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Full-Text Articles in Physical Sciences and Mathematics

Net-Zero Target And Emissions From Land Conversions: A Case Study Of Maryland's Climate Solutions Now Act, Philip Hutton May 2023

Net-Zero Target And Emissions From Land Conversions: A Case Study Of Maryland's Climate Solutions Now Act, Philip Hutton

All Theses

Many climate change “solution” plans include net-zero goals, which involve balancing the anthropogenic greenhouse gas emissions (GHG) with their removal. Achieving net-zero goals is particularly problematic for soils because they are often excluded from GHG inventories and reduction plans. For example, Maryland’s Climate Solutions Now Act (Senate Bill 528) put forward a target of net-zero emissions by 2045. To achieve these goals, the state of Maryland (MD) needs to quantify GHG emissions. Soils are currently excluded from MD’s GHG assessments. This study examines the challenges in meeting net-zero goals by using carbon dioxide (CO2). The current study quantified …

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The Fate Of Fraxinus Spp. In New Jersey : A Physioeconomic Evaluation Of The Forest System And Infrastructure Implications, Erik W. Lyttek May 2023

The Fate Of Fraxinus Spp. In New Jersey : A Physioeconomic Evaluation Of The Forest System And Infrastructure Implications, Erik W. Lyttek

Theses, Dissertations and Culminating Projects

Though the impacts of pest infestation on forested ecosystems vary, the natural factors that facilitate outbreaks are of concern across multiple disciplines across the USA, affecting such diverse fields as economics, ecology, hydrology, atmospheric sciences, and pedology. Emerald Ash Borer (EAB) is a typical example of an aggressive invasive forest pest; it has proven difficult to eliminate, resistant to natural predators, and damaging to local flora. EAB has recently proliferated through the northern and mid-western forests of the USA, devastating the native population of ash trees (Fraxinus spp.), and has invaded an estimated 28% of all susceptible trees. In New …

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Evaluating The Problem Solving Abilities Of Chatgpt, Fankun Zeng May 2023

Evaluating The Problem Solving Abilities Of Chatgpt, Fankun Zeng

McKelvey School of Engineering Theses & Dissertations

This thesis addresses the need for a fair evaluation of language models' problem solving abilities by presenting a unified evaluation framework for ChatGPT on 16 problem solving datasets (e.g., NaturalQA, HellaSwag, MMLU, etc.). We evaluate the model's performance using F1, exact match, and quasi-exact match metrics and find that ChatGPT is highly accurate in solving tasks that require commonsense and knowledge. However, we also identify truncated text bias and few-shot scenarios as challenges that may impact ChatGPT's performance. Our research highlights the importance of standardizing datasets and developing a unified evaluation system for the fair evaluation of language models. Overall, …

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Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden May 2023

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

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Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria May 2023

Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …

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Are You Cloud-Certified? Preparing Computing Undergraduates For Cloud Certification With Experiential Learning, Eng Lieh Ouh, Benjamin Gan May 2023

Are You Cloud-Certified? Preparing Computing Undergraduates For Cloud Certification With Experiential Learning, Eng Lieh Ouh, Benjamin Gan

Research Collection School Of Computing and Information Systems

Cloud Computing skills have been increasing in demand. Many software engineers are learning these skills and taking cloud certification examinations to be job competitive. Preparing undergraduates to be cloud-certified remains challenging as cloud computing is a relatively new topic in the computing curriculum, and many of these certifications require working experience. In this paper, we report our experiences designing a course with experiential learning to prepare our computing undergraduates to take the cloud certification. We adopt a university project-based experiential learning framework to engage industry partners who provide project requirements for students to develop cloud solutions and an experiential risk …

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Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong May 2023

Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong

Research Collection School Of Computing and Information Systems

In this paper, we empirically examine and assess the effectiveness of a chatbot workshop as experiential teaching and learning tool to engage undergraduate students enrolled in an elective course “Doing Business with A.I.” in the Lee Kong Chian School of Business (LKCSB) at Singapore Management University. The chatbot workshop provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity are designed to impart conversation and user-centric design know how and know why to students. A key didactical aspect which informs the design and flow …

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Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang May 2023

Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …

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Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang May 2023

Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang

Research Collection School Of Computing and Information Systems

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal …

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Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu May 2023

Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims …

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Win: Weight-Decay-Integrated Nesterov Acceleration For Adaptive Gradient Algorithms, Pan Zhou, Xingyu Xie, Shuicheng Yan May 2023

Win: Weight-Decay-Integrated Nesterov Acceleration For Adaptive Gradient Algorithms, Pan Zhou, Xingyu Xie, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Training deep networks on large-scale datasets is computationally challenging. In this work, we explore the problem of “how to accelerate adaptive gradient algorithms in a general manner”, and aim to provide practical efficiency-boosting insights. To this end, we propose an effective and general Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptive algorithms. Taking AdamW and Adam as examples, we minimize a dynamical loss per iteration which combines the vanilla training loss and a dynamic regularizer inspired by proximal point method (PPM) to improve the convexity of the problem. To introduce Nesterov-alike-acceleration into AdamW and Adam, we respectively use the first- and …

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Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo May 2023

Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …

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Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li May 2023

Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Identifying security patches via code commits to allow early warnings and timely fixes for Open Source Software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, …

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A Study Of Variable-Role-Based Feature Enrichment In Neural Models Of Code, Aftab. Hussain, Md. Rafiqul Islam. Rabin, Bowen. Xu, David Lo, Mohammad Amin. Alipour May 2023

A Study Of Variable-Role-Based Feature Enrichment In Neural Models Of Code, Aftab. Hussain, Md. Rafiqul Islam. Rabin, Bowen. Xu, David Lo, Mohammad Amin. Alipour

Research Collection School Of Computing and Information Systems

Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [1], [2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of …

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Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo May 2023

Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …

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Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo May 2023

Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or …

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What's Behind Tight Deadlines? Business Causes Of Technical Debt, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza May 2023

What's Behind Tight Deadlines? Business Causes Of Technical Debt, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza

Research Collection School Of Computing and Information Systems

What are the business causes behind tight deadlines? What drives the prioritization of features that pushes quality matters to the back burner? We conducted a survey with 71 experienced practitioners and did a thematic analysis of the openended answers to the question: “Could you give examples of how business may contribute to technical debt?” Business-related causes were organized into two categories: pure-business and business/IT gap, and they were related to ‘tight deadlines’ and ‘features over quality’, the most frequently cited management reasons for technical debt. We contribute a cause-effect model which relates the various business causes of tight deadlines and …

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Message From The Chairs: Techdebt 2023, Christoph Treude, Yuanfang Cai, Xin Xia, Zadia Codabux, Hideaki Hata, Florian Deissenboeck, Rodrigo Spinola May 2023

Message From The Chairs: Techdebt 2023, Christoph Treude, Yuanfang Cai, Xin Xia, Zadia Codabux, Hideaki Hata, Florian Deissenboeck, Rodrigo Spinola

Research Collection School Of Computing and Information Systems

Welcome to the 6th ACM/IEEE International Conference on Technical Debt, TechDebt 2023, co-located with the International Conference on Software Engineering (ICSE) 2023, in the beautiful city of Melbourne, Australia. After several years of virtual and hybrid conferences, TechDebt 2023 marks the first predominantly in-person edition of the conference series since the onset of the Covid-19 pandemic.

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Task Offloading And Resource Allocation Based On Dl-Ga In Mobile Edge Computing, Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan May 2023

Task Offloading And Resource Allocation Based On Dl-Ga In Mobile Edge Computing, Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan

Turkish Journal of Electrical Engineering and Computer Sciences

With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and …

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Uibee: An Improved Deep Instance Segmentation And Classification Of Ui Elements In Wireframes, Cahi̇t Berkay Kazangi̇rler, Caner Özcan, Buse Yaren Teki̇n May 2023

Uibee: An Improved Deep Instance Segmentation And Classification Of Ui Elements In Wireframes, Cahi̇t Berkay Kazangi̇rler, Caner Özcan, Buse Yaren Teki̇n

Turkish Journal of Electrical Engineering and Computer Sciences

User Interface (UI) is a basic concept in which individuals interact with any computer program or technological device to create a graphical design. In the initial stages of app development, UI prototype is a must. An automatic analysis system for the basic execution of UI designs will considerably speed up the development of designs according to old-fashioned methods. In this approach, it is aimed at saving cost and time by automating the process. For the aforesaid objective, we present a new approach rather than the traditional methods. For this reason, a high amount of elements in wireframes are detected and …

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Analysis And Implementation Of A New High-Buck Dc-Dc Converter With Interleaved Output Inductors And Soft Switching Capability, Sajad Ghabeli Sani, Mohamad Reza Banaei, Seyed Hossein Hosseini May 2023

Analysis And Implementation Of A New High-Buck Dc-Dc Converter With Interleaved Output Inductors And Soft Switching Capability, Sajad Ghabeli Sani, Mohamad Reza Banaei, Seyed Hossein Hosseini

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses …

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Integration Of Neural Network And Distance Relay To Improve The Fault Localization On Transmission Lines, Linh Tran May 2023

Integration Of Neural Network And Distance Relay To Improve The Fault Localization On Transmission Lines, Linh Tran

Turkish Journal of Electrical Engineering and Computer Sciences

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still …

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Deep Learning-Based Turkish Spelling Error Detection With A Multi-Class False Positive Reduction Model, Burak Aytan, Cemal Okan Şakar May 2023

Deep Learning-Based Turkish Spelling Error Detection With A Multi-Class False Positive Reduction Model, Burak Aytan, Cemal Okan Şakar

Turkish Journal of Electrical Engineering and Computer Sciences

Spell checking and correction is an important step in the text normalization process. These tasks are more challenging in agglutinative languages such as Turkish since many words can be derived from the root word by combining many suffixes. In this study, we propose a two-step deep learning-based model for misspelled word detection in the Turkish language. A false positive reduction model is integrated into the system to reduce the false positive predictions originating from the use of foreign words and abbreviations that are commonly used in Internet sharing platforms. For this purpose, we create a multi-class dataset by developing a …

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Unbiased Federated Learning In Energy Harvesting Error-Prone Channels, Zeynep Çakir, Eli̇f Tuğçe Ceran Arslan May 2023

Unbiased Federated Learning In Energy Harvesting Error-Prone Channels, Zeynep Çakir, Eli̇f Tuğçe Ceran Arslan

Turkish Journal of Electrical Engineering and Computer Sciences

Federated learning (FL) is a communication-efficient and privacy-preserving learning technique for collaborative training of machine learning models on vast amounts of data produced and stored locally on the distributed users. This paper investigates unbiased FL methods that achieve a similar convergence as state-of-the-art methods in scenarios with various constraints like an error-prone channel or intermittent energy availability. For this purpose, we propose FL algorithms that jointly design unbiased user scheduling and gradient weighting according to each user's distinct energy and channel profile. In addition, we exploit a prevalent metric called the age of information (AoI), which quantifies the staleness of …

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An Analytical Solution Of Fractional Order Pi Controller Design For Stable/Unstable/Integrating Processes With Time Delay, Erdal Çökmez, İbrahi̇m Kaya May 2023

An Analytical Solution Of Fractional Order Pi Controller Design For Stable/Unstable/Integrating Processes With Time Delay, Erdal Çökmez, İbrahi̇m Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to put forward an analytical solution for tuning parameters of a fractional order PI (FOPI) controller for stable, unstable, and integrating processes with time delay. Following this purpose, the analytical weighted geometrical center (AWGC) method has been extended to the design of fractional order PI controllers. To apply AWGC, the stability equations of the closed-loop system are written in terms of process and fractional order PI controller parameters. With the proposed method, the centroid can be calculated analytically, and the controller parameters can be easily calculated without the need of repetitive drawings of the stability boundary regions. …

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More Wifi For Everyone: Increasing Spectral Efficiency In Wifi6 Networks Using A Distributed Obss/Pd Mechanism, Ali̇ Karakoç, Hüseyi̇n Bi̇rkan Yilmaz, Mehmet Şükrü Kuran May 2023

More Wifi For Everyone: Increasing Spectral Efficiency In Wifi6 Networks Using A Distributed Obss/Pd Mechanism, Ali̇ Karakoç, Hüseyi̇n Bi̇rkan Yilmaz, Mehmet Şükrü Kuran

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a distributed algorithm that determines effective Overlapping Basic Service Set/Preamble Detection (OBSS/PD) threshold levels in each WiFi6 device to maximize the total throughput by increasing the spectral efficiency. Within WiFi6 standard, OBSS/PD mechanism is introduced to increase the overall efficiency of WiFi networks by tuning the receiver sensitivity as well as the transmission power. In a nutshell, the proposed algorithm, RACEBOT, tunes the hearing (i.e. reception) and speaking (i.e. transmission) parameters of each WiFi device individually for the betterment of the WiFi experience of all WiFi networks in a neighborhood. WiFi experience is not only …

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Two Sides Of The Same Coin: Exploiting The Impact Of Identifiers In Neural Code Comprehension, Shuzheng Gao, Cuiyun Gao, Chaozheng Wang, Jun Sun, David Lo, Yue Yu May 2023

Two Sides Of The Same Coin: Exploiting The Impact Of Identifiers In Neural Code Comprehension, Shuzheng Gao, Cuiyun Gao, Chaozheng Wang, Jun Sun, David Lo, Yue Yu

Research Collection School Of Computing and Information Systems

Previous studies have demonstrated that neural code comprehension models are vulnerable to identifier naming. By renaming as few as one identifier in the source code, the models would output completely irrelevant results, indicating that identifiers can be misleading for model prediction. However, identifiers are not completely detrimental to code comprehension, since the semantics of identifier names can be related to the program semantics. Well exploiting the two opposite impacts of identifiers is essential for enhancing the robustness and accuracy of neural code comprehension, and still remains under-explored. In this work, we propose to model the impact of identifiers from a …

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Integrating Ai-Generative Tools In Web Design Education: Enhancing Student Aesthetic And Creative Copy Capabilities Using Image And Text-Based Ai Generators, Jason Lively, James Hutson, Elizabeth Melick May 2023

Integrating Ai-Generative Tools In Web Design Education: Enhancing Student Aesthetic And Creative Copy Capabilities Using Image And Text-Based Ai Generators, Jason Lively, James Hutson, Elizabeth Melick

Faculty Scholarship

Artificial Intelligence (AI) is poised to disrupt all levels of education. The recent advances in the generative capabilities of new chatbots, AI art generators, and large language models have upended the art and design development pipeline. At the same time, the focus has remained on the nature of creativity and the role of humans in the creative process, prompting calls to ban AI art, bring lawsuits over copyright infringement, and demand universal watermarks to identify AI-generative content. Regardless of the outcome of such litigation, AI has already radically altered the workflow for artists and designers. This case study aims to …

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Enhancing Institutional Assessment And Reporting Through Conversational Technologies: Exploring The Potential Of Ai-Powered Tools And Natural Language Processing, James Hutson, Daniel Plate May 2023

Enhancing Institutional Assessment And Reporting Through Conversational Technologies: Exploring The Potential Of Ai-Powered Tools And Natural Language Processing, James Hutson, Daniel Plate

Faculty Scholarship

This study explores the potential of conversational technologies, AI-powered tools, and natural language processing (NLP) in enhancing institutional assessment and reporting processes in higher education. The traditional approach to assessment often involves labor-intensive manual analysis of extensive data and documents, which burdens institutions. To address these challenges, AI-powered tools, such as ChatGPT, LangChain, Poe, Claude, and others, along with NLP techniques, are investigated in relationship to their ability to improve institutional assessment practices and output. By leveraging these advanced technologies, assessment officers and institutional effectiveness, researchers can engage in dynamic conversations with data, transforming spreadsheets and documents from static artifacts …

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Hydrologic Implications Of Snow-Vegetation Interactions In A Semiarid Mountain Climate, Maggi Kraft May 2023

Hydrologic Implications Of Snow-Vegetation Interactions In A Semiarid Mountain Climate, Maggi Kraft

Boise State University Theses and Dissertations

Knowledge of the complex interaction between snow, vegetation, and streamflow in semiarid mountain climates is necessary for predicting water resources. The effects of warming temperatures on snow distribution will cascade into vegetation water use and streamflow. Due to our reliance on snow water resources, it is necessary to understand how vegetation affects snow distribution, how vegetation uses snow water inputs and the subsequent effects on streamflow in the current and warming climate. The overall objective of this research is to improve our understanding of snow-vegetation interactions in a semiarid climate. In this dissertation, I use field data to evaluate how …

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