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Articles 91 - 120 of 919
Full-Text Articles in Computer Sciences
Dynamic Anomaly Threshold Based Malicious Behavior Detection In Lora-Assisted Industrial Iot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das
Dynamic Anomaly Threshold Based Malicious Behavior Detection In Lora-Assisted Industrial Iot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das
Computer Science Faculty Research & Creative Works
Smart manufacturing, powered by Long Range (LoRa) communication-assisted Industrial Internet of Things (IIoT), offers significant benefits but also incurs security concerns due to device compromise. In addition, various application scenarios and inherent heterogeneity of IIoT devices induce significant challenges for reliable behavior detection of compromised devices. While existing work is mostly on detecting compromised devices and there exists limited work on modeling system behavior, an open question is how to model the per-device behavior in an IIoT deployment and how behavioral changes can be automatically adapted in different scenarios. This paper proposes Misbehav, a novel self-learning device behavior anomaly detection …
Iterative Recommendations Based On Monte Carlo Sampling And Trust Estimation In Multi-Stage Vehicular Traffic Routing Games, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das
Iterative Recommendations Based On Monte Carlo Sampling And Trust Estimation In Multi-Stage Vehicular Traffic Routing Games, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das
Computer Science Faculty Research & Creative Works
The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the …
Citrus: Cost And Ischemia Time Reduction Using Urban Air Mobility Solutions For Organ Transport, Debjyoti Sengupta, Anurag Satpathy, Arindam Khanda, Sajal K. Das
Citrus: Cost And Ischemia Time Reduction Using Urban Air Mobility Solutions For Organ Transport, Debjyoti Sengupta, Anurag Satpathy, Arindam Khanda, Sajal K. Das
Computer Science Faculty Research & Creative Works
Urban Air Mobility (UAM) involves the use of both piloted and autonomous aerial vehicles, ranging from small unmanned aerial vehicles (UAVs), such as drones, to larger passenger-carrying personal air vehicles (PAVs). This ground-breaking approach holds the potential to transform healthcare logistics by facilitating the fast and efficient transportation of organs between hospitals, addressing critical mobility challenges in healthcare delivery. However, scheduling organ transport is fraught with challenges, including (1) the limited availability of UAM vehicles at specific hospital branches, (2) the critical Cold Ischemia Time (CIT) for various organs, and (3) the high flying costs associated with moving organs from …
Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu
Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
White matter (WM) serves as a fundamental component of the brain providing essential structural support and facilitating the brain cognitive processes. Thus, an accurate and efficient description of the brain's white matter structure is essential for understanding brain function connectivity and development. In this work we used the deep model to combine the information of the WM fiber bundle shape and its related cortical folding patterns together representing the WM fiber bundle from diffusion MRI tractography into a pre-defined low-dimensional space and generate the numerical representation vector. This cortical-aware vector-quantized variational encoder (CA-VQVAE) framework leverages cortical locations and folding patterns …
Classiffication Of Mild Cognitive Impairment Based On Dynamic Functional Connectivity Using Spatio-Temporal Transformer, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Tianming Liu, Dajiang Zhu
Classiffication Of Mild Cognitive Impairment Based On Dynamic Functional Connectivity Using Spatio-Temporal Transformer, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding …
Real-Time Testbed For Studying Cyberattacks And Defense In Der-Integrated Smart Inverter Systems, M. Maliha, A. Oluyomi, M. Booge, S. Bhattacharjee, N. Braasch, P. Gomez, Sajal K. Das
Real-Time Testbed For Studying Cyberattacks And Defense In Der-Integrated Smart Inverter Systems, M. Maliha, A. Oluyomi, M. Booge, S. Bhattacharjee, N. Braasch, P. Gomez, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a grid-tied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man-in-the-middle (MITM) attacker. The MITM attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from …
Mgco: Mobility-Aware Generative Computation Offloading In Edge-Cloud Systems., Aswini Ghosh, Nelson Sharma, Shivendu Mishra, Rajiv Misra, Sajal K. Das
Mgco: Mobility-Aware Generative Computation Offloading In Edge-Cloud Systems., Aswini Ghosh, Nelson Sharma, Shivendu Mishra, Rajiv Misra, Sajal K. Das
Computer Science Faculty Research & Creative Works
Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient re source utilization in multi-access edge computing (MEC) systems. A key difficulty lies in leveraging real user trajectories to jointly optimize horizontal (inter-edge) and vertical (edge-to-cloud) task offloading decisions. This paper proposes a two-dimensional offloading scheme for a multi-layer edge–cloud architecture that enables collaborative task execution among resource-constrained edge nodes under mobility conditions. We present MGCO (Mobility-Aware Generative Computation Offloading), a generative AI–driven Transformer-based sequence-to-sequence Deep Q-Network (s2s-DQN) framework that learns from real-time trajectory data to anticipate user movement and optimize task placement dynamically. The Transformer architecture is …
Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu
Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu
Computer Science Faculty Research & Creative Works
We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked …
Fuzzy-Based Deep Reinforcement Learning For Suicidal Ideation Detection In Online Social Networks, Greeshma Lingam, Sajal K. Das
Fuzzy-Based Deep Reinforcement Learning For Suicidal Ideation Detection In Online Social Networks, Greeshma Lingam, Sajal K. Das
Computer Science Faculty Research & Creative Works
Suicidal ideation is a major psychological problem, and preventing this social risk is recognized as an important research topic. In reality, there can be several reasons why a person experiences suicidal ideation. Each individual can express views, emotions, and several types of symptoms related to suicidal ideation on the most popular social media platforms. In online social networks (OSNs), identification of suicidal ideation is one of the major challenging tasks. Existing studies have shown that the delay in understanding and identifying various risk factors can cause the suicidal event to occur. Due to the scarcity of data and understanding, the …
Circa: A Framework For Collaborative Identification Of Root Cause Analysis In Iot Microservices, Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das
Circa: A Framework For Collaborative Identification Of Root Cause Analysis In Iot Microservices, Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das
Computer Science Faculty Research & Creative Works
With continuous growth of IoT applications, service failures are quite inevitable. Due to the complexity and dynamics of IoT services, the root cause analysis (RCA) following an alert can assist in quickly resolving the possible faults. However, the time scales of metrics (e.g., CPU utilization, memory usage) generated by microservices and the dynamic topologies generated by calls between the Application Program Interfaces (APIs) are different. Moreover, the status of devices is an important aspect of RCA in IoT. All these make it extremely challenging to learn failure features of microservice metrics and API calls. Therefore, we propose a novel framework …
When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das
When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of …
Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das
Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …
Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Computer Science Faculty Research & Creative Works
In the original published article, Figure 3 appears with the Fig. 1 caption, Figure 1 appears with the Fig. 2 caption, and Figure 2 appears with the Fig. 3 caption. The article has been updated to correct this error.
Remenet: A Memory-Enhanced Gan Model For Intrusion Detection In Transportation Cyber-Physical Systems, Xin Wang, Lianbo Ma, Sajal K. Das, Zhonghua Liu
Remenet: A Memory-Enhanced Gan Model For Intrusion Detection In Transportation Cyber-Physical Systems, Xin Wang, Lianbo Ma, Sajal K. Das, Zhonghua Liu
Computer Science Faculty Research & Creative Works
Ensuring the safety and reliability of Transportation Cyber-Physical Systems (T-CPS) is critical. However, the increasing interconnectedness of T-CPS exposes them to sophisticated cyberattacks, necessitating robust intrusion detection systems (IDS) to safeguard against evolving threats. This paper aims to enhance the security of T-CPS by addressing two key challenges: effective anomaly detection and handling imbalanced datasets in intrusion detection tasks. In this paper, we propose ReMeNet (Reconstruction Memory Network), a novel intrusion detection model that combines a memory module with a GAN-based architecture to enhance anomaly detection and data reconstruction. To address the challenge of imbalanced datasets, we incorporate a Vector …
V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria
V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria
Computer Science Faculty Research & Creative Works
The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us …
Securing Federated Learning From Distributed Backdoor Attacks Via Maximal Clique And Dynamic Reputation System, Priyesh Ranjan, Ashish Gupta, Sajal K. Das
Securing Federated Learning From Distributed Backdoor Attacks Via Maximal Clique And Dynamic Reputation System, Priyesh Ranjan, Ashish Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning (FL) is a distributed learning paradigm that leverages the computational strength of local devices to collaboratively train a model. The clients train the local model on their respective devices and submit the weight updates to the server for aggregation. This paradigm allows the clients to experience diverse data without sharing their local data with other participants or the server. However, FL is susceptible to backdoor attackers that deliberately train the model on altered data, essentially trying to get favor on a specific subtask separated from the main task. In this work, we focus on powerful backdoor attackers who …
Rush: Rule-Based Scheduling For Low-Latency Serverless Computing, Priyanka Ashok Birajdar, Kush Anchalia, Anurag Satpathy, Sourav Kanti Addya
Rush: Rule-Based Scheduling For Low-Latency Serverless Computing, Priyanka Ashok Birajdar, Kush Anchalia, Anurag Satpathy, Sourav Kanti Addya
Computer Science Faculty Research & Creative Works
Serverless computing abstracts server management, enabling developers to focus on application logic while benefiting from automatic scaling and pay-per-use pricing. However, dynamic workloads pose challenges in resource allocation and response time optimization. Response time is a critical performance metric in serverless environments, especially for latency-sensitive applications, where inefficient scheduling can degrade user experience and system efficiency. This paper proposes RUSH (Rule-based Scheduling for Low-Latency Serverless Computing), a lightweight and adaptive scheduling framework designed to reduce cold starts and execution delays. RUSH employs a set of predefined rules that consider system state, resource availability, and timeout thresholds to make proactive, latency-Aware …
Message From Workshop Chairs, Sushil K. Prasad, Srishti Srivastava, Satish Puri, David Bunde, Shubbhi Taneja, Buddhi Ashan Mallika Kankanamalage
Message From Workshop Chairs, Sushil K. Prasad, Srishti Srivastava, Satish Puri, David Bunde, Shubbhi Taneja, Buddhi Ashan Mallika Kankanamalage
Computer Science Faculty Research & Creative Works
No abstract provided.
Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song
Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song
Computer Science Faculty Research & Creative Works
Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …
Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang
Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang
Computer Science Faculty Research & Creative Works
Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …
Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan
Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan
Computer Science Faculty Research & Creative Works
Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users’ emotional states. Traditional methods often rely on cameras or wearable sensors, which raise privacy concerns and add extra device burdens. In addition, existing acoustic-based methods struggle to maintain satisfactory performance when there is a distribution shift between the training dataset and the inference dataset. In this paper, we introduce FacER+, an active acoustic facial expression recognition system, which eliminates the requirement for external microphone arrays. FacER+ extracts facial expression features by analyzing the echoes of near-ultrasound signals emitted between the 3D facial …
Tackling Selfish Clients In Federated Learning, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das
Tackling Selfish Clients In Federated Learning, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. in this paper, we propose a Robust aggregation strategy for the FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true …
Prevention Of Attacks Via Requested Displayable Content, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Prevention Of Attacks Via Requested Displayable Content, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Computer Science Faculty Research & Creative Works
A method and system disable executable script in requested displayable content. Responsive to requesting displayable content, a non-executable code sequence and a mis-matched font file that maps a plurality of characters of the requested displayable content to the non-executable code sequence is received. The non-executable code sequence is displayed as a text string in accordance with the received mis-matched font file.
Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk
Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk
Computer Science Faculty Research & Creative Works
We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its …
Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain
Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain
Computer Science Faculty Research & Creative Works
The Advanced Particle-astrophysics Telescope (APT) is a planned space-based observatory designed to localize MeV to TeV transients such as gamma-ray bursts in real time using onboard computational hardware. The Antarctic Demonstrator for APT (ADAPT) is a prototype high-altitude balloon mission scheduled to fly during the 2025–26 season. Gamma-ray-induced scintillations in CsI tiles will be captured by perpendicular arrays of optical fibers running across both tile surfaces, as well as SiPM-based edge detectors to improve light collection and calorimetry. Signal samples are captured by analog waveform digitizer ASICs then sent to the front end of the computational pipeline, which is designed …
Optical Lens Attack On Deep Learning Based Monocular Depth Estimation, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Ziqi Zhang, Haydar Radha
Optical Lens Attack On Deep Learning Based Monocular Depth Estimation, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Ziqi Zhang, Haydar Radha
Computer Science Faculty Research & Creative Works
Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a singlecamera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived …
Extending Segment Tree For Polygon Clipping And Parallelizing Using Openmp And Openacc Compiler Directives, M. K. Buddhi Ashan, Satish Puri, Sushil K. Prasad
Extending Segment Tree For Polygon Clipping And Parallelizing Using Openmp And Openacc Compiler Directives, M. K. Buddhi Ashan, Satish Puri, Sushil K. Prasad
Computer Science Faculty Research & Creative Works
A segment tree is a versatile tree-based data structure over intervals or line segments efficiently supporting several computational operations such as stabbing query, segment arrangement, and planar point location, both theoretically and practically. Polygon clipping is a basic operation in domains such as Computer Graphics, Computer-aided Design, and Geographic Information Science (GIS). Given two polygons with n vertices, polygon clipping algorithms find the geometric intersection or union in O(n2) time using Foster's all-to-all edge intersection testing and O((n + k) logn) time using Vatti's sweep line-based method, where k is the number of intersections. No known segment tree implementation, including …
A Human-Centered Power Conservation Framework Based On Reverse Auction Theory And Machine Learning, Enrico Casella, Simone Silvestri, Denise A. Baker, Sajal K. Das
A Human-Centered Power Conservation Framework Based On Reverse Auction Theory And Machine Learning, Enrico Casella, Simone Silvestri, Denise A. Baker, Sajal K. Das
Computer Science Faculty Research & Creative Works
Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As …
Doing Personal Laps: Llm-Augmented Dialogue Construction For Personalized Multi-Session Conversational Search, Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. De Vries, Jeff Dalton, Faegheh Hasibi
Doing Personal Laps: Llm-Augmented Dialogue Construction For Personalized Multi-Session Conversational Search, Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. De Vries, Jeff Dalton, Faegheh Hasibi
Computer Science Faculty Research & Creative Works
The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. …
Trec Ikat 2023: A Test Collection For Evaluating Conversational And Interactive Knowledge Assistants, Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi
Trec Ikat 2023: A Test Collection For Evaluating Conversational And Interactive Knowledge Assistants, Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi
Computer Science Faculty Research & Creative Works
Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and responding in a naturalistic manner to user requests. The extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to enable researchers to test and evaluate their Conversational Search Agent (CSA). The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Personal Text Knowledge Base (PTKB) that defines the bespoke user personas. A total of 344 turns with approximately 26,000 passages are provided as assessments on relevance, as well …