Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1107)
- TÜBİTAK (199)
- Wright State University (191)
- Technological University Dublin (182)
- Old Dominion University (126)
-
- Missouri University of Science and Technology (112)
- San Jose State University (111)
- City University of New York (CUNY) (92)
- Neutrosophic Systems with Applications (89)
- MBZUAI (70)
- Dartmouth College (67)
- Brigham Young University (65)
- University of Texas at El Paso (63)
- Chulalongkorn University (62)
- University of Nebraska - Lincoln (54)
- Zayed University (54)
- Kennesaw State University (49)
- Boise State University (48)
- Edith Cowan University (48)
- New Jersey Institute of Technology (44)
- California Polytechnic State University, San Luis Obispo (43)
- Embry-Riddle Aeronautical University (42)
- Purdue University (41)
- Chapman University (40)
- University of Nebraska at Omaha (40)
- University of Arkansas, Fayetteville (38)
- Montclair State University (36)
- University of South Florida (36)
- Air Force Institute of Technology (35)
- MMU Press (35)
- Keyword
-
- Machine learning (184)
- Natural language processing (182)
- Artificial intelligence (140)
- Natural Language Processing (131)
- Machine Learning (125)
-
- Deep learning (118)
- Artificial Intelligence (94)
- Large language models (84)
- Social media (80)
- Computer Science (78)
- Twitter (64)
- Deep Learning (61)
- NLP (60)
- Sentiment analysis (59)
- Large Language Models (56)
- Computational linguistics (53)
- Department of Computer Science and Engineering (53)
- Text mining (51)
- Neural networks (48)
- Data mining (44)
- Generative AI (44)
- AI (42)
- Computer science (39)
- Classification (37)
- Information retrieval (36)
- Social Media (36)
- Fuzzy logic (35)
- Semantics (34)
- Sentiment Analysis (33)
- LLM (32)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1032)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Theses and Dissertations (160)
- Master's Projects (99)
- Conference papers (93)
-
- Neutrosophic Systems with Applications (89)
- Kno.e.sis Publications (84)
- Dissertations (70)
- Computer Science Faculty Research & Creative Works (66)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (62)
- Browse all Theses and Dissertations (61)
- Computer Science Faculty Publications (58)
- All Works (54)
- Faculty Publications (49)
- Computer Science Faculty Publications and Presentations (45)
- Publications and Research (44)
- Articles (40)
- Electronic Theses and Dissertations (39)
- Computer Science and Engineering Faculty Publications (38)
- Natural Language Processing Faculty Publications (38)
- Departmental Technical Reports (CS) (36)
- Journal of Informatics and Web Engineering (35)
- Dissertations and Theses Collection (Open Access) (34)
- USF Tampa Graduate Theses and Dissertations (34)
- Computer Science: Faculty Publications (33)
- Dissertations, Theses, and Capstone Projects (32)
- Theses (31)
- CCAC Theses and Dissertations (30)
- Faculty Scholarship (27)
- Masters Theses (26)
- Publication Type
- File Type
Articles 961 - 990 of 4469
Full-Text Articles in Entire DC Network
Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao
Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao
Research Collection School Of Computing and Information Systems
Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models?To tackle …
Exploring The Application Of Digital Twin Technology In The Energy Sector Using Merec And Mairca Methods, Asmaa Elsayed, Bilal Arain, Karam M. Sallam
Exploring The Application Of Digital Twin Technology In The Energy Sector Using Merec And Mairca Methods, Asmaa Elsayed, Bilal Arain, Karam M. Sallam
Neutrosophic Systems with Applications
Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in …
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …
Raemap: Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardena
Raemap: Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardena
Computer Science Theses & Dissertations
Eye-tracking offers insights into human cognition and attention allocation by capturing and interpreting eye movements. Traditionally, eye tracking research has relied on offline analysis methods, requiring post-experiment processing to derive insights from gaze data. This approach limits its applicability in contexts requiring immediate feedback and adaptive responses based on real-time gaze behaviors. Recent advancements in computing power and eye tracking technology have enabled a shift towards real-time analysis, allowing modern systems to interpret gaze data with minimal latency, facilitating integration into interactive applications and experiments. Despite these advancements, existing tools often face challenges with real-time computation of advanced gaze measures …
Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós
Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós
Computer Science Theses & Dissertations
Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …
Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli
Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli
Engineering Management & Systems Engineering Theses & Dissertations
Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …
An Inference-Centric Approach To Natural Language Processing And Cognitive Modeling, Animesh Nighojkar
An Inference-Centric Approach To Natural Language Processing And Cognitive Modeling, Animesh Nighojkar
USF Tampa Graduate Theses and Dissertations
Reasoning over natural text is highly nuanced, and interpretations can vary widely depending on cultural background, financial status, age, gender, or even mood. This doctoral dissertation seeks to not only mimic human reasoning behaviors but also improve the task used in natural language processing (NLP) to capture naturalistic reasoning, known as the Natural Language Inference (NLI) task. NLI involves determining whether a hypothesis is true (entailment), false (contradiction), or indeterminate (neutral) based on a given premise. Initially, we will investigate the extent to which NLP systems designed to capture semantic equivalence actually measure meaning equivalence. After establishing that they do …
A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen
A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen
USF Tampa Graduate Theses and Dissertations
Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless …
Social Messaging Application With Translation And Speech-To-Text Transformation, Kang Qin Yip, Pey Yun Goh, Lee Ying Chong
Social Messaging Application With Translation And Speech-To-Text Transformation, Kang Qin Yip, Pey Yun Goh, Lee Ying Chong
Journal of Informatics and Web Engineering
Unlike traditional SMS or MMS, messaging apps offer a broader range of data transmission capabilities. The application utilizes a WIFI or internet connection and enables users to exchange information through various means such as text, voice, and multimedia files. However, popular messaging applications such as WeChat, Telegram, and WhatsApp have limitations in language translation and file uploading size. Thus, this project aims to address these limitations by developing a social messaging application that serves as a comprehensive communication tool. The application will facilitate both written and verbal communication by providing translation services for various languages, including voice messages. The proposed …
Sentiment Analysis In Social Media: A Case Study Of Hike In University School Fees In Selected Nigerian Universities, Abdulahi Olarewaju Aremu, Isah Muhammad
Sentiment Analysis In Social Media: A Case Study Of Hike In University School Fees In Selected Nigerian Universities, Abdulahi Olarewaju Aremu, Isah Muhammad
Journal of Informatics and Web Engineering
Faced with escalating operational costs and government disinvestment, Nigerian public universities are implementing tuition fee increases to maintain institutional functionality. This necessary fiscal measure comes in the wake of 2022 industrial action, which exacerbated pre-existing financial strain through extended work stoppages and potentially higher costs associated with resuming activities, while leaving unaddressed the longstanding demands of academics for improved welfare and working conditions. The court-mandated resumption of academic activities without resolution of these core issues further strained university finances, leading to a significant increase in tuition fees. Using VADER, this study investigated social media sentiments related to the increase in …
Knowledge-Based Word Tokenization System For Urdu, Asif Khan, Khairullah Khan, Wahab Khan, Sadiq Nawaz Khan, Rafiul Haq
Knowledge-Based Word Tokenization System For Urdu, Asif Khan, Khairullah Khan, Wahab Khan, Sadiq Nawaz Khan, Rafiul Haq
Journal of Informatics and Web Engineering
Word tokenization, a foundational step in natural language processing (NLP), is critical for tasks like part-of-speech tagging, named entity recognition, and parsing, as well as various independent NLP applications. In our tech-driven era, the exponential growth of textual data on the World Wide Web demands sophisticated tools for effective processing. Urdu, spoken widely across the globe, is experiencing a surge in, presents unique challenges due to its distinct writing style, the absence of capitalization features, and the prevalence of compound words. This study introduces a novel knowledge-based word tokenization system tailored for Urdu. Central to this system is a maximum …
Wix For Web Development And The Application Of The Waterfall Model And Project Based Learning For Project Completion: A Case Study, Mawar Madiah, Ng Kai Xuen, Tan Yew Wen, Tan Zhi Heng, Chong Zhi Tian, Chan Jia Xuan
Wix For Web Development And The Application Of The Waterfall Model And Project Based Learning For Project Completion: A Case Study, Mawar Madiah, Ng Kai Xuen, Tan Yew Wen, Tan Zhi Heng, Chong Zhi Tian, Chan Jia Xuan
Journal of Informatics and Web Engineering
Website development without prior knowledge of HTML or programming experience would be a significant challenge. This study aims to share the students' experiences developing a website using Wix, a user-friendly website builder. The website was assigned as a project-based assessment of one of the courses required to complete a Foundation programme. The course was delivered using a project-based learning (PBL) approach in this context. The students worked as a group to write a project proposal, plan activities, develop the website, write reports, and present the outcome. As for the website development process, the study demonstrates the completion of the project …
Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah
Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah
Theses and Dissertations
Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Theses and Dissertations
Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …
Ranking Cloud Service Providers Using Swara-Marcos In Type-2 Neutrosophic Number Set Environment, Mai Mohamed, Shaimaa Ayman, Rui Yong, Jun Ye
Ranking Cloud Service Providers Using Swara-Marcos In Type-2 Neutrosophic Number Set Environment, Mai Mohamed, Shaimaa Ayman, Rui Yong, Jun Ye
Neutrosophic Systems with Applications
Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims …
Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack
Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack
Dissertations and Theses Collection (Open Access)
This dissertation explores the dynamic interplay between combinatorial creativity and technology-driven innovation within various knowledge-intensive fields. It critically examines the role of combinatorial creativity in generating groundbreaking innovations by amalgamating existing ideas and technologies. This research incorporates a detailed examination of how knowledge, whether tacit or explicit, can be transformed into actionable data to foster innovation in crowdsourcing contexts. Chapter 2 provides an overview of the relevant literature on how Artificial Intelligence and Knowledge Management Systems can support combinatorial creativity. The study further delves into the transformative impact of knowledge management systems, particularly focusing on crowdsourcing platforms that leverage collective …
The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam
The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam
Dissertations, Theses, and Capstone Projects
In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …
Let’S Think Outside The Box: Exploring Leap-Of-Thought In Large Language Models With Multimodal Humor Generation, Shanshan Zhong, Zhongzhan Huang, Shanghua Gao, Wushao Wen, Liang Lin, Marinka Zitnik, Pan Zhou
Let’S Think Outside The Box: Exploring Leap-Of-Thought In Large Language Models With Multimodal Humor Generation, Shanshan Zhong, Zhongzhan Huang, Shanghua Gao, Wushao Wen, Liang Lin, Marinka Zitnik, Pan Zhou
Research Collection School Of Computing and Information Systems
Chain-of-Thought (CoT) [2, 3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs — a nonsequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus …
Mix-Initiative Response Generation With Dynamic Prefix Tuning, Yuxiang Nie, Heyan Huang, Xian-Ling Mao, Lizi Liao
Mix-Initiative Response Generation With Dynamic Prefix Tuning, Yuxiang Nie, Heyan Huang, Xian-Ling Mao, Lizi Liao
Research Collection School Of Computing and Information Systems
Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holistic response generation model without any distinction among different initiatives. It leads to the cross-contamination problem, where the model confuses different initiatives and generates inappropriate responses. Moreover, obtaining plenty of human annotations for initiative labels can be expensive. To address this issue, we propose a general mix-Initiative Dynamic Prefix Tuning framework (IDPT) to decouple different initiatives from the generation model, which learns initiative-aware prefixes in …
Enhancing Code Vulnerability Detection Via Vulnerability-Preserving Data Augmentation, Shangqing Liu, Wei Ma, Jian Wang, Xiaofei Xie, Ruitao Feng, Yang Liu
Enhancing Code Vulnerability Detection Via Vulnerability-Preserving Data Augmentation, Shangqing Liu, Wei Ma, Jian Wang, Xiaofei Xie, Ruitao Feng, Yang Liu
Research Collection School Of Computing and Information Systems
Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) classification task for example determining whether it is vulnerable or not. This poses a challenge for a single deep-learning based model to effectively learn the wide array of vulnerability characteristics. Furthermore, due to the challenges associated with collecting large-scale vulnerability data, these detectors often overfit limited training datasets, resulting in lower model generalization performance. To address the aforementioned challenges, in this work, we introduce a fine-grained vulnerability detector namely FGVulDet. …
Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale
Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale
Master's Theses
We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …
Securing The Inbox: Advancing Phishing Email Detection With Fine-Tuned Bert, Fatima Rashed Al Saedi
Securing The Inbox: Advancing Phishing Email Detection With Fine-Tuned Bert, Fatima Rashed Al Saedi
Theses
In recent years, phishing attacks have persisted as a widespread threat in the contemporary digital environment, presenting substantial risks to individuals and organizations. Cybercriminals are devising increasingly sophisticated strategies to deceive users through malicious emails. In response to this challenge, this research focuses on developing a new tool for detecting phishing emails utilizing the BERT algorithm. The tool aims to enhance email security by accurately identifying deceptive emails and protecting users from potential cyber threats. The primary objective of this study is to investigate how leveraging the BERT algorithm can improve the detection of phishing emails compared to traditional methods. …
Towards Faster Inference Of Transformers: Strategies For Accelerating Decoding Processes, Cunxiao Du
Towards Faster Inference Of Transformers: Strategies For Accelerating Decoding Processes, Cunxiao Du
Dissertations and Theses Collection (Open Access)
This thesis delves into the acceleration and optimization of Transformer inference, a subject of increasing importance with the emergence of Large Language Models (LLMs). The study primarily addresses the challenges posed by two inherent properties of Transformers during inference: the quadratic complexity of the attention mechanism and the sequential nature of autoregressive inference. The research is structured into three main parts. The first part enhances the learning capabilities of non-autoregressive Transformers, achieving a remarkable 15.0x acceleration on machine translation tasks. The following section focuses on lossless acceleration through speculative decoding, where the proposed algorithm, Glide with CAPE, is shown to …
Anomaly Heterogeneity Learning For Open-Set Supervised Anomaly Detection, Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang
Anomaly Heterogeneity Learning For Open-Set Supervised Anomaly Detection, Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang
Research Collection School Of Computing and Information Systems
Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to …
Actively Learn From Llms With Uncertainty Propagation For Generalized Category Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Bobo Li, Jing Jiang
Actively Learn From Llms With Uncertainty Propagation For Generalized Category Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Bobo Li, Jing Jiang
Research Collection School Of Computing and Information Systems
Generalized category discovery faces a key issue: the lack of supervision for new and unseen data categories. Traditional methods typically combine supervised pretraining with self-supervised learning to create models, and then employ clustering for category identification. However, these approaches tend to become overly tailored to known categories, failing to fully resolve the core issue. Hence, we propose to integrate the feedback from LLMs into an active learning paradigm. Specifically, our method innovatively employs uncertainty propagation to select data samples from high-uncertainty regions, which are then labeled using LLMs through a comparison-based prompting scheme. This not only eases the labeling task …
Impact Of Similarities In Gender And Physical Appearance Between User And Embodied Conversational Agents On Trustworthiness, Empathy, And Service Evaluation, Sookyoung Park
Dartmouth College Master’s Theses
Embodied conversational agents (ECAs) have significantly enhanced human-machine interactions and show considerable potential in various industries such as customer service, education, healthcare, entertainment, and finance [1, 2]. This study explores the impact of similarities in gender and physical appearance between ECAs and users on the perceptions of trustworthiness, empathy, and service evaluation within the context of counselor ECAs. We conducted a within-subject experiment (n=50), using a 2x2 factorial arrangement, that varied the gender and the physical appearance of four distinct AI avatars. Participants interacted with each avatar, completing a post-experiment survey and participating in semi-structured interviews. Our findings indicate that …
Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu
Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu
Master's Theses
In the era of total digitization of documents, navigating vast and heterogeneous data landscapes presents significant challenges for effective information retrieval, both for humans and digital agents. Traditional methods of knowledge organization often struggle to keep pace with evolving user demands, resulting in suboptimal outcomes such as information overload and disorganized data. This thesis presents a case study on a pipeline that leverages principles from cognitive science, graph theory, and semantic computing to generate semantically organized knowledge graphs. By evaluating a combination of different models, methodologies, and algorithms, the pipeline aims to enhance the organization and retrieval of digital documents. …
Learning Transferable Negative Prompts For Out-Of-Distribution Detection, Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Learning Transferable Negative Prompts For Out-Of-Distribution Detection, Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Research Collection School Of Computing and Information Systems
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external out-lier data. Further, current …
Sgsh : Stimulate Large Language Models With Skeleton Heuristics For Knowledge Base Question Generation, Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
Sgsh : Stimulate Large Language Models With Skeleton Heuristics For Knowledge Base Question Generation, Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
Research Collection School Of Computing and Information Systems
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework …
The Whole Is Better Than The Sum : Using Aggregated Demonstrations In In-Context Learning For Sequential Recommendation, Wang Lei, Ee-Peng Lim
The Whole Is Better Than The Sum : Using Aggregated Demonstrations In In-Context Learning For Sequential Recommendation, Wang Lei, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation datasets show that LLMSRec-Syn outperforms state-of-the-art LLM-based sequential recommendation methods. In some cases, LLMSRec-Syn can perform on par with …