Digital Commons Network^™

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 …

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt

Browse all Theses and Dissertations

Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …

Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad

Electronic Theses and Dissertations, 2020-2023

Successful software authorship identification has both software forensics applications and privacy implications. However, the process requires an efficient extraction of quality authorship attributes. The extraction of such attributes is very challenging due to several factors such as the variety of software formats, number of available samples, and possible obfuscation or adversarial manipulation. We focus on software authorship identification from three central perspectives: large-scale single-authored software, real-world multi-authored software, and the robustness assessment of code authorship identification methods against adversarial attacks. First, we propose DL-CAIS, a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software …

Learning Transferable Representations For Visual Recognition, Yang Zhang

Electronic Theses and Dissertations, 2020-2023

In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable …

Chemical Disease Relation Extraction Task Using Genetic Algorithm With Two Novelvoting Methods For Classifier Subset Selection, Stanley Chika Onye, Nazi̇fe Di̇mi̇li̇ler, Ari̇f Akkeleş

Turkish Journal of Electrical Engineering and Computer Sciences

Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system (MCS) for the extraction of chemical-induced disease relations. A genetic algorithm (GA) is employed to select classifier ensembles from a pool of base classifiers. Moreover, the voting method used for combining the members of each of the ensembles is also selected during evolution in the GA framework. The performances of the MCSs are determined by the algorithms used for selecting the classifiers, the diversity among the selected classifiers, and the voting method used in the classifier combination. The …

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

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 …

Using Prosody To Spot Location Mentions, Gerardo Cervantes

Open Access Theses & Dissertations

Identifying location mentions in speech is important for many information retrieval and information extraction tasks; here I explore the use of prosody for location spotting. While previous work has explored the use of prosody for spotting named entities, including locations, the specific value of prosody for finding locations in spontaneous speech has not been measured. Using the Switchboard corpus and LSTM modeling I obtain results indicating that prosody is useful in spotting location mentions. Further, I identify specific prosodic

features that tend to mark locations in American English.

“Sorry I Didn’T Hear You.” The Ethics Of Voice Computing And Ai In High Risk Mental Health Populations, Fazal Khan, Christopher Villongco

Scholarly Works

This article examines the ethical and policy implications of using voice computing and artificial intelligence to screen for mental health conditions in low income and minority populations. Mental health is unequally distributed among these groups, which is further exacerbated by increased barriers to psychiatric care. Advancements in voice computing and artificial intelligence promise increased screening and more sensitive diagnostic assessments. Machine learning algorithms have the capacity to identify vocal features that can screen those with depression. However, in order to screen for mental health pathology, computer algorithms must first be able to account for the fundamental differences in vocal characteristics …

Human Face Sketch To Rgb Image With Edge Optimization And Generative Adversarial Networks, Feng Zhang, Huihuang Zhao, Wang Ying, Qingyun Liu, Alex Noel Joseph Raj, Bin Fu

Computer Science Faculty Publications

Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on …

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Named Entity Recognition Based On A Graph Structure, David Muñoz, Fernando Pérez Téllez, David Pinto

Articles

The identification of indirect relationships between texts from different sources makes the task of text mining useful when the goal is to obtain the most valuable information from a set of texts. That is why in the field of information retrieval the correct recognition of named entities plays an important role when extracting valuable information in large amounts of text. Therefore, it is important to propose techniques that improve the NER classifiers in order to achieve the correct recognition of named entities. In this work, a graph structure for storage and enrichment of named entities is proposed. It makes use …

Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures, Abhijit Mahalunkar, John Kelleher

Conference papers

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Brexit: Psychometric Profiling The Political Salubrious Through Machine Learning: Predicting Personality Traits Of Boris Johnson Through Twitter Political Text, James Usher, Pierpaolo Dondio

Conference papers

Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN …

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 …

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 …

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 …

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. …

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 …