Digital Commons Network^™

Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome, Yanjun Lyu, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Mild cognitive impairment (MCI) is recognized as a precursor to Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder of the brain. The neurodegeneration of brain connectivity networks plays a pivotal role in the development and progression of MCI. Traditionally, brain networks are generated using coarse-grained brain regions, where the regions serve as nodes and their functional or structural connections are used as edges. Recently, a novel finer scale brain folding patterns named 3hinge gyrus (3HG) was identified, which is defined as the conjunctions coming from three directions on gyral crests. 3HGs have been shown playing an important role in …

Eye-Gaze Guided Multi-Modal Alignment For Medical Representation Learning, Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li

Computer Science Faculty Research & Creative Works

In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationships. In this work, we propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of medical visual and textual features. We explore the natural auxiliary role of radiologists' eye-gaze data in aligning medical images and text and introduce a novel approach by using eye-gaze data, collected synchronously …

Communication-Efficient Federated Learning For Leo Constellations Integrated With Haps Using Hybrid Noma-Ofdm, Mohamed Elmahallawy, Tony T. Luo, Khaled Ramadan

Computer Science Faculty Research & Creative Works

Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federated learning (FL) with satellite communications (SatCom) so that numerous low Earth orbit (LEO) satellites can collaboratively train a machine learning model. However, the special communication environment of SatCom leads to a very slow FL training process up to days and weeks. This paper proposes NomaFedHAP, a novel FL-SatCom approach tailored to LEO satellites, that (1) utilizes high-altitude platforms (HAPs) as distributed parameter servers (PSs) to enhance satellite visibility, and (2) introduces non-orthogonal multiple access (NOMA) …

Towards Fine-Gained Services: Nfv-Assisted Tracking And Positioning Using Micro-Services For Multi-Robot Cooperation, Bo Yi, Lin Qiu, Jianhui Lv, Yingpu Nian, Xingwei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Robotics as a Service (RaaS) emerges as a new paradigm to motivate diversified potential of the "remote-controlled economy" for flexible and efficient service provision with the help of cloud computing. The multi-robot cooperation (MRC) technology has been widely used in various intelligent logistics scenarios, such as warehouses, factories, airports and subway stations, benefiting from the advantages of high operational efficiency and low labor cost. While promising, the corresponding challenge is that the service functions deployed on logistics robots (LRs) are more prone to failures such as resource exhaustion and error configuration in the multi-robot system (MRS). In this way, it …

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 …

Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

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 …

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 …

Move: Matching Game For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal Das

Computer Science Faculty Research & Creative Works

Autonomous Vehicles (AV s) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU's resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient …

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 …

Mime: Mobility-Induced Dynamic Matching For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Autonomous vehicles (AVs) execute compute intensive control operations like adjusting speed and steering, causing significant energy dissipation and latency due to resource limited onboard units (OBUs). Offloading these tasks to Roadside Units (RSUs) is a solution, but it faces challenges. First, the stringent latency requirements are impacted by the vehicle's stochastic velocity. Second, allocating limited RSU resources to numerous vehicles within its coverage area is difficult. This paper proposes the MIME framework to address these issues. We use Discrete Fourier transform (DFT) that computes the average velocity over an aperiodic velocity signal extracted from a real-world dataset. for resource allocation, …

Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar

Computer Science Faculty Research & Creative Works

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: to tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference …

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 …

Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the impact of these attacks on data collection by web crawlers, emphasizing their evasion of traditional detection methods. Through empirical evaluation, we uncover vulnerabilities in existing crawler mechanisms, particularly in code inspection, and propose enhancements to mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, emphasizing the necessity for robust countermeasures. Furthermore, we introduce a mitigation …

150, Scott Brande

All 3D Rock Models

Model 150 - pegmatite - igneous rock

SketchFab link: https://sketchfab.com/3d-models/150-135e099a4d4145848c7f26c0c2d5c438

Questionable Means To Admirable Ends; A Critique Of New Jersey’S Limited Certifications Of Eligibility Pilot, Gabriella Andrews

Student Works

No abstract provided.

Surveyor: Scratching For A Wild Moon, Nina Grigg

Harvey M. Applebaum ’59 Award

At the dawn of the space race, neither the moon as a destination nor man’s physical presence there were foregone conclusions. In order to get to that all important "first step," NASA had to dissect the Moon as a romantic symbol into concrete ground that space boots could tread on. Identifying these sites on a terrain so uniquely hostile to human habitation required remote exploration mediated by totally encapsulating technology. However, in the attempts of scientists and engineers to objectively explore the lunar wilderness, they unintentionally transformed it into an extension of the urban landscape. Once only conceivable as a …

Impact Of Financial Management System On Organizational Accountability And Responsibility: A Study Of Corporate Entities, Nur Farhanah Binti Marjuni, Junaid M. Shaikh

ASEAN Journal on Science and Technology for Development

This research investigates the impact of Financial Management Systems (FMS) on organizational accountability and responsibility in Brunei's manufacturing sector, utilizing a mixed-methods approach that includes primary data collected through questionnaires distributed to 41 local manufacturing companies and supplemented by secondary data from other ASEAN countries for comparative analysis. The primary focus is to assess the awareness and significance of efficient FMS implementation. Findings demonstrate a substantial influence of FMS on organizational accountability and responsibility, supported by an analysis of primary and secondary data from the ASEAN region. Nevertheless, this study acknowledges limitations such as its exclusive focus on Brunei and …

Echolocation On Manifolds, Kerong Wang

Honors Theses

We consider the question asked by Wyman and Xi [WX23]: ``Can you hear your location on a manifold?” In other words, can you locate a unique point x on a manifold, up to symmetry, if you know the Laplacian eigenvalues and eigenfunctions of the manifold? In [WX23], Wyman and Xi showed that echolocation holds on one- and two-dimensional rectangles with Dirichlet boundary conditions using the pointwise Weyl counting function. They also showed echolocation holds on ellipsoids using Gaussian curvature.

In this thesis, we provide full details for Wyman and Xi's proof for one- and two-dimensional rectangles and we show that …

Monitoring Aerosols With Time Resolution With A Rotating Drum Sampler Using La-Icpms For Elemental Analysis, John V. Piorkowski

Honors Theses

This thesis presents a comprehensive study on the characterization and application of a rotating drum sampler for air quality monitoring, with a focus on aerosol composition analysis using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS). The study aimed to determine (1) the cut-off points of the drum sampler to better understand its performance characteristics and (2) to investigate indoor air quality, specifically within makerspaces on Bucknell University’s campus. The research highlighted the novelty of applying LA-ICPMS for time-resolved aerosol composition analysis, demonstrating the potential for low-cost pollution concentration studies. The characterization experiments did not yield expected results, but the …

Experimental Evaluation Of Mesh Size In Physically Crosslinked Polymer Gels Using Dynamic Light Scattering, Duncan M. Hill

Honors Theses

Block copolymer gels are under consideration as drug delivery patches. Controlling and modeling the transport inside the gel is an important contribution to the successful delivery of pharmaceuticals. The diffusive transport of a solute – such as a therapeutic compound – within the gel can be described by the mesh size of the gel, which is defined as the distance between polymer chains. Existing experimental evaluations of mesh size do not work for block copolymer gels due to differences in the fundamental gel structure. As such, an experimental method of measuring mesh size of block copolymer gels using dynamic light …

The Precedence-Constrained Quadratic Knapsack Problem, Changkun Guan

Honors Theses

This thesis investigates the previously unstudied Precedence-Constrained Quadratic Knapsack Problem (PC-QKP), an NP-hard nonlinear combinatorial optimization problem. The PC-QKP is a variation of the traditional Knapsack Problem (KP) that introduces several additional complexities. By developing custom exact and approximate solution methods, and testing these on a wide range of carefully structured PC-QKP problem instances, we seek to identify and understand patterns that make some cases easier or harder to solve than others. The findings aim to help develop better strategies for solving this and similar problems in the future.

Evaluating Field Evidence Of Fine-Grained Sediment Abrasion In Gravel-Bedded Rivers: A Case Study Of The Lillooet And Suiattle Rivers In The Cascade Volcanic Arc (British Columbia, Canada, And Washington, Usa), Devin T. Johnson

Honors Theses

Steep, gravel-bedded rivers are important sites for studying gravel abrasion, where material transported as bed load experiences size reduction and rounding due to grain-to-grain collisions. Studying fine sediment (diameter) abrasion and grain shape changes in fluvial systems is more challenging due to difficulties in quantifying sediment shape at this scale, but it is assumed that fine particles traveling in suspension do not experience abrasion. Improvements in particle characterization technology now allow for the accurate, rapid quantification of particle shape for sediments as fine as 0.8 μm through dynamic image analysis. Here we use the Camsizer X2 (CX2) to test the …

Computationally Modeling The Human-Structure Interaction Response Of An Occupied Cantilevered Structure, Brennan Smith

Honors Theses

There is a limited understanding of the impact that passive human occupants have on a dynamic structural system, referred to as Human-Structure Interaction (HSI). Cantilevers are naturally prone to excessive vibrations due to their long unsupported spans, and cantilevered structures such as those commonly found in the seating area of a stadium facility or concert hall are designed to support a high density of occupancy.

This study determined that HSI in cantilevered structures can be modeled using a simple two-degree-of-freedom system. The results of the model were validated by data that was collected on a small-scale laboratory structure intentionally designed …

Performance Optimization For A Scintillating Glass Electromagnetic Calorimeter At The Eic, J. Crafts, Renee H. Fatemi, T. Horn, D. Kalinkin

Physics and Astronomy Faculty Publications

The successful realization of the EIC scientific program requires the design and construction of high-performance particle detectors. Recent developments in the field of scientific computing and increased availability of high performance computing resources have made it possible to perform optimization of multi-parameter designs, even when the latter require longer computational times (for example simulations of particle interactions with matter). Procedures involving machine-assisted techniques used to inform the design decision have seen a considerable growth in popularity among the EIC detector community. Having already been realized for tracking and RICH PID detectors, it has a potential application in calorimetry designs. A …

Should The Future Of Ais Conferences Be Hybrid?, Traci Carte, Matthew Nelson, Monica J. Garfield, Athanasia Pouloudi, Mani R. Subramani, Guillermo Rodríguez-Abitia, Souren Paul

Faculty Publications - Information Technology

Academic conferences provide a needed opportunity for academic community members to come together and share ideas. COVID-19 forced AIS to host conferences remotely for two years. From that experience, we learned a few things about virtualizing our conference activities including the potential for virtual conferences to widen participation and membership. In this paper, we reflect on that learning through a lens informed by reviewing published work on conference hybridization. We also make recommendations for how future conference chairs can think about AIS conferences. Changing how our conferences are delivered is risky, but simply returning to the old normal is also …

Resolving The Tension: Using Behavioral Law And Economics To Build Criminal Attempt Standards, Reilly S. Hobson

Student Works

No abstract provided.

Leandro V. State: The Challenges Of Litigating School Funding Equity In Hyper-Partisan Times, Mark Dorosin

Journal Publications

The Leandro case serves as a critical case study highlighting the limitations of litigation in addressing educational inequities, particularly in states committed to undermining public education. Despite the judiciary's constitutional role as an independent check on legislative actions, the politicization of state supreme courts has transformed them into partisan entities. This shift erodes public trust in the legal process and disproportionately harms marginalized groups, many of whom have historically relied on the courts to safeguard their constitutional rights, including equitable access to public education. The Leandro case underscores the challenges of achieving meaningful educational reform through litigation in a highly …

Machines Make Mistakes Too: Planning For Ai Liability In Contracting, Mark Sayre, Kyle Glover

Journal of Law, Technology, & the Internet

Recent advances in artificial intelligence have set off a frenzy of commercial activity, with companies fearful that they may fall behind if they are unable to quickly incorporate the new technology into their products or their internal processes. At the same time, numerous scholars from the machine learning community have warned of the fundamental risks that uninhibited use of artificial intelligence poses to society. The question is not whether artificial intelligence will cause harm, but when, and how. The certainty of future harm necessitates that legal scholars and practitioners examine the liability implications of artificial intelligence. While this topic has …