Digital Commons Network^™

Cloud Computation & Beyond, Yassine Chahid, Patrick Slattery

Publications and Research

This research aims to explore the advancements in cloud computing and their potential influences on other technologies, and which advancements in computer networking facilitate them. These technologies are rapidly progressing in power and complexity, thereby altering how institutions and the public access information. Cloud computing provides a service that enables hardware with limited capabilities to overcome the limitations of on-device components. Due to its reliance on device connections, networking technology serves as the backbone of its operation. The research method encompasses reviewing various publications to understand how networking infrastructure facilitates cloud solutions, and how improvements in relevant hardware can expand …

Empirical Insights Into Ai-Assisted Game Development: A Case Study On The Integration Of Generative Ai Tools In Creative Pipelines, Andrew Begemann, James Hutson

Student Scholarship

This study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D …

Research On The Solutions Generation And Evaluation In Scenario-Based Intelligence Service Based On Multi-Source Data Aggregation, Yuefen Wang, Xiaoyi Dong, Jin He

Journal of Scientific Information Research

[Purpose/significance]Faced with the challenges of big data and artificial intelligence technology, knowledge services are undergoing profound changes, starting from the task scenarios of user needs, this paper explores the scenario-based intelligence service process that supports and matches different industries and their business scenarios. [Method/process]This paper takes the scenario-based intelligence service R-S model as the core, builds the scenario-based intelligence service solutions generation and evaluation process framework based on multi-source data aggregation, briefly describes the main contents and operation of multi-source data aggregation, and takes an organization's "Russia-Ukraine conflict" equipment information quick perception intelligence service as an example, discusses in detail …

Analysis Of American Think Tanks' Views On China-Us Chip Competition And Its Enlightenment, Bingcheng He, Guoli Yang

Journal of Scientific Information Research

[Purpose/significance]This paper focuses on the viewpoints of leading American think tanks on chip competition with China, aiming to unravel the logic behind the formulation of U.S. chip policies towards China. It also seeks to explore innovative pathways for the development of China's chip industry and provide strategic recommendations beneficial to Sino-American chip competition. [Method/process]The study selects 20 representative research reports from 9 prominent think tanks and employs a literature analysis approach to dissect the viewpoints, motives, and potential influences found in these reports. [Result/conclusion]The results reveal that most American think tanks adopt a firm stance, perceiving the rise of China's …

Promoting Ethical Technology Design Practices By Leveraging Human Psychology, Emily Foster-Hanson, Sukrit Venkatagiri

Psychology Faculty Works

The design of technology can result in unintended and unethical consequences. Despite a recent upswing in interventions for enabling more ethical technology design, however, there is little empirical evidence on which strategies work and why. In this review and provocation, we detail how research on the psychology of belief and behavior change can help shift ethical culture within technology design teams, organizations, and the industry more broadly. We suggest three approaches, each supported by empirical evidence: (1) questioning intuitive assumptions, (2) highlighting system complexity, and (3) targeting social and organizational structures. Crucially, these three approaches rely on both individual and …

A Privacy-Preserving Federated Learning Framework For Blockchain Networks, Youssif Abuzied, Mohamed Ghanem, Fadi Dawoud, Habiba Gamal, Eslam Soliman, Hossam Sharara, Tamer Elbatt

Faculty Journal Articles

In this paper we introduce a scalable, privacy-preserving, federated learning framework, coined FLoBC, based on the concept of distributed ledgers underlying blockchains. This is motivated by the rapid growth of data worldwide, especially decentralized data which calls for scalable, decenteralized machine learning models which is capable of preserving the privacy of the data of the participating users. Towards this objective, we first motivate and define the problem scope. We then introduce the proposed FLoBC system architecture hinging on a number of key pillars, namely parallelism, decentralization and node update synchronization. In particular, we examine a number of known node update …

React: Recognize Every Action Everywhere All At Once, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Page Daniel Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

In the realm of computer vision, Group Activity Recognition (GAR) plays a vital role, finding applications in sports video analysis, surveillance, and social scene understanding. This paper introduces Recognize Every Action Everywhere All At Once (REACT), a novel architecture designed to model complex contextual relationships within videos. REACT leverages advanced transformer-based models for encoding intricate contextual relationships, enhancing understanding of group dynamics. Integrated Vision-Language Encoding facilitates efficient capture of spatiotemporal interactions and multi-modal information, enabling comprehensive scene understanding. The model’s precise action localization refines joint understanding of text and video data, enabling precise bounding box retrieval and …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Unified Training Of Universal Time Series Forecasting Transformers, Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Research Collection School Of Computing and Information Systems

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel …

Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …

Fine-Grained Passenger Load Prediction Inside Metro Network Via Smart Card Data, Xiancai Tian, Chen Zhang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Metro system serves as the backbone for urban public transportation. Accurate passenger load prediction for the metro system plays a crucial role in metro service quality improvement, such as helping operators schedule train timetables and passengers plan their trips. However, existing works can only predict low-grained passenger flows of origin-destination (O-D) paths or inflows/outflows of each station but cannot predict passenger load distribution over the whole metro network. To this end, this paper proposes an end-to-end inference framework, PIPE, for passenger load prediction of every metro segment between two adjacent stations, by only utilizing smart card data. In particular, PIPE …

Reproducibility Debt: Challenges And Future Pathways, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility of scientic computation is a critical factor in validating its underlying process, but it is often elusive. Complexity and continuous evolution in software systems have introduced new challenges for reproducibility across a myriad of computational sciences, resulting in growing debt. This requires a comprehensive domain-agnostic study to dene and asses Reproducibility Debt (RpD) in scientic software, thus uncovering and classifying all underlying factors attributed towards its emergence and identication i.e., causes and eects. Moreover, an organised map of prevention strategies is imperative to guide researchers for its proactive management. This vision paper highlights the challenges that hinder eective management …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen

Department of Sociology: Dissertations, Theses, and Student Research

The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …

The Information Content Of Financial Statement Fraud Risk: An Ensemble Learning Approach, Wei Duan, Nan Hu, Fujing Xue

Research Collection School Of Computing and Information Systems

This study aims to assess the financial statement fraud risk ex ante and empirically explore its information content to help improve decision-making and daily operations. We propose an ex-ante fraud risk index by adopting an ensemble learning approach and a theoretically grounded framework. Our ensemble learning model systematically examines the fraud process and deals effectively with the unique challenges in the financial fraud setting, which yields superior prediction performance. More importantly, we empirically examine the information content of our estimated ex-ante fraud risk from the perspective of operational efficiency. Our empirical results find that the estimated ex-ante fraud risk is …

Cognitive Technologies, Tom Davenport

Asian Management Insights

AI and the revolution of work.

Professor Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, speaks about how companies can integrate generative Artificial Intelligence (GenAI) into their operations while ensuring workforce adaptation and skills development.

Development Of An Algorithm To Identify And Calculate The Amount Of File Slack On An Image Of A Given Drive, Nicholas Flynn

Honors Theses

As society increasingly relies on technology, the rates of cyber crime have been increasing at exponential rates. Cyber criminals are also discovering new ways to hide evidence of their crimes. This study develops a forensic analysis algorithm to evaluate the amount of file slack on an image of a drive. Slack space, leftover drive space on a disk sector after a file has been written, can be exploited to hide data. The algorithm aims to detect and calculate this slack space to help direct forensic investigations. The algorithm was evaluated on a population dataset of 100,000 files with random data …

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Multi-Scale Deep Representation Learning In Synthetic Biology, Xiaoyi Liu

Theses and Dissertations

Synthetic biology advances and combines the expertise of engineers and biologists, bridging the gap between engineering and natural life. Synthetic biology has been generally categorized into two broad branches by developing new biological components, networks, and systems to reprogram organisms. The first branch involves using synthetic molecules to mimic natural biological functions. The second branch focuses on assembling natural biological components in novel ways, aiming to produce systems with unique, practical functions. Thus, the de novo engineering of biological modules and synthetic pathways is used in related practical bioengineering applications, such as drug-targeting strategies and microbial product manufacturing. Therefore, synthetic …

Quantitative Evaluation Of Security Intelligence Policy Texts In China: Text Analysis Based On Pmc Model, Bin Zhang

Journal of Scientific Information Research

[Purpose/significance]Analyzing the laws, regulations and policies related to security intelligence in China can not only provide reference for decision-making, but also effectively enrich the connotation of China's overall national security concept. [Method/process]Using the LDA topic model, text mining was conducted on laws, regulations and policies related to security intelligence in China, and theme words were extracted from them. At the same time, based on the selection of policy indicators by existing scholars, scientifically select and design evaluation indicators for China's security intelligence laws, regulations, and policies. Referring to the overall national security concept, several representative policy contents were selected for …

Green Finance Growth Prediction Model Based On Time-Series Conditional Generative Adversarial Networks, Aya Salama Abdelhady, Nadia Dahmani, Lobna M. Abouel-Magd, Ashraf Darwish, Aboul Ella Hassanien

All Works

Climate change mitigation necessitates increased investment in green sectors. This study proposes a methodology to predict green finance growth across various countries, aiming to encourage such investments. Our approach leverages time-series Conditional Generative Adversarial Networks (CT-GANs) for data augmentation and Nonlinear Autoregressive Neural Networks (NARNNs) for prediction. The green finance growth predicting model was applied to datasets collected from forty countries across five continents. The Augmented Dickey-Fuller (ADF) test confirmed the non-stationary nature of the data, supporting the use of Nonlinear Autoregressive Neural Networks (NARNNs). CT-GANs were then employed to augment the data for improved prediction accuracy. Results demonstrate the …

A Smartphone-Based Cardiac Health Monitoring System For Hypertension, Kazi Shafiul Alam

Dissertations (1934 -)

Hypertension is the primary modifiable risk factor affecting global health across all causes. It is responsible for developing serious health concerns, including Cardiovascular diseases (CVD), the number one cause of death worldwide. More than half of individuals with hypertension do not know about their condition, and a substantial portion of those who are aware do not get the appropriate treatment. However, effectively managing hypertension has the potential to reduce the global burden of disease and mortality. Monitoring hypertension and cardiovascular health at home or in the office often requires specialized equipment or wearable devices, which can be uncomfortable, require specific …

Empowering Informed Decision-Making In Mental Health Care: A Web-Based Recommendation System For Mobile App Selection, Md Romael Haque

Dissertations (1934 -)

In 2022, 23.1% of adults in the United States (77 million individuals) were affected by mental health (MH) concerns. Due to the inaccessibility and high cost of traditional treatment, around 55% of people with severe mental illnesses do not receive treatment. Mental health concerns are prevalent, affecting a significant portion of the population in the United States. Traditional treatment options are often inaccessible and expensive, leaving many people without essential mental healthcare. However, the rise of mobile technologies has given rise to a promising solution: mobile mental health applications (MMHAs). These apps offer greater accessibility and affordability, potentially expanding mental …

Predicting Critical Risks And Long-Term Impact Of Covid-19 Patients With Substance Use Disorder (Sud) Using Machine Learning, Jiawei Wu

Dissertations (1934 -)

The crisis of substance use disorder (SUD), a problematic pattern of substance use that causes significant impairment or distress, is one of the most devastating threats to the public health system in the United States and it is ongoing. Opioid dependency or opioid overdose is a common drug use disorder and the number of deaths due to opioid overdose is increasing significantly during the COVID-19 pandemic because opioid use impacts mostly human respiratory, increases vulnerability to COVID-19, and further leads to higher morbidity and mortality. Other substance use disorders, including alcohol, cocaine, cannabis, and tobacco, will also exacerbate both physical …

Enhancing Security In Modern Medical Devices: The Medicalharm Methodology And Cyberllama2, Emmanuel Kwarteng

Dissertations (1934 -)

With the rapid growth of Modern Medical Devices (MMDs) and their increasing connectivity to enhance patient care, concerns about security, privacy, and safety are paramount. If compromised, these devices can expose sensitive patient information and harm patients. Therefore, securing MMDs against cyber-attacks is critical. Threat modeling, mandated by the FDA as a premarket submission requirement in the MMD domain, serves as the first defense mechanism. However, our investigation of 119 participants from various MMD manufacturing companies revealed a need for a tailored threat modeling methodology that considers both patient safety and device complexity. To address this, we present MEDICALHARM, a …

Establishing Metrics To Encourage Broader Use Of Atomic Requirements – A Call For Exchange And Experimentation, William L. Honig

Computer Science: Faculty Publications and Other Works

There are seemingly many advantages to being able to identify, document, test, and trace single or “atomic” requirements during system development and maintenance. Ongoing work with Agile development has focused on “user stories” that can capture individual features for implementation. However, it is still difficult to evaluate the quality of such requirements and teaching their creation is difficult.

Based on a working definition of atomic requirement, this paper proposes a set of metrics for their evaluation. Ten metrics designed to measure atomic requirements are presented here: five used on individual requirements statements and five applied to a requirements document or …

Integrating Remote Sensing And Machine Learning To Determine Past, Current And Future Crop Water Use From The Nubian Sandstone Aquifer System, Moaz Ishag

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The agriculture sector is a significant consumer of water, and sustainable water use begins with monitoring irrigated land. Delineating irrigated land supports decision-makers and promotes the sustainable use of this crucial resource. This study focuses on the Nubian Sandstone Aquifer System (NSAS), the largest aquifers in the world, which spans Egypt, Sudan, Libya, and Chad. The study aims to: 1) quantify the increase in irrigated hectares (both pivot and non-pivot) from 2000-2001 to 2023-2024; 2) identify major irrigated crop types and their water requirements; and 3) quantify groundwater crop water use from the NSAS using remote sensing via the Google …

Missing History Of A Modern Domesticate: Historical Demographics And Genetic Diversity In Farm-Bred Red Fox Populations, Halie M. Rando, Emmarie P. Alexander, Sophie Preckler-Quisquater, Cate B. Quinn, Jeremy T. Stutchman, Jennifer L. Johnson, Estelle R. Bastounes, Beata Horecka, Kristina L. Black, Michael P. Robson, Darya V. Shepeleva, Yury E. Herbeck, Anastasiya V. Kharlamova, Lyudmila N. Trut, Jonathan N. Pauli, Benjamin N. Sacks, Anna V. Kukekova

Computer Science: Faculty Publications

The first record of captive-bred red foxes (Vulpes vulpes) dates to 1896 when a breeding enterprise emerged in the provinces of Atlantic Canada. Because its domestication happened during recent history, the red fox offers a unique opportunity to examine the genetic diversity of an emerging domesticated species in the context of documented historical and economic influences. In particular, the historical record suggests that North American and Eurasian farm-bred populations likely experienced different demographic trajectories. Here, we focus on the likely impacts of founder effects and genetic drift given historical trends in fox farming on North American and Eurasian farms. A …

Compiler-Provenance Identification In Obfuscated Binaries Using Vision Transformers, Wasif Khan, Saed Alrabaee, Mousa Al-Kfairy, Jie Tang, Kim Kwang Raymond Choo

All Works

Extracting compiler-provenance-related information (e.g., the source of a compiler, its version, its optimization settings, and compiler-related functions) is crucial for binary-analysis tasks such as function fingerprinting, detecting code clones, and determining authorship attribution. However, the presence of obfuscation techniques has complicated the efforts to automate such extraction. In this paper, we propose an efficient and resilient approach to provenance identification in obfuscated binaries using advanced pre-trained computer-vision models. To achieve this, we transform the program binaries into images and apply a two-layer approach for compiler and optimization prediction. Extensive results from experiments performed on a large-scale dataset show that the …