Digital Commons Network^™

Social Networks And Large Language Models For Division I Basketball Game Winner Prediction, Gina Sprint

Computer Science Faculty Scholarship

Sporting event outcome prediction is a well-established and actively researched domain, with a particular focus on college basketball’s March Madness tournament. Researchers, fans, and gamblers alike seek accurate game-level predictions using features such as tournament seeds, season performance, and expert opinions. While machine learning algorithms have been harnessed to build prediction models, no perfect model or human-created bracket has emerged. This paper explores a novel approach to basketball game outcome prediction by utilizing the power of social networks and large language models (LLMs). LLMs are trained to understand and generate text, often eliminating the need for a feature engineering step. …

Sales Forecasting For Retail Business Using Xgboost Algorithm And Timesfm, Prathana Dankorpho

Chulalongkorn University Theses and Dissertations (Chula ETD)

The retail industry is continuously evolving with the expansion of sales channels and the diversification of product assortments. However, current forecasting methods, relying on simplistic statistical models, frequently encounter difficulties in adjusting to the dynamic environment. This limitation leads to challenges in accurately predicting sales. Consequently, there is a critical need to improve the accuracy and frequency of sales predictions to enable timely decision-making for business strategies. Through a comprehensive analysis of datasets from 2019 to 2023, this study illustrates the advantages of integrating XGBoost and TimesFM to gain deeper insights into sales patterns. Results demonstrate a significant enhancement in …

Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik

Data Science Faculty Publications

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. …

A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi

School of Cybersecurity Faculty Publications

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …

Foraging Economies: A Market Based Methodology For Robotic Swarm Foraging, John A. Little

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach …

Effect Of Specific Data Variations On Automated Speaker Recognition, Ethan David Meighen

Graduate Theses, Dissertations, and Problem Reports (ETD)

Speaker recognition is not a new biometric modality but there are still many obstacles in the way in order for it to become as used as fingerprint recognition, facial recognition, and iris recognition. Many real-world environmental conditions, hardware device variations, and human behavior present serious challenges to the use of opportunistic voice or speaker samples for identification purposes. Non-idealities, identified as nuisance factors, include environmental noise, input device quality, length of utterance, sample rate variation, and unscripted data are common nuisance factors that can impact speaker recognition match score performance. The impact of the nuisance factors listed above were evaluated …

Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

Browse all Theses and Dissertations

This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …

A Comparison Of Lexical Tokenization Methods, Nathan Culmer

Williams Honors College, Honors Research Projects

The purpose of this project was to compare tokenization methods, or methods of breaking up a text into meaningful parts for use in natural language processing. The effectiveness of several commonly used tokenization methods were investigated, including morpheme tokenization, which takes into account the linguistic features of the language. In addition, I proposed and implemented a new technique to consider the capitalization pattern of a word in the tokenization process, in order to allow this process to include more natural language features. The effectiveness of these methods was compared by using them in a sentiment analysis model for various datasets, …

The Amethyst Compiler, David Britton

Williams Honors College, Honors Research Projects

Amethyst is a custom programming language, whose compiler translates Amethyst source code into textual LLVM IR, which can, in turn, be compiled by LLVM back-ends to produce executable binaries. This paper explores compiler concepts and implementation details for the Amethyst compiler, describes the LLVM architecture, and provides an overview of some Amethyst language features.

Autonomous Robot For Indoor Enhanced Living (Ariel), Prabhjot Kaur

Wayne State University Dissertations

The global population is aging rapidly, with the total percentage of older adults (65 years and older) projected to increase from 10\% of the total population in 2022 to 16\% by 2050, according to the World Population Prospectus 2022 issued by the United Nations. For certain parts of the world such as Europe and North America, this translates to 1 in every 4 persons is projected to be 65 years or older by 2060. This trend raises concerns about providing quality long-term care for the older population. Moreover, according to the 2021 survey by the American Association of Retired Persons …

Improving The Robustness Of Neural Networks To Adversarial Patch Attacks Using Masking And Attribution Analysis, Atandra Mahalder

Honors Undergraduate Theses

Computer vision algorithms, including image classifiers and object detectors, play a pivotal role in various cyber-physical systems, spanning from facial recognition to self-driving vehicles and security surveillance. However, the emergence of real-world adversarial patches, which can be as simple as stickers, poses a significant threat to the reliability of AI models utilized within these systems. To address this challenge, several defense mechanisms such as PatchGuard, Minority Report, and (De)Randomized Smoothing have been proposed to enhance the resilience of AI models against such attacks. In this thesis, we introduce a novel framework that integrates masking with attribution analysis to robustify AI …

Music Recommendation Using Exemplars And Contrastive Learning, Tina Tran

Honors Undergraduate Theses

The popularity of AI audio applications is growing, it is used in chatbots, automated voice translation, virtual assistants, and text-to-speech translation. Audio classification is crucial in today’s world with a growing need to sort and classify millions of existing audio data with increasing amounts of new data uploaded over time. In the area of classification lies the difficult and lucrative problem of music recommendation. Research in music recommendation has trended over time towards collaborative-based approaches utilizing large amounts of user data. These approaches tend to deal with the cold-start problem of insufficient data and are costly to train. We look …

L3dml: Facilitating Geo-Distributed Machine Learning In Network Layer, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Bo Lei, Hongke Zhang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables …

Message From The Phd Dissertation Showcase Chairs, Panos K. Chrysanthis, Sanjay Madria

Computer Science Faculty Research & Creative Works

No abstract provided.

Smart Connected Farms And Networked Farmers To Improve Crop Production, Sustainability And Profitability, Asheesh K. Singh, Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair, Suzanne Fey, Fateme Fotouhi, Ashish Gupta, Amit Jha, Jorge C. Martinez-Palomares, Kevin Menke, Aaron Prestholt, Vishesh K. Tanwar, Xu Tao, Anusha Vangala, Matthew E. Carroll, Sajal K. Das, Guilherme Depaula

Computer Science Faculty Research & Creative Works

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. a network and coordinated farmer network provide unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. the aim of this article is to provide a comprehensive overview of the state of the art in SCF including the …

Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan

Computer Science Faculty Research & Creative Works

Smartphones and wearable devices have been integrated into our daily lives, offering personalized services. However, many apps become overprivileged as their collected sensing data contains unnecessary sensitive information. For example, mobile sensing data could reveal private attributes (e.g., gender and age) and unintended sensitive features (e.g., hand gestures when entering passwords). To prevent sensitive information leakage, existing methods must obtain private labels and users need to specify privacy policies. However, they only achieve limited control over information disclosure. In this work, we present Hippo to dissociate hierarchical information including private metadata and multi-grained activity information from the sensing data. Hippo …

Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation often neglects the potential for interaction between labeled and unlabeled images. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction. The first part, embedding fusion, interpolates between labeled and unlabeled embeddings to improve representation learning. The second part is a new loss, grounded in …

Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose …

Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can …

The Perils Of Wi-Fi Spoofing Attack Via Geolocation Api And Its Defense, Xiao Han, Junjie Xiong, Wenbo Shen, Mingkui Wei, Shangqing Zhao, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in …

Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng

Research Collection School Of Computing and Information Systems

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) [1] toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2,2)-threshold Paillier cryptosystem with fast …

A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Demonstrating Canvas-Based Processing Of Multiple Camera Streams At The Edge, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We demonstrate criticality-aware canvas-based processing of multiple concurrent camera streams at the resource constrained edge to show substantial improvement in the accuracy-throughput trade-off. The proposed system focuses the available computation resources on select Regions of Interest (RoI) across all the camera streams by (i) extracting RoI from the input camera stream (ii) 2D bin packing the RoI on a canvas frame and (iii) batching and inferring upon these constructed composite canvas frames with a YOLOv5 object detection model. Our experiments show that such canvas-based processing can (i) sustain real-time processing throughput of 23 FPS per camera across 6 concurrent input …

Societal Impacts Of Artificial Intelligence: Ethics, Legal, And Governance Issues, Yuzhou Qian, Keng Siau, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) is quickly changing the way we work and the way we live. The emergence of ChatGPT has thrust AI, especially Generative AI, into the spotlight. The societal impact of AI is on most people's minds. This article presents several research projects on how AI impacts work and society. Three research works are discussed in this article. The first study develops a theoretical framework structuring the legal and ethical objectives that are needed and the means to achieve them. The second study concentrates on bias and discrimination issues embedded in AI applications. It focuses on enhancing the collaboration …

Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le

Research Collection School Of Computing and Information Systems

Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …

Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learningbased SE system designed for wireless earbuds. Specifically, by jointly using the earbud’s in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under …

Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao

Research Collection School Of Computing and Information Systems

Traditional kernel updates such as perfective maintenance and vulnerability patching requires shutting the system down, disrupting continuous execution of applications. Enterprises and researchers have proposed various live updating techniques to patch the kernel with lower downtime to reduce the loss of useful uptime. However, existing kernel live update techniques either rely on specific support from the target OS, or are deployed in virtualized environments (i.e., systems running in virtual machines). In this article we present KShot , a hardware-assisted live and secure kernel function update mechanism for native operating systems. By leveraging x86 SMM and Intel SGX, KShot runs in …

Effects Of Mindfulness And Emotion Regulation On Aesthetics: A Theoretical Model From Hedonic Perspective Of Processing Fluency, Geng-Bao Lin, Fiona Fui-Hoon Nah, Choon Ling Sia

Research Collection School Of Computing and Information Systems

Research has shown that processing fluency positively impacts perceived aesthetics, with pleasure mediating the relationship. Considering the important role of pleasure, we propose studying the role of emotion regulation in moderating the mediated relationship from processing fluency to perceived aesthetics. Based on our hypotheses, individuals’ emotion regulation strategies are expected to have moderating effects on the relationship between processing fluency and perceived aesthetics such that cognitive reappraisal positively moderates the relationship from processing fluency to pleasure, and expressive suppression negatively moderates the relationship from pleasure to perceived aesthetics. Trait mindfulness is also expected to influence perceived aesthetics through emotion regulation …

Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

According to the Complementary Learning Systems (CLS) theory (McClelland et al. 1995) in neuroscience, humans do effective continual learning through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose DualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via …

Applying Neural Networks To Predict Factors Affecting Harmful Algal Blooms For Timely Alerting And Implementing Preventive Measures In Ireland's Marine Ecosystem., Nikolai Potapov

ICT

This study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.

Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems.