Computer Sciences Commons^™

Improving Document Classification By Integrating Human-Crafted Semantic Knowledge, Lubomir Stanchev, Zachary Weinfeld

Computer Science and Software Engineering

Document classification is a pivotal task in various domains, warranting the development of robust algorithms. Among these, the Bidirectional Encoder Representations from Transformers (BERT) algorithm, introduced by Google, has proven to perform well when fine-tuned for the task at hand. Leveraging transformer architecture, BERT demonstrates stellar language understanding capabilities. However, the integration of BERT with a range of approaches has shown potential for further enhancing classification accuracy. This paper investigates three techniques that leverage semantic understanding to improve the performance of document classification models trained with BERT. First, we balance corpora afflicted by imbalanced training data distributions. Next, we substitute …

Developing Policies For The Ethical Use Of Artificial Intelligence In Higher Education And Libraries, April Sheppard, Matthew Mayton

Staff and Faculty Scholarship

This presentation will provide sample artificial intelligence policy language from various higher education institutions and academic libraries. Topics covered will include the acceptable use of AI in the classroom, the role of faculty in making AI-related decisions, syllabus statements, AI use and detection, AI literacy, and library policies regarding AI. Participants will be able to compare and contrast policies to help them develop their own policies that work for their unique organization.

Towards A Transparency-Based, Value-Sensitive Design Solution For Bias In Self-Driving Cars: An Ethical Violation Assessment And Risk Analysis Framework On Consumer-Held Values, Nada Ahmad Madkour

Master's Theses and Doctoral Dissertations

Background: The rapid growth of automated systems and artificial intelligence (AI), particularly, self-driving cars (SDCs), has attracted significant investments and can potentially contribute to humanity’s flourishing. However, before widespread adoption, it is important to address ethical violations such as bias in AI, highlighted by many real-world cases of bias in AI leading to unfair outcomes in tools like facial recognition, hiring software, and pedestrian detection. Bias in AI can lead to potentially fatal outcomes in SDCs, emphasizing the need for a thorough examination of bias in SDCs.

Purpose: To enhance AI ethics by providing tools to support transparency and value- …

A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …

Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das

Computer Science Faculty Research & Creative Works

UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …

Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …

Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed …

Uncovering The Causes Of Emotions In Software Developer Communication Using Zero-Shot Llms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski

Computer Science Faculty Research & Creative Works

Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by 'delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels. In …

Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra

Computer Science Faculty Research & Creative Works

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …

Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …

Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das

Computer Science Faculty Research & Creative Works

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther …

Analyzing Real-Time Insect Detection In Smart Connected Farms, Ashish Gupta, Vishesh Kumar Tanwar, Amit Nath Jha, Sajal K. Das

Computer Science Faculty Research & Creative Works

With a vision of smart connected farms, this research proposes an insect detection framework (InsDet) to identify the most harmful corn crop insect, known as corn rootworm beetle. InsDet employs an object detection model with varying sizes to localize the insects in sticky-trap images.

Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park

Computer Science Faculty Research & Creative Works

Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions - the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposes Freyr+, a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms. Freyr+ monitors each function's resource utilization in real-time …

Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ , so that a given submodular function $f(S)$ on $S$ is maximized and the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service …

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 …

A Comprehensive Survey On Pretrained Foundation Models: A History From Bert To Chatgpt, Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

Computer Science Faculty Research & Creative Works

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks across different data modalities. A PFM (e.g., BERT, ChatGPT, GPT-4) is trained on large-scale data, providing a solid parameter initialization for a wide range of downstream applications. In contrast to earlier methods that use convolution and recurrent modules for feature extraction, BERT learns bidirectional encoder representations from Transformers, trained on large datasets as contextual language models. Similarly, the Generative Pretrained Transformer (GPT) method employs Transformers as feature extractors and is trained on large datasets using an autoregressive paradigm. Recently, ChatGPT has demonstrated significant success in large language …

Are Academia And Industry Listening To Each Other? A Citation Analysis Of Ux Research Methods Resources, Abigail Bakke, Leonard Dibono

English Department Publications

Technical and Professional Communication (TPC) has been facing concerns of viability, in both its relationship with industry and its ability to build a relevant and valid body of research. TPC’s disconnection with industry may be reflected in its relationship to UX as well, despite both fields’ shared values. To better understand how TPC and User Experience (UX) are relating to each other, we conducted a citation analysis of a sample of SIGDOC papers and a sample of Nielsen Norman Group (NN/g) practitioner articles focused on research methods. The SIGDOC papers tended to cite TPC sources, while the NN/g articles cited …

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 …

Survey Of Memory Consolidation Techniques For Video Question Answering, Matthew Couts, Pha Nguyen, Khoa Luu

Inquiry: The University of Arkansas Undergraduate Research Journal

Video Question Answering (VideoQA) is a field of research focused on developing models that can engage in natural conversations with humans about the content of videos. Currently, the most successful approaches involve analyzing videos frame-by-frame, which is computationally and memory-intensive. To imitate human memory, the Atkinson-Shiffrin memory model can formulate the machine’s video understanding capability through Vision-Language Models. Reducing the number of frames processed by the model is a crucial operation in this approach category and can be handled by a memory consolidation algorithm. The memory consolidation algorithm should be able to determine the keyframes to transfer from short-term to …

How Increased Ransomware Attacks Have Impacted Hospitals In The United States, Mackenzie Dotson

Theses, Dissertations and Capstones

Introduction: The healthcare industry, particularly hospitals, have fallen prey to the alarming rise of ransomware attacks. In recent years, highly sophisticated cybergroups, armed with substantial funds and advanced technology, have intensified their focus on hospitals. Despite the advice against it, most hospitals have paid the ransom in order to regain access to their electronic systems and patient data, underlining the severity of these attacks.

Purpose of the Study: The purpose of this research was to evaluate the effects of ransomware attacks on hospitals in the US to determine if the patients were at risk due to hackers withholding patient information …

The Measure Of Efficiency And Effectiveness When Using Artificial Intelligence (Ai) In Radiology, Jordan Watts

Theses, Dissertations and Capstones

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its …

Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das

Computer Science Faculty Research & Creative Works

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …

Machine Intelligence With Associative Memory And Event-Driven Transaction History, Rao Mikkilineni, W. Patrick Kelly

Barowsky School of Business | Faculty Scholarship

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these …

Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das

Computer Science Faculty Research & Creative Works

Connected Vehicles Rely on Sophisticated Software Systems for Diverse Features, Including Navigation, Entertainment, Communication, and Safety Functions. as Technology Continues to Advance, the Reliance on Software in Connected Vehicles Becomes Increasingly Integral to their overall Performance and the Delivery of Innovative Features. Therefore, in the Domain of Software-Enabled Automobiles, the Implementation of over-The-Air (OTA) Software Updates is Deemed Essential for the Dissemination of Software and Fixes in Connected Vehicles. the Conventional Method of Addressing This Matter Entailed Manufacturers Undertaking the Task of Recalling Outdated Vehicles; However, the Central Issue Lies in the Considerable Challenge of Effectively Notifying Owners through Recall …

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

Computer Science Faculty Research & Creative Works

No abstract provided.

Personalized Federated Graph Learning On Non-Iid Electronic Health Records, Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal K. Das

Computer Science Faculty Research & Creative Works

Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For …

Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang

Computer Science Faculty Research & Creative Works

Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine …

Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo

Computer Science Faculty Research & Creative Works

In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …