Digital Commons Network^™

Unlearning Hidden Bias Between Refugees : An Initial Empirical Investigation, Akshay Sunil Gurnaney

Master's Projects

The challenges that refugees in various regions encounter are common knowledge. One such challenge is a bias among refugees on ethnocentric grounds. In particular, there are various articles that have pointed out the struggles faced by Syrian refugees in countries like Europe as a result of implicit bias. In fact, the media coverage of Syrian crises and the government responses to the same shed negative light on the refugees themselves. On the contrary, the media coverage of Ukrainian crises is very different with lesser restrictions from the governments.

This research attempts to identify the extent of implicit bias between Ukrainian …

Temporal Dilation In Video Resnet For Sign Language Translation, Xiaoqian Yang

Master's Projects

Sign languages, vital for communication among the deaf and hard-of-hearing (DHH) people, face a significant linguistic diversity challenge with over 200 distinct sign languages worldwide. Bridging this communication gap is a priority. Traditional tools like interpreters and costly translation devices have limitations. This project aims to use deep learning techniques to develop a model capable of recognizing sign language from short videos. Our model not only recognizes the sign from a single video clip, but is also capable of making prediction of consecutive pairs of signs. To achieve zero-short gesture sequence recognition, we propose a novel temporal dilation strategy, converting …

Ensemble Transformer Architecture For Efficient And Real Time Sign Language Translation, Sumeet Ghegade

Master's Projects

Sign language is a form of visual language that uses face expression and hand gestures to communicate thoughts and concepts. The term refers to multiple visual languages that share some common visual cues but differ in their grammar and syntax. Sign language translation (SLT) is a crucial step in closing the communication gap between hearing and hearingimpaired people. The study of SLT using machine learning has gotten a lot of interest during the last three years but despite progress, SLT research is still in its early phases. Most of the previous approaches first convert the signs to glosses and then …

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

Development And Analysis Of A Game Play In Chulalongkorn University Central Library Metaverse, Aisha Munir

Chulalongkorn University Theses and Dissertations (Chula ETD)

Metaverse is the next generation of the internet that allows people to interact with digital content in a more immersive and interactive way. This paradigm shift extends to the realm of education, where the concept of a smart campus is reshaping traditional educational systems. This research focuses on the integration of the metaverse in education, with a specific emphasis on smart libraries. Recognizing libraries as integral components of educational systems, this study proposes a metaverse platform for Chulalongkorn University's central library. The proposed platform enables users to explore the virtual library remotely and simulate resource classification and physical service in …

Prioritized Object Detection Integrating Fuzzy Logic Risk Assessment And Planned Path., Khatereh Mohammadi

Graduate Research Theses & Dissertations

Nowadays, Autonomous Vehicles are in the spotlight as a response to the challenges stemming from population growth, worsening traffic congestion, and rising road accidents. Researchers and companies are concentrating on developing Automated Driving Systems (ADS) to tackle these issues.Of the various components that constitute an autonomous vehicle, perception plays a pivotal role, akin to the function of human eyes and the human brain. Therefore, ensuring precise and reliable perception within Autonomous Vehicles is paramount. This thesis is inspired by the principles of human perception, particularly on the road, and endeavors to replicate this process. Just as a human driver does …

The Employees’ Perspective: Situational Leadership Style Flexibility And Effectiveness And Employee Performance In A Technological Organization, Merari Cortes

Walden Dissertations and Doctoral Studies

Poor employee performance can have adverse effects on business outcomes. Business leaders are concerned with poor performance, which can minimize profitability and negatively impact business sustainability. Grounded in the Situational Leadership II® model, the purpose of this quantitative correlational study was to examine the relationship between situational leadership style flexibility, effectiveness, and employee performance as perceived by employees, controlling for employee gender, job location, and tenure. A random sample of 99 technology company employees completed the Leader Behavior Analysis II®– Other and the Employee Job Performance measurement tools. Using hierarchical multilinear regression, employee gender, job location, and tenure were entered …

Groundwater Potential Mapping At Highly Populated Sub-Districts Of Thimphu District Using Fuzzy Analytic Hierarchy Process, Thongley Thongley, Tshering Choki, Kuenzang Choden

Applied Environmental Research

Groundwater is important when there is an insufficient spring water supply for daily needs. The population growth and expansion of the cities demand more water. Thimphu is the capital city of Bhutan and facing water shortage in recent times due to urban expansion and population growth. This study aims to find the potential area for groundwater using the Fuzzy Analytic Hierarchy Process. The factors used for this study are elevation, slope, curvature, topographic wetness index, drainage density, normalized difference vegetation index, distance from fault density, rainfall distribution map, soil texture map, and drainage density. The rating for the factors and …

Research On Improving The Salary Structure Of Port State Control Officers In Taiwan, Te-Yi Chang, Wen-Chih Ho, Chung-Ping Liu, Yung-Wei Chen

Journal of Marine Science and Technology–Taiwan

The implementation of port state control (PSC) has been challenging in Taiwan due to the lack of port state control officers (PSCOs), and foreign ships berthing at ports in Taiwan that cannot be effectively inspected. PSCOs of Taiwan are subordinate to the public service system and restrained by the number of personnel by the Executive Yuan. Compared with similar professions, their salaries are significantly different from those of the industry, making recruiting and retaining PSCOs a difficult task. Accordingly, this study improved the salary structure of PSCOs by conducting field interviews with existing PSCOs in Taiwan and applying the Fuzzy-AHP …

Assessing Hierarchical Corporate Sustainability Transition Practices Under Uncertainty: An Approach In The Port And Shipping Industry In Southeast Asia, Mei-Train Yeh, Feng-Ming Tsai, Taufik Kurrahman

Journal of Marine Science and Technology–Taiwan

The port and shipping industry in Southeast Asia has experienced significant development in response to the increasing demand for maritime logistics and transportation services. However, this expansion has resulted in notable social and environmental impacts. Consequently, it is imperative for both scholars and practitioners to comprehend the concept of corporate sustainability transition (CST), which entails the establishment of shipping operations that effectively balance environmental, social, and economic considerations. Additionally, technologies that are used to facilitate the transition process must be investigated. Several studies have been undertaken to ascertain the attributes pertaining to sustainability transition. Nevertheless, there has been a lack …

Cook-Gen: Robust Generative Modeling Of Cooking Actions From Recipes, Revathy Venkataramanan, Kaushik Roy, Kanak Ray, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth

Publications

As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recipe instructions to assess nutritional contents and provide recipe recommendations. The recent and remarkable successes of generative AI methods, such as auto-regressive large language models, can lead to robust methods for a more comprehensive understanding of recipes for healthy food recommendations beyond surface-level nutrition content assessments. In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and …

Tutorial - Shodhguru Labs: Optimization And Hyperparameter Tuning For Neural Networks, Kaushik Roy

Publications

Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class …

การพัฒนาขั้นตอนวิธีสำหรับการจับคู่ชื่อสินค้าที่มีรูปแบบการตั้งชื่อที่หลากหลายโดยใช้การประมวลผลภาษาธรรมชาติ, สหฤษฎ์ เจียมสกุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในขอบเขตของการซื้อขายสินค้าระหว่างองค์กร ความท้าทายที่สำคัญเกิดขึ้นเมื่อต้องจับคู่คำสั่งซื้อของลูกค้ากับชื่อสินค้าในฐานข้อมูล ซึ่งชื่อสินค้าเหล่านี้มีรูปแบบการตั้งชื่อที่หลากหลาย วิทยานิพนธ์นี้จะกล่าวถึงความซับซ้อนของการจับคู่ชื่อสินค้าภายในบริษัท เจนบรรเจิด จำกัด ซึ่งเป็นบริษัทที่เชี่ยวชาญในการขายสินค้าสำหรับธุรกิจบริการ ปัจจุบันวิธีการที่ใช้แก้ปัญหานี้คือการใช้พนักงานเปรียบเทียบคำสั่งซื้อของลูกค้ากับชื่อสินค้าในฐานข้อมูลทีละรายการ ซึ่งเป็นกระบวนการที่สิ้นเปลืองแรงงานและเวลาเป็นอย่างมาก วิทยานิพนธ์นี้จึงนำเสนอการใช้เทคนิคการประมวลผลภาษาธรรมชาติ (NLP) เพื่อแก้ปัญหาดังกล่าว โดยเริ่มจากการใช้ Pointwise Mutual Information (PMI) ในการคัดเลือกคำสำคัญในพจนานุกรมแบบกำหนดเอง เพื่อการตัดคำภาษาไทยที่เหมาะสมกับชุดข้อมูล จากนั้นนำระบบตัดคำที่ปรับปรุงแล้วมาใช้ในการออกแบบขั้นตอนวิธีสำหรับการสกัดคำหลัก รวมถึงการใช้เทคนิคการวิเคราะห์ความหมายแฝง (LSA) เพื่อค้นหาคำที่คล้ายกับคำค้นหา สุดท้ายจึงนำส่วนต่างๆ มารวมเข้ากับการใช้เทคนิคการจับคู่ข้อความโดยประมาณ จนเกิดเป็นขั้นตอนวิธีสำหรับจับคู่รายชื่อสินค้า ขั้นตอนวิธีที่นำเสนอสามารถทำงานได้อย่างมีประสิทธิภาพ โดยมีความแม่นยำในการระบุผลลัพธ์มากถึง 93.80% และมีความครอบคลุมมากถึง 91.67% ในการทดสอบจับคู่กับชุดตัวอย่างคำสั่งซื้อจริงจากลูกค้า

การตัดสินใจหลายเกณฑ์แบบไฮบริดเพื่อหาคู่ค้าพันธมิตรเชิงกลยุทธ์ในอุตสาหกรรมสารกึ่งตัวนำ, จิรยุ พุดพวง

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอแนวทางในการคัดเลือกคู่ค้าเชิงกลยุทธ์ในอุตสาหกรรมสารกึ่งตัวนำ โดยใช้วิธีการผสมผสานระหว่างตรรกศาสตร์คลุมเครือ กระบวนการลำดับชั้นเชิงวิเคราะห์ (F-AHP) และวิธีการจัดลำดับความสำคัญโดยพิจารณาจากความคล้ายคลึงกันในอุดมคติ (F-TOPSIS) เพื่อจัดการกับความกำกวมและความไม่แน่นอนในการตัดสินใจของมนุษย์ กระบวนการไฮบริดนี้ประกอบด้วยหลายขั้นตอนหลัก ได้แก่ การศึกษาขั้นตอนการตัดสินใจและการคัดเลือกเกณฑ์จากวรรณกรรมและผู้เชี่ยวชาญ การวิเคราะห์ข้อมูลการจัดซื้อ การประเมินความสำคัญของเกณฑ์โดยใช้ F-AHP และการจัดอันดับทางเลือกโดยใช้ F-TOPSIS พร้อมทั้งการวิเคราะห์ความไว (Sensitivity Analysis) ของเกณฑ์ต่าง ๆ เพื่อประเมินความเสถียรของผลลัพธ์ ผลการศึกษาจากกรณีศึกษาพบว่าวิธีการนี้สามารถลดการสูญเสียรายได้อย่างมีนัยสำคัญ ประมาณ 3.2 ล้านดอลลาร์ และช่วยลดความเสี่ยงในการเลือกคู่ค้าที่ไม่เหมาะสม ซึ่งอาจส่งผลต่อประสิทธิภาพการดำเนินงานและความสามารถในการแข่งขันในตลาด นอกจากนี้ยังส่งผลให้มีการประหยัดต้นทุน เพิ่มความพึงพอใจของลูกค้า และปรับปรุงประสิทธิภาพในการส่งมอบสินค้าและคุณภาพของผลิตภัณฑ์ได้อย่างดีเยี่ยม อีกทั้งยังมีการพัฒนาระบบสนับสนุนการตัดสินใจที่สามารถปรับเปลี่ยนและนำไปใช้ในอุตสาหกรรมอื่นๆ ที่มีลักษณะการตัดสินใจที่ซับซ้อนเช่นเดียวกัน

Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio

Articles

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

The Role Of Teacher Agency In Using Gis To Teach Sustainability: An Evaluation Of A Lower Secondary School Story Mapping Gis Initiative In Ireland, Sasha Brown, Zerrin Doganca Kucuk, Angela Rickard, James Lonergan, Rachel Abernethy, Lorraine Mcnerney, Ronan Foley, Avril Behan, Aengus Byrne, Bernard Essel, Jack Ffrench, Jonathan Faull, Conor Cahalane

Articles

This paper investigates the role of teacher agency for those who were trained in using an innovative Geographic Information Systems (GIS) approach and incorporated GIS into their teaching. While national curricula increasingly emphasise STEM and sustainability approaches, there has been limited analysis of how teachers can bring new software, including GIS, and new approaches into the classroom. Through an evaluation of a teaching initiative “5*S: Space, Surveyors and Students – STEM and the Sustainable Development Goals” designed to bring GIS technology, satellite data, and the Sustainable Development Goals (SDGs) into classrooms as activities and resources for teachers and students in …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because of a lack of training on safety-constrained and specialized clinical process knowledge (Pro-Know). In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the …

Queer In Ai: A Case Study In Community-Led Participatory Ai, Anaelia Ovalle, Arjun Subramonian, Ashwiin Singh, Claas Voelcker, Danica Sutherland, Davide Locatelli, Eva Breznik, Felip Klubicka, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melind Agarwal, Nyx Mclean, Pan Xu, A. Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, S.T. John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew Mcnamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark

Conference papers

Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive …

Tutorial - Shodhguru Labs: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

The Troubling Emergence Of Hallucination In Large Language Models--An Extensive Definition, Quantification, And Prescriptive Remediations, Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das

Publications

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a finegrained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) …

Performance Improvement Of Hybrid System Based Dfig-Wind/Pv/Batteries Connected To Dc And Ac Grid By Applying Intelligent Control, Younes Sahri, Salah Tamalouzt, Sofia Lalouni Belaid, Mohit Bajaj, Sherif S.M. Ghoneim, Hossam Zawbaa, Salah Kamel

Articles

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Screw Loose Toolbox, Anton Schaefer

Practice Papers

Blue Engineering is a student-driven course on the environmental and social responsibility of engineers. It has been developed by student initiatives at two German universities since 2010. By 2023, there are more than 15 courses at universities in Germany and in the Netherlands using the open source course design. In assessing the learning outcomes of the participants, the need to promote the skill of transfer of learning of the students became clear. This practice paper presents the current approach: 24 metaphorical tools have been developed, each of which functions like a special lens, allowing to recognise certain patterns of action, …

Detection Of Truthful, Semi-Truthful, False And Other News With Arbitrary Topics Using Bert-Based Models, Elena Shushkevich, John Cardiff, Anna Boldyreva

Conference Papers

Easy and uncontrolled access to the Internet provokes the wide propagation of false information, which freely circulates in the Internet. Researchers usually solve the problem of fake news detection (FND) in the framework of a known topic and binary classification. In this paper we study possibilities of BERT-based models to detect fake news in news flow with unknown topics and four categories: true, semi-true, false and other. The object of consideration is the dataset CheckThat! Lab proposed for the conference CLEF-2022. The subjects of consideration are the models SBERT, RoBERTa, and mBERT. To improve the quality of classification we use …

Show, Prefer And Tell: Incorporating User Preferences Into Image Captioning, Annika Lindh, Robert J. Ross, John Kelleher

Conference papers

Image Captioning (IC) is the task of generating natural language descriptions for images. Models encode the image using a convolutional neural network (CNN) and generate the caption via a recurrent model or a multi-modal transformer. Success is measured by the similarity between generated captions and human-written “ground-truth” captions, using the CIDEr [14], SPICE [1] and METEOR [2] metrics. While incremental gains have been made on these metrics, there is a lack of focus on end-user opinions on the amount of content in captions. Studies with blind and low-vision participants have found that lack of detail is a problem [6, 13, …

How Online Discourse Networks Fields Of Practice: The Discursive Negotiation Of Autonomy On Art Organisation About Pages, Tommie Soro

Articles

This article examines how the online discourse of art organisations forges relationships between the artworld and the fields of politics and economy. Combining elements of Pierre Bourdieu’s field analysis and Norman Fairclough’s critical discourse analysis, the article analyses an elite art magazine, e-flux, and an elite art museum, IMMA, and the activities of discourses, genres, and utterances on their about pages. Its results suggest that the about pages of these organisations forge links between the artworld and the fields of politics and economy by mobilising discourse in these fields and by incorporating discourse practices from these fields. The ideological tension …

Identifying Design-Build Decision-Making Factors And Providing Future Research Guidelines: Social Network And Association Rule Analysis, Ramy Khalef, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

There is a dire need to rebuild existing infrastructure with strategic and efficient methods. Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more time and cost-efficient project delivery method. Past research studied factors influencing DB but without providing a holistic analytic approach. This paper fills this knowledge gap. First, a systematic literature review is performed using the preferred reporting items for systematic reviews and meta-analyses techniques, and a set of factors affecting DB projects are then identified and clustered, using k-means clustering, based on the whole literature discussions. Second, a graph theory approach, social network analysis …

Robustembed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training, Javad Asl, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have demonstrated their exceptional performance across a wide range of natural language processing tasks. The utilization of PLM-based sentence embeddings enables the generation of contextual representations that capture rich semantic information. However, despite their success with unseen samples, current PLM-based representations suffer from poor robustness in adversarial scenarios. In this paper, we propose RobustEmbed, a self-supervised sentence embedding framework that enhances both generalization and robustness in various text representation tasks and against diverse adversarial attacks. By generating high-risk adversarial perturbations to promote higher invariance in the embedding space and leveraging the perturbation within a novel contrastive …

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Enabling Cybersecure And Resilient Smart Distribution Grid With Edge Devices, Partha Sarathi Sarker

Graduate Theses, Dissertations, and Problem Reports (ETD)

The smart distribution grid achieves enhanced efficiency and flexibility through the utilization of advanced communication infrastructure, digital devices, and robust computation and control capabilities. Grid-edge devices including distributed energy resources (DERs) like solar photovoltaics (PV), battery storage systems, and intelligent electric loads like electric vehicles and smart appliances, are contributing to the growth of the smart distribution grid. This evolution is transforming the grid into a multifaceted network of interconnected devices and systems, enabling bidirectional data communication and power flows. However, these new layers of data integration and control in the smart grid introduce vulnerabilities to cyberattacks and accidental failures, …