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Articles 31 - 60 of 919
Full-Text Articles in Computer Sciences
You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin
You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin
Computer Science Faculty Research & Creative Works
We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …
Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman
Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman
Computer Science Faculty Research & Creative Works
Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …
Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang
Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang
Computer Science Faculty Research & Creative Works
Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …
Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das
Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …
Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma
Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma
Computer Science Faculty Research & Creative Works
Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …
Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria
Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria
Computer Science Faculty Research & Creative Works
Mining industry is rapidly transforming into an AI-driven cyber-physical ecosystem where safety and operational reliability depend on robust perception, resilient communication, trustworthy distributed intelligence and continuous monitoring of miners and equipment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair trajectory prediction and weaken the reliability of distributed learning systems. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for …
Smartflow: A Communication-Efficient Sdn Framework For Cross-Silo Federated Learning, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman
Smartflow: A Communication-Efficient Sdn Framework For Cross-Silo Federated Learning, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman
Computer Science Faculty Research & Creative Works
Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models while preserving data privacy. In such settings, clients repeatedly exchange model weights with a central server, making the overall training time highly sensitive to network performance. However, conventional routing methods often fail to prevent congestion, leading to increased communication latency and prolonged training. Software-Defined Networking (SDN), which provides centralized and programmable control over network resources, offers a promising way to address this limitation. To this end, we propose SmartFLow, an SDN-based framework designed to enhance communication efficiency in cross-silo FL. SmartFLow dynamically adjusts routing paths in response …
Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman
Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman
Computer Science Faculty Research & Creative Works
Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …
Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das
Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …
Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das
Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das
Computer Science Faculty Research & Creative Works
Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …
Real-Time Likelihood Map Generation To Localize Short-Duration Gamma-Ray Transients, Jeremy Buhler, Marion Sudvarg
Real-Time Likelihood Map Generation To Localize Short-Duration Gamma-Ray Transients, Jeremy Buhler, Marion Sudvarg
Computer Science Faculty Research & Creative Works
High-energy transient astrophysical phenomena, such as supernovae and binary neutron star mergers, benefit from a multi-wavelength investigation in which a space- or balloon-based omnidirectional telescope detects and localizes early high-energy emissions (such as a gamma-ray burst), then alerts a narrow-field follow-up instrument to observe the source. The high-energy telescope must provide a map that assigns to each sky location a likelihood that the source appears there. To issue prompt alerts despite limits on communication bandwidth and latency, it is desirable to compute this map aboard the high-energy telescope, but doing so requires rapid response while computing under stringent size, weight, …
Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler
Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler
Computer Science Faculty Research & Creative Works
FPGAs are widely deployed on high-energy astrophysics telescopes to read out sensor data from front-end electronics. To support continuous data streams or high trigger rates, FPGA logic may be employed to process raw sensor readout values, reducing the volume of data transmitted, processed, and stored by downstream CPU-based computational platforms. Across instruments, these FPGA-based processing pipelines often have similar semantics and share common stages. However, diverse telescope designs require unique implementations of the constituent algorithms, and the logic is often rewritten from scratch for a new instrument. Writing, simulating, and debugging firmware is difficult and time consuming. However, High-Level Synthesis …
Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg
Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg
Computer Science Faculty Research & Creative Works
The Antarctic Demonstrator for the Advanced Particle-astrophysics Telescope (ADAPT) gamma-ray/cosmic-ray instrument serves as a precursor to the proposed APT mission. The APT mission is designed to improve sensitivity in the MeV-TeV gamma-ray range by an order of magnitude compared to current missions and is optimized for dark-matter and multimessenger research. The ADAPT instrument uses scintillating fibers for particle tracking and sodium-doped cesium iodide (CsI:Na) tiles read out with wavelength shifting (WLS) fibers for imaging, with solid-state silicon photomultipliers (SiPMs) for calorimetry. It includes four layers of imaging calorimeter detectors and scintillating-fiber trackers, functioning both as a Compton and Pair telescope …
Performance Modeling And Improvements On The Grb Source Localization Streaming Pipeline Aboard The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Ye Htet, Ye Htet, Marion Sudvarg, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, Wenlei Chen, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler, Eric Burns
Performance Modeling And Improvements On The Grb Source Localization Streaming Pipeline Aboard The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Ye Htet, Ye Htet, Marion Sudvarg, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, Wenlei Chen, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler, Eric Burns
Computer Science Faculty Research & Creative Works
The Advanced Particle-astrophysics Telescope (APT) is a mission concept for a space-based gamma-ray telescope whose capabilities include prompt localization of gamma-ray bursts (GRBs) to support multi-wavelength and multi-messenger astrophysics. ADAPT — APT's balloon-borne prototype — can localize GRBs in well under a second using on-board computing hardware. ADAPT will partner with ground-based, fast-slewing optical telescopes, rapidly providing alerts that enable the partner to observe a short-duration burst within a few seconds of detection. In this work, we investigate the utility of having ADAPT issue progressively more accurate location estimates for a GRB as detected Compton events from the burst accumulate …
Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri
Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri
Computer Science Faculty Research & Creative Works
Similarity searches are a critical task in data mining. As datasets grow larger, exact nearest neighbor searches quickly become unfeasible, leading to the adoption of approximate nearest neighbor (ANN) searches. ANN has been studied for text data, images, and trajectories. However, there has been little effort to develop ANN systems for polygons in spatial database systems and geographic information systems. We present PolyMinHash, a system for approximate polygon similarity search that adapts MinHashing into a novel 2D polygon-hashing scheme to generate short, similarity-preserving signatures of input polygons. Minhash is generated by counting the number of randomly sampled points needed before …
Early Career Setback And Future Achievement In Professional Sports, Suman Kalyan Maity, Yang Wang, Nima Dehmamy, Victoria Medvec, Brian Uzzi, Dashun Wang
Early Career Setback And Future Achievement In Professional Sports, Suman Kalyan Maity, Yang Wang, Nima Dehmamy, Victoria Medvec, Brian Uzzi, Dashun Wang
Computer Science Faculty Research & Creative Works
A central tenet of human performance posits that past success is a key predictor of future outcomes. This principle underpins selection processes in various human endeavors, shaping opportunity, wage, and winner-take-all inequalities. Here we systematically examine the future performance of previous winners and non-winners across two sports contexts using two different empirical strategies. First, we track young athletes participating in world-class track and field competitions and compare the future performance of bronze medalists to those finishing just shy of the podium. Next, we study a novel natural experiment in tennis, where we compare future performances of 'lucky losers'—players who advanced …
Learning-Assisted Schedulability Analysis: Opportunities And Limitations, Sanjoy Baruah, Pontus Ekberg, Marion Sudvarg
Learning-Assisted Schedulability Analysis: Opportunities And Limitations, Sanjoy Baruah, Pontus Ekberg, Marion Sudvarg
Computer Science Faculty Research & Creative Works
We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a …
Modular Architecture For High-Performance And Low Overhead Data Transfers, Rasman Mubtasim Swargo, Engin Arslan, Md Arifuzzaman
Modular Architecture For High-Performance And Low Overhead Data Transfers, Rasman Mubtasim Swargo, Engin Arslan, Md Arifuzzaman
Computer Science Faculty Research & Creative Works
High-performance applications necessitate rapid and dependable transfer of massive datasets across geographically dispersed locations. Traditional file transfer tools often suffer from resource underutilization and instability due to fixed configurations or monolithic optimization methods. We propose AutoMDT, a novel Modular Data Transfer Architecture, to address these issues by employing a deep reinforcement learning agent to simultaneously optimize concurrency levels for read, network, and write operations. This solution incorporates a lightweight network-system simulator, enabling offline training of a Proximal Policy Optimization (PPO) agent in approximately 45 minutes on average, thereby overcoming the impracticality of lengthy online training in production networks. AutoMDT's modular …
Modeling And Optimizing Real-Time Telescope Interaction For Multi-Wavelength Observation Of Gamma-Ray Bursts, Ye Htet, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, James Buckley
Modeling And Optimizing Real-Time Telescope Interaction For Multi-Wavelength Observation Of Gamma-Ray Bursts, Ye Htet, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, James Buckley
Computer Science Faculty Research & Creative Works
Multi-wavelength observation of gamma-ray bursts (GRBs) requires real-time interaction among multiple telescopes. A gamma-ray telescope detects and localizes a GRB in the sky and must then communicate with an optical telescope to direct the latter toward the GRB as quickly as possible. We previously developed software for ADAPT, a suborbital gamma-ray telescope, to localize GRBs in real time, on a timescale shorter than that of the GRB itself. This work therefore studies progressive localization, in which ADAPT computes a series of increasingly accurate location estimates during a GRB to enable a partner instrument to more rapidly find it. We describe …
Connected-Component Labeling Using Hls For High-Energy Particle Physics Instruments, Nick Song, Marion Sudvarg, Roger Chamberlain
Connected-Component Labeling Using Hls For High-Energy Particle Physics Instruments, Nick Song, Marion Sudvarg, Roger Chamberlain
Computer Science Faculty Research & Creative Works
Many instruments used in high-energy particle physics observations, e.g., gamma-ray telescopes, use FPGAs for front-end signal processing of raw sensor data. The use of high-level synthesis (HLS) to express the signal processing algorithms has the potential to significantly reduce development time for new instruments of this type. We describe our experience with one of the computational stages in the signal processing pipeline, island detection, exploring its implementation across multiple configurations: 1D versus 2D islands, and 4-way versus 8-way connected-component labeling (CCL) in the 2D configuration. We report resource usage and performance for both configurations of 2D island detection, including the …
Proactllm: Proactive Conversational Information Seeking With Large Language Models, Shubham Chatterjee, Xi Wang, Shuo Zhang, Sajad Ebrahimi, Zhaochun Ren, Debasis Ganguly, Gareth Jones, Emine Arrousse, Hamed Zamani
Proactllm: Proactive Conversational Information Seeking With Large Language Models, Shubham Chatterjee, Xi Wang, Shuo Zhang, Sajad Ebrahimi, Zhaochun Ren, Debasis Ganguly, Gareth Jones, Emine Arrousse, Hamed Zamani
Computer Science Faculty Research & Creative Works
Large Language Models (LLMs) have transformed information access by enabling human-like text understanding and generation. This workshop explores the next step for conversational AI: building proactive information-seeking assistants that go beyond reactive question answering. We aim to investigate how LLMs can anticipate user needs, model complex context, support mixed-initiative interactions, integrate retrieval and external tools, personalize responses, adapt through feedback, and ensure fairness, transparency, and cognitive grounding. Bringing together experts from NLP, IR, HCI, and cognitive science, the workshop will serve as a timely forum for advancing intelligent, proactive dialogue systems. It will also foster interdisciplinary collaboration.
Dcrda: Deadline-Constrained Function Scheduling In Serverless-Cloud Platform, Priyanka Ashok Birajdar, Divya Meena, Anurag Satpathy, Sourav Kanti Addya
Dcrda: Deadline-Constrained Function Scheduling In Serverless-Cloud Platform, Priyanka Ashok Birajdar, Divya Meena, Anurag Satpathy, Sourav Kanti Addya
Computer Science Faculty Research & Creative Works
The serverless computing model frees developers from operational and management tasks, allowing them to focus solely on business logic. This paper addresses the computationally challenging function-container-virtual machine (VM) scheduling problem, especially under stringent deadline constraints. We propose a two-stage holistic scheduling framework called DCRDA targeting deadline-constrained function scheduling. In the first stage, the function-to-container scheduling is modeled as a one-to-one matching game and solved using the classical Deferred Acceptance Algorithm (DAA). The second stage addresses the container-to-VM assignment, modeled as a many-to-one matching problem, and solved using a variant of the DAA, the Revised-Deferred Acceptance Algorithm (RDA), to account for …
Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu
Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting 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 influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, …
The Implications Of Insecure Use Of Fonts Against Pdf Documents And Web Pages, Junjie Xiong, Mingkui Wei, Xiao Han, Zhuo Lu, Yao Liu
The Implications Of Insecure Use Of Fonts Against Pdf Documents And Web Pages, Junjie Xiong, Mingkui Wei, Xiao Han, Zhuo Lu, Yao Liu
Computer Science Faculty Research & Creative Works
This paper identifies the importance of the safe use of fonts in web and document security. We find multiple attack surfaces that can be exploited by an adversary using malicious fonts. We conduct a comprehensive evaluation of Portable Document Format (PDF) documents collected from the real world to investigate how an attacker can bypass PDF signatures. We further evaluate the potential security threats that an attacker can bring to web-based emails. Our study shows that various security issues may be caused by the inappropriate use of fonts, which are nevertheless overlooked in the past years. As such, guidelines promoting the …
Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari
Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari
Computer Science Faculty Research & Creative Works
Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …
“Silent Is Not Actually Silent”: An Investigation Of Toxicity On Bug Report Discussion, Mia Mohammad Imran, Jaydeb Sarker
“Silent Is Not Actually Silent”: An Investigation Of Toxicity On Bug Report Discussion, Mia Mohammad Imran, Jaydeb Sarker
Computer Science Faculty Research & Creative Works
Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resolving defects, yet their inherently problem-focused nature and emotionally charged context make them susceptible to toxic interactions. This study explores toxicity in GitHub bug reports through a qualitative analysis of 203 bug threads, including 81 toxic ones. Our findings reveal that toxicity frequently arises from misaligned perceptions of bug severity and priority, unresolved frustrations with tools, and lapses in professional communication. These toxic interactions not only derail productive discussions but also reduce the likelihood of actionable outcomes, …
Llput: Investigating Large Language Models For Bug Report-Based Input Generation, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran, Tarannum Shaila Zaman
Llput: Investigating Large Language Models For Bug Report-Based Input Generation, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran, Tarannum Shaila Zaman
Computer Science Faculty Research & Creative Works
Failure-inducing inputs play a crucial role in diagnosing and analyzing software bugs. Bug reports typically contain these inputs, which developers extract to facilitate debugging. Since bug reports are written in natural language, prior research has leveraged various Natural Language Processing (NLP) techniques for automated input extraction. With the advent of Large Language Models (LLMs), an important research question arises: how effectively can generative LLMs extract failure-inducing inputs from bug reports? In this paper, we propose LLPut, a technique to empirically evaluate the performance of three open-source generative LLMs-LLaMA, Qwen, and Qwen-Coder-in extracting relevant inputs from bug reports. We conduct an …
Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton
Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton
Computer Science Faculty Research & Creative Works
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. …
Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu
Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, …
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Computer Science Faculty Research & Creative Works
In multi-messenger astrophysics, signals of multiple types (e.g., gravitational waves, neutrinos, electromagnetic waves) are combined in an effort to learn more about the observed phenomena of interest. The Advanced Particle-astrophyics Telescope (APT) is a mission concept for a space-borne instrument that detects gammaray bursts (GRBs) omnidirectionally, facilitating multi-messenger observations by identifying and localizing celestial events of interest. Here, we describe the on-instrument computations for APT and its Antarctic Demonstrator (ADAPT) as well as techniques for follow-up observations of transient events.