SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted TOWARDS ROBUST AND ACCURATE TEXT-TO-CODE GENERATION(University of Central Florida, 2024) almohaimeed, saleh; Wang, LiqiangDatabases play a vital role in today’s digital landscape, enabling effective data storage, manage- ment, and retrieval for businesses and other organizations. However, interacting with databases often requires knowledge of query (e.g., SQL) and analysis, which can be a barrier for many users. In natural language processing, the text-to-code task, which converts natural language text into query and analysis code, bridges this gap by allowing users to access and manipulate data using everyday language. This dissertation investigates different challenges in text-to-code (including text-to-SQL as a subtask), with a focus on four primary contributions to the field. As a solution to the lack of statistical analysis in current text-to-code tasks, we introduce SIGMA, a text-to- Code dataset with statistical analysis, featuring 6000 questions with Python code labels. Baseline models show promising results, indicating that our new task can support both statistical analysis and SQL queries simultaneously. Second, we present Ar-Spider, the first Arabic cross-domain text-to-SQL dataset that addresses multilingual limitations. We have conducted experiments with LGESQL and S2SQL models, enhanced by our Context Similarity Relationship (CSR) approach, which demonstrates competitive performance, reducing the performance gap between the Arabic and English text-to-SQL datasets. Third, we address context-dependent text-to-SQL task, often overlooked by current models. The SParC dataset was explored by utilizing different question rep- resentations and in-context learning prompt engineering techniques. Then, we propose GAT-SQL, an advanced prompt engineering approach that improves both zero-shot and in-context learning experiments. GAT-SQL sets new benchmarks in both SParC and CoSQL datasets. Finally, we introduce Ar-SParC, a context-dependent Arabic text-to-SQL dataset that enables users to interact with the model through a series of interrelated questions. In total, 40 experiments were conducted to investigate this dataset using various prompt engineering techniques, and a novel technique called GAT Corrector was developed, which significantly improved the performance of all base- line models.11 0Item Restricted Scalex: Scalability Exploration of Multi-Agent Reinforcement Learning Agents in Grid-Interactive Efficient Buildings(Saudi Digital Library, 2023-08-11) Almilaify, Yara; Nagy, ZoltanTransitioning to renewable energy and decarbonization presents challenges for grid-interactive efficient building (GEB) communities. Conventional control systems struggle to maximize intermittent renewable energy, but advanced control architecture and utilization of renewable sources with energy storage can overcome this limitation and optimize energy flexibility. Reinforcement learning (RL) offers potential solutions, but its scalability and computational demands in large-scale settings remain unclear. This paper examines the scalability of Soft-Actor Critic (SAC) in multi-agent systems, comparing decentralized-independent SACs and centralized SACs using CityLearn, an OpenAI Gym environment. We consider neighborhoods consisting of 2 to 64 single-family residential buildings, each equipped with cooling and heating storage devices, domestic hot water storage devices, electrical storage devices, and solar PV systems. Our findings suggest that independent controllers outperform the centralized controller with increasing number of buildings. We also show that the performance on the building level can differ from the aggregated performance.28 0Item Restricted THE EFFECT OF USING A TECHNOLOGY BASED SELF-MONITORING INTERVENTION ON ON-TASK BEHAVIOR FOR STUDENTS WITH BEHAVIORAL ISSUES IN AN INCLUSIVE CLASSROOM(2023-08) Algethami, Sami; Vasquez, EleazarThis study examined the effectiveness of using a technology-based self-monitoring intervention called Monitoring Behavior on the Go (MoBeGo). On-task behavior for students with behavioral issues was the primary dependent variable in the study. The researcher employed a single-subject withdrawal design (ABAB) with two generalization phases (C-D) to investigate the ability of MoBeGo to generalize the results to a different setting. Visual analysis of graphs revealed the participants had a clear functional relationship between MoBeGo and percentage of on-task behavior. The finding illustrated on-task behaviors in a different setting did not increase without using MoBeGo and therefore no automatic generalization occurred in different settings. A replicated phase (D) was conducted to confirm the finding, and the results showed the percentage of on-task behavior increased in math and science classes which used MoBeGo and did not increase in reading/writing which did not use MoBeGo. Also, the outcomes showed MoBeGo has a high level of acceptability among teachers who participated in the study. The researcher evaluated this single-subject withdrawal design (ABABCD) by using the What Works Clearinghouse (WWC) evidence standards. In addition, the researcher utilized the Single-Case Analysis and Review Framework (SCARF) to evaluate the study outcomes. The evaluation results of using WWC and SCARF are discussed in Chapter 4. The researcher discussed major lessons learned and some limitations of using technology based self-monitoring (TBSM). In addition, implications for practitioners, researchers, and application developers were included as future directions for using TBSM. Moreover, the researcher discussed the potential role of self-monitoring-based artificial intelligence (SMBAI) in education, and the use of artificial intelligence (AI), large language models (LLMs), or machine learning (ML) with self-monitoring apps. Finally, some important questions were raised about protecting privacy and minimizing the risk of data breaches for individuals, and how to ensure the security of individuals’ data.47 0Item Restricted Applications Of Artificial Intelligence In Supply Chain Management In The Era Of Industry 4.0(2023) Ali, Arishi; Krishna, KrishnanNowadays, an emerging trend in Supply Chain Management (SCM) is a focus shift from classical Supply Chain (SC) to digital SC. However, decisions in the digital SC context require new tools and methodologies that consider the digitalization environment. Artificial Intelligence (AI) methodologies can provide learning, predictive, and automated decision-making capabilities in the digital environment. Among a wide range of problems in the SCM field, risk management, logistics, and transportation have received less attention from an AI perspective. The work presented in this dissertation proposes three AI-based approaches to help SCs manage their operations more effectively using creative risk monitoring and logistics/transportation solutions in the era of Industry 4.0. In the first study, a Digital Twin (DT) framework for analyzing and predicting the impact of COVID-19 disruptions on the manufacturing SC is developed to support the decision-making process in disrupted SC. The proposed Digital SC Twin (DSCT) model is aimed to work as an online controlling tower to monitor the behavior of physical SC in the digital environment and guide SCM managers to make the necessary adjustments to minimize risks and maintain SC stability during disruptions. In the second study, a contactless truck-drone delivery model for last-mile problems in the SC is introduced to support logistics and transportation operations during pandemics. A hybrid AI approach is developed to provide quality real-time solutions for the introduced truck-drone delivery system. In the third study, a collaborative Multi-Agent Deep Reinforcement Learning (MADRL) approach for vehicle routing in the SCM is designed to facilitate collaboration and communication among multiple vehicles in the SC distribution networks. Overall, the methods and models presented in this dissertation can enable SCs to transform their traditional practices, provide cost savings, support real-time decision-making, and enable self-optimization and self-healing capabilities in the age of Industry 4.056 0Item Restricted Deep Learning Methods to Investigate Online Hate Speech and Counterhate Replies to Mitigate Hateful Content(2025-05-15) Albanyan, Abdullah; Blanco, Eduardo; Albert, MarkHateful content and offensive language are commonplace on social media platforms. Many surveys prove that high percentages of social media users experience online harassment. Previous efforts have been made to detect and remove online hate content automatically. However, removing users’ content restricts free speech. A complementary strategy to address hateful content that does not interfere with free speech is to counter the hate with new content to divert the discourse away from the hate. In this dissertation, we complement the lack of previous work on counterhate arguments by analyzing and detecting them. Firstly, we study the relationships between hateful tweets and replies. Specifically, we analyze their fine-grained relationships by indicating whether the reply counters the hate, provides a justification, attacks the author of the tweet, or adds additional hate. The most obvious finding is that most replies generally agree with the hateful tweets; only 20% of them counter the hate. Secondly, we focus on the hate directed toward individuals and detect authentic counterhate arguments from online articles. We propose a methodology that assures the authenticity of the argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative compared to counterhate generation approaches that may hallucinate unsupported arguments. Thirdly, we investigate the replies to counterhate tweets beyond whether the reply agrees or disagrees with the counterhate tweet. We analyze the language of the counterhate tweet that leads to certain types of replies and predict which counterhate tweets may elicit more hate instead of stopping it. We find that counterhate tweets with profanity content elicit replies that agree with the counterhate tweet. This dissertation presents several corpora, detailed corpus analyses, and deep learning-based approaches for the three tasks mentioned above.54 0