for the making of purchase decisions. Fuzzy-based sentiment analysis aims at classifying customer sentiment at a fine-grained level. This study deals with the development of a fuzzy-based sentiment analysis by extending fuzzy hedges and rule-sets for a more efficient classification of customer sentiment and satisfaction. …

Modeling Twitter Sentiment As A Function Of Particulate Matter 2.5 For Communities Impacted By Wildfire Across Montana And Idaho, Matthew Kelly

Graduate Student Theses, Dissertations, & Professional Papers

Fine particulate matter (PM2.5) is a known pollutant with clinically detrimental physiological and behavioral effects. We consider Twitter sentiment as a potential indicator for well-being in communities impacted by wildfire-associated PM2.5 across Montana and Idaho spanning 5 years (2014-2018). From these geospatial air quality data and geo-tagged tweets, we trained county level models to examine the power of Twitter sentiment as a function of PM2.5. For all 24 counties sampled, we found between 1 and 8 affective dimensions where a positive �� 2 was detected with a significant F-statistic (�� < 0.05). Specifically, we show that sentiment for anticipation in the wildfire-prone county of Missoula, MT yielded respective training/test set �� 2 of 0.0958 and 0.0686 with a p-value for the F-statistic of 3.09E-07. These analyses support social media sentiment as a potential public health metric by showing one of the first observations of a relationship between PM2.5 and Twitter sentiment.

Special Issue On Multi-Criteria Decision Making (Mcdm) And Neutrosophic Tools, Florentin Smarandache, Mohamed Abdel-Basset

Branch Mathematics and Statistics Faculty and Staff Publications

In the rising trends of information technology, the concepts of uncertainty have started gaining greater importance with time in solving operational research problems in supply chain model, project management, transportation problem, or inventory control problems. Moreover, day-by-day competition is becoming tougher in imprecise environments. For instance, customer demand is often being affected by several varying factors like production price, income level, and the like. In these cases, the demand either remains unfulfilled or is difficult to obtain with certainty in the real-world market. Fuzzy sets are not always able to directly depict such uncertainties because they exhibit numeric only membership …

New Challenges In Neutrosophic Theory And Applications, Florentin Smarandache, Stefan Vladutescu, Miihaela Colhon, Wadei Al-Omeri, Saeid Jafari, Muhammad Zahir Khan, Muhammad Farid Khan, Muhammad Aslam, Abdur Razzaque Mughal

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic theory has representatives on all continents and, therefore, it can be said to be a universal theory. On the other hand, according to the three volumes of “The Encyclopedia of Neutrosophic Researchers” (2016, 2018, 2019), plus numerous others not yet included in Encyclopedia book series, about 1200 researchers from 73 countries have applied both the neutrosophic theory and method. Neutrosophic theory was founded by Professor Florentin Smarandache in 1998; it constitutes further generalization of fuzzy and intuitionistic fuzzy theories. The key distinction between the neutrosophic set/logic and other types of sets/logics lies in the introduction of the degree of …

Improved, Extended, And Total Impact Factor Of A Journal, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this short paper we recall the (Garfield) Impact Factor of a journal, we improve and extend it, and eventually present the Total Impact Factor that reflects the most accurate impact factor.

Mother Nature, Lady Justice: Ecofeminism And Judicial Decision-Making, Jonathan Alexis Picado

Open Access Theses & Dissertations

Ecofeminism offers a feminist perspective that links gender to how humans relate to the natural world. As such, this framework explores the connections between the oppression of nature and the oppression of women, such as widespread views that both women and nature are property, are to be dominated, and are most valuable when cultivated and curated by men. I apply this philosophical and sociological framework to judicial decision-making, where women judges should view environmental issues as women's issues and thus be more likely to vote in favor of the environmental protections relative to her male peers. I evaluate this theory …

The Stained Glass Of Knowledge: On Understanding Novice Mental Models Of Computing, Briana Christina Bettin

Dissertations, Master's Theses and Master's Reports

Learning to program can be a novel experience. The rigidity of programming can be at odds with beginning programmer's existing perceptions, and the concepts can feel entirely unfamiliar. These observations motivated this research, which explores two major questions: What factors influence how novices learn programming? and How can analogy by more appropriately leveraged in programming education?

This dissertation investigates the factors influencing novice programming through multiple methods. The CS1 classroom is observed as a "whole system", with consideration to the factors present in it that can influence the learning process. Learning's cognitive processes are elaborated to ground exploration into specifically …

Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard

Research Collection School Of Computing and Information Systems

Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technologies (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., …

Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …

Key Strategies For Successful Information Technology Projects, David Morris

Walden Dissertations and Doctoral Studies

AbstractThe successful implementation of information technology (IT) projects globally depends on the increasing reliance of project managers on IT innovations. Some project managers and business leaders do not possess the strategies to effectively manage and successfully implement IT projects. Grounded in coordination theory, the purpose of this qualitative multiple case study was to explore strategies used to successfully implement IT projects. Data were collected using semistructured interviews and from company documents. The participants comprised 6 IT project managers in Northeast Ohio who successfully implemented IT projects. Thematic analysis was used to analyze the data. Four themes emerged: communication amongst all …

Psu At Clef-2020 Arqmath Track: Unsupervised Re-Ranking Using Pretraining, Shaurya Rohatgi, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

This paper elaborates on our submission to the ARQMath track at CLEF 2020. Our primary run for the main Task-1: Question Answering uses a two-stage retrieval technique in which the first stage is a fusion of traditional BM25 scoring and tf-idf with cosine similarity-based retrieval while the second stage is a finer re-ranking technique using contextualized embeddings. For the re-ranking we use a pre-trained robertabase model (110 million parameters) to make the language model more math-aware. Our approach achieves a higher NDCG0 score than the baseline, while our MAP and P@10 scores are competitive, performing better than the best submission …

Smartcitecon: Implicit Citation Context Extraction From Academic Literature Using Unsupervised Learning, Chenrui Gao, Haoran Cui, Li Zhang, Jiamin Wang, Wei Lu, Jian Wu

Computer Science Faculty Publications

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers in the ACL Anthology. The model with 19 features achieves F₁=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a …

Acknowledgement Entity Recognition In Cord-19 Papers, Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, Christopher Griffin

Computer Science Faculty Publications

Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F₁=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to …

Opening Books And The National Corpus Of Graduate Research, William A. Ingram, Edward A. Fox, Jian Wu

Computer Science Faculty Publications

Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. …

Professional Development For Mathematics Teacher Education Faculty: Need And Design, Billy Jackson, Shandy Hauk, Jenq Jong Tsay, Alma Ramirez

School of Mathematical & Statistical Sciences Faculty Publications

The purpose of this report is to share a conceptual model useful in the design of professional learning about teaching for university mathematics faculty. The model is illustrated by examples from a particular design effort: the development of an online shortcourse for faculty new to teaching mathematics courses for prospective primary school teachers. How novice mathematics teacher educators grow as instructors is an emerging area of research and development in the United States. At the same time, it is well established that effective instructional design of any course, including a course for faculty, requires breadth first: understanding and anticipating the …

Pretraining Deep Learning Models For Natural Language Understanding, Han Shao

Honors Papers

Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerged in 2018, researchers have started to study and use pretrained bidirectional autoencoding or autoregressive models to solve language problems. In this project, I conducted research to fully understand BERT and XLNet and applied their pretrained models to two language tasks: reading comprehension (RACE) and part-of-speech tagging (The Penn Treebank). After experimenting with those released models, I implemented my own version of ELECTRA, a pretrained text encoder as a discriminator instead of a generator to improve compute-efficiency, with BERT as its underlying architecture. To reduce the number …

Strategies For Managing Offshore Outsourcing For Business Sustainability, Idrissa Maiga Hamidou Issa

Walden Dissertations and Doctoral Studies

Ineffective offshore outsourcing strategies can contribute to unexpected and significant costs for businesses. Strategies to improve outsourcing outcomes are critical to business managers because often third parties contracted to implement offshore outsourcing fail to fulfil business managers’ expectations who can benefit from understanding how to manage outsourced operations and anticipate potential problems. Grounded in transaction cost theory, cultural and customs theory, and the resource-based view theory, the purpose of this qualitative single case study was to explore strategies and processes business managers use to maintain their organization after the implementation of offshore outsourcing. The participants consisted of 3 managers who …

Perceptions, Potholes, And Possibilities Of Using Digital Voice Assistants To Differentiate Instructions, Adrian A. Weir

Walden Dissertations and Doctoral Studies

Access to technologies and understanding the potential uses of technology to differentiate instruction have been a concern for the teachers and students in a local school district located in the southeastern United States. Despite the emergence of digital voice assistants (DVAs) as tools for instructions, teachers lack knowledge and strategies for using DVAs to differentiate instruction in their classrooms. The purpose of this qualitative study was to identify teacher knowledge and strategies employed among special education (SPED) teachers using DVAs to differentiate instruction in their classrooms. The concepts of Carol Tomlinson’s differentiation theory and Mishra and Koehler’s TPACK framework served …

Assessing Environmental Impact Through Dpsir Framework And Environmental Justice Lenses:A Case Study Of Inland Capture Fisheries In Mahakam River, Etik Sulistiowati Ningsih

Chulalongkorn University Theses and Dissertations (Chula ETD)

Inland capture fisheries significantly contribute to the achievement of SDG, but most inland capture fisheries are poorly managed or not managed at all. Inland fishery is often facing against the big-scale industrial project. In the Middle Mahakam Area, two dominant natural resources exploited for the industrial project are palm oil and coal mining. Palm oil production, coal mining production, the human population was increasing in the last ten years, and on the opposite, the water quality index is decreasing. However, although many previous studies suggested that fish catches are declining, fisheries statistics show the opposite data. Government statistics show that …

A Robust System For Core Thai Natural Language Processing Technologies, Can Udomcharoenchaikit

Chulalongkorn University Theses and Dissertations (Chula ETD)

As the amount of unstructured textual data grows, it becomes increasingly important to build an intelligent system that can process it. Natural Language Processing (NLP) is a technology that allows a computer to exploit human languages to perform tasks. Deep learning models have shown excellent results across fundamental tasks in NLP, such as word segmentation, part-of-speech tagging, and named-entity recognition. However, in many situations, these proposed methods fail to perform well. For an NLP system to be robust, it must address issues such as out-of-vocabulary and spelling-mistakes. This thesis's research goal is to develop NLP models that can handle malformed …

Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations, Chanatip Saetia

Chulalongkorn University Theses and Dissertations (Chula ETD)

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, …

Explainable Stock Price Prediction Using Technical Indicators With Short Thai Textual Information, Kittisak Prachyachuwong

Chulalongkorn University Theses and Dissertations (Chula ETD)

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To …

Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca), Pimarn Kantithammakorn

Chulalongkorn University Theses and Dissertations (Chula ETD)

The Montreal Cognitive Assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning where scores are based on the number of unique correct words produced by the test-taker. However, with different languages, it is possible that unique words may be counted differently. This study focuses on Thai as a language that differs from English in its type of word combination. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the language fluency test of the MoCA with Thai language support. The …

การค้นคืนข้ามภาษาสำหรับคำทับศัพท์ภาษาไทย/อังกฤษโดยใช้ทรานฟอร์เมอร์, อภิชัจจ์ โชดกวณิชย์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การค้นคืนข้ามภาษานั้นเป็นงานที่ท้าทายในวิทยาการด้านการประมวลผลภาษาธรรมชาติของไทย ด้วยเหตุผลในด้านของความแตกต่างระหว่างภาษา เช่น การออกเสียง และ กฎการทับศัพท์ วิทยานิพนธ์เล่มนี้ได้นำเสนอ ขั้นตอนวิธีการค้นคืนข้ามภาษาสำหรับคำทับศัพท์ภาษาไทย/อังกฤษโดยใช้ทรานฟอร์เมอร์ วิธีการที่นำเสนอนี้ช่วยให้สามารถค้นคืนคำทับศัพท์ข้ามภาษาได้โดยไม่ต้องอาศัยพจนานุกรม ซึ่งการค้นคืนข้ามภาษาโดยไม่อาศัยพจนุกรมนั้นจำเป็นต้องใช้หลักการเข้ารหัสเสียงซึ่งเป็นสัญลักษณ์แทนเสียงอ่านของคำ จากผลการทดลองของโมเดลการเรียนรู้แบบกึ่งสอน (Semi-supervised) ด้วยวิธี K-Fold cross validation แสดงให้เห็นว่า ขั้นตอนวิธีการเข้ารหัสคำที่นำเสนอให้ค่าเฉลี่ยของค่าแม่นยำ ค่าเรียกคืน และค่า F1 อยู่ที่ 85.08%, 88.25% และ 86.63% ตามลำดับ สำหรับชุดข้อมูลภาษาไทย และค่าเฉลี่ยของค่าแม่นยำ ค่าเรียกคืน และค่า F1 ของชุดข้อมูลภาษาอังกฤษอยู่ที่ 80.44%, 87.15% และ 83.66% ตามลำดับ

การพัฒนาตัวแบบรู้จำบุคลิกภาพจากเสียงสนทนาในบริบทของศูนย์ให้บริการข้อมูลลูกค้า, นคร ศรีณรงค์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ศูนย์บริการข้อมูลลูกค้าเป็นช่องทางสำคัญในการติดต่อระหว่างภาคธุรกิจและลูกค้า ตัวชี้วัดที่สำคัญของการทำงานของศูนย์บริการข้อมูลลูกค้าคือความพึงพอใจของลูกค้า ข้อมูลหลักที่ได้จากศูนย์บริการข้อมูลลูกค้าคือเสียงสนทนา ทำให้ผู้วิจัยสนใจที่จะศึกษาแนวทางในการสร้างตัวแบบรู้จำบุคลิกภาพจากเสียง ข้อมูลเสียงและเพศถูกเก็บจากหน่วยตัวอย่าง 92 คน พร้อมกับข้อมูลบุคลิกภาพโดยใช้แบบวัด MPI (Maudsley Personality Inventory) แบบวัดดังกล่าวแบ่งบุคลิกภาพออกเป็น 2 ด้าน คือด้าน E-scale (Extraversion และ Introversion) และ N-scale (Neuroticism และ Stability) ซึ่งนำมาใช้ในการพัฒนาตัวแบบจำแนกบุคลิกภาพทั้งสองด้าน เทคนิคที่ใช้ในการพัฒนาตัวแบบประกอบด้วย Logistic regression, SVM, Random forest และ Artificial neural network โดยพบว่าตัวแบบที่พัฒนาด้วยเทคนิค Artificial neural network มีประสิทธิภาพสูงสุดในการรู้จำ E-scale โดยมีค่า Positive predictive value (ค่าวัดประสิทธิภาพของ Introversion) เท่ากับ 0.71 และค่า Negative predictive value (ประสิทธิภาพของ Extraversion) เท่ากับ 0.75 ในส่วนของ N-scale ไม่พบตัวแบบที่พัฒนาด้วยเทคนิคใดมีประสิทธิภาพเพียงพอ ในการศึกษาครั้งนี้พบว่าบุคลิกภาพ Extraversion และ Introversion ซึ่งสามารถนำมาประยุกต์กับงานภาคธุรกิจ สามารถรู้จำจากเสียงสนทนาในบริบทศูนย์บริการข้อมูลลูกค้า โดยสามารถนำไปใช้มอบหมายพนักงานที่มีบุคลิกภาพเหมือนกับลูกค้าเพื่อเพิ่มความพึงพอใจของลูกค้าในการติดต่อสื่อสาร ภาคธุรกิจยังสามารถนำข้อมูลบุคลิกภาพเหล่านี้ไปใช้ต่อยอดในการแนะนำผลิตภัณฑ์ หรือโฆษณา ให้เหมาะสมกับลูกค้าแต่ละคน