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Articles 20041 - 20070 of 294652

Full-Text Articles in Physical Sciences and Mathematics

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 Jan 2025

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 …

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Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria Jan 2025

Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria

Computer Science Faculty Research & Creative Works

The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchain based solution to ensure Quality of Service (QoS) from third party IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a …

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Icrop+: An Edge-Boosted Crop Disease Detection System Via Tinyml And Lora Communication, Xu Tao, Jackson Butcher, Simone Silvestri, Sajal K. Das Jan 2025

Icrop+: An Edge-Boosted Crop Disease Detection System Via Tinyml And Lora Communication, Xu Tao, Jackson Butcher, Simone Silvestri, Sajal K. Das

Computer Science Faculty Research & Creative Works

Crop disease detection is essential for controlling dis-ease spread and minimizing agricultural losses. In this demo, we present an implementation of iCrop+, an end-to-end autonomous crop disease detection system that integrates on-device AI, low-power long-range communication (LoRa), and server-based deep learning to create a hybrid architecture suitable for real-world deployment. The prototype efficiently balances local processing and remote inference through category-based optimization, adaptive classification, and intelligent data transmission, ensuring that only the most informative segments are transmitted to the server. Built on low-cost devices such as Raspberry Pi, LoRa transceiver modules, and a laptop, the demo showcases its potential for …

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Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes Jan 2025

Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), …

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Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu Jan 2025

Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …

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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 Jan 2025

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 …

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Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu Jan 2025

Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

Computer Science Faculty Research & Creative Works

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain specific. To this end, in …

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Reindsplit: Reinforced Dynamic Split Learning For Pest Recognition In Precision Agriculture, Vishesh Kumar Tanwar, Soumik Sarkar, Asheesh K. Singh, Sajal K. Das Jan 2025

Reindsplit: Reinforced Dynamic Split Learning For Pest Recognition In Precision Agriculture, Vishesh Kumar Tanwar, Soumik Sarkar, Asheesh K. Singh, Sajal K. Das

Computer Science Faculty Research & Creative Works

To empower precision agriculture through distributed machine learning (DML), split learning (SL) has emerged as a promising paradigm, partitioning deep neural networks (DNNs) between edge devices and servers to reduce computational burdens and preserve data privacy. However, conventional SL frameworks' one-split-fits-all strategy is a critical limitation in agricultural ecosystems where edge insect monitoring devices exhibit vast heterogeneity in computational power, energy constraints, and connectivity. This leads to straggler bottlenecks, inefficient resource utilization, and compromised model performance. Bridging this gap, we introduce ReinDSplit, a novel reinforcement learning (RL)-driven framework that dynamically tailors DNN split points for each device, optimizing efficiency without …

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Fuzzy-Based Deep Reinforcement Learning For Suicidal Ideation Detection In Online Social Networks, Greeshma Lingam, Sajal K. Das Jan 2025

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 …

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When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das Jan 2025

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 …

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Circa: A Framework For Collaborative Identification Of Root Cause Analysis In Iot Microservices, Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das Jan 2025

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 …

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Mgco: Mobility-Aware Generative Computation Offloading In Edge-Cloud Systems., Aswini Ghosh, Nelson Sharma, Shivendu Mishra, Rajiv Misra, Sajal K. Das Jan 2025

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 …

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Message From Workshop Chairs, Sushil K. Prasad, Srishti Srivastava, Satish Puri, David Bunde, Shubbhi Taneja, Buddhi Ashan Mallika Kankanamalage Jan 2025

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.

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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 Jan 2025

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 …

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Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das Jan 2025

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 …

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Prime Factorization And Unit Calculations Of Quadratic Integer Rings, Gabriel F. Roca Jan 2025

Prime Factorization And Unit Calculations Of Quadratic Integer Rings, Gabriel F. Roca

Honors Undergraduate Theses

The failure of unique factorization in a ring leads to the investigation of the closest algebraic structure, which are prime ideals. Using generalizations that have helped solve questions such as Fermat's Last Theorem, there is interest to study the elements with a multiplicative inverse (units) via the geometry and arithmetic patterns that arise in quadratic integer rings, since they provide tools for other questions in mathematics, ranging from pure algebra to applications in cryptography, and more. Overall, the following thesis provides a small exposition on the theory of integral domains and some specific calculations.

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Flowing Together Or Alone: Impact Of Collaboration In The Metaverse, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, Langtao Chen Jan 2025

Flowing Together Or Alone: Impact Of Collaboration In The Metaverse, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, Langtao Chen

Research Collection School Of Computing and Information Systems

The metaverse is the next-generation Internet (Web3) that facilitates social connections and collaborations in a virtual world environment. Given the potential of the metaverse to provide more satisfying and effective means of remote collaborations, exploring the possibility of leveraging the metaverse for these endeavors is warranted. Therefore, an important question to address is whether greater engagement occurs when tasks are completed collaboratively versus individually in the metaverse. We address this question by drawing on flow and transportation theories to hypothesize the effect of carrying out a creative task in the metaverse collaboratively versus alone on one's cognitive absorption, a contextually …

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The Gender Wage Gap In An Online Labor Market: The Cost Of Interruptions, Abi Adams, Kotaro Hara, Kristy Milland, Chris Callison-Burch Jan 2025

The Gender Wage Gap In An Online Labor Market: The Cost Of Interruptions, Abi Adams, Kotaro Hara, Kristy Milland, Chris Callison-Burch

Research Collection School Of Computing and Information Systems

This paper analyses gender differences in working patterns and wages on Amazon Mechanical Turk, a popular online labour platform. Using information on 2 million tasks, we find no gender differences in task selection nor experience. Nonetheless, women earn 20% less per hour on average. Gender differences in working patterns are a significant driver of this wage gap. Women are more likely to interrupt their working time on the platform with consequences for their task completion speed. A follow-up survey shows that the gender differences in working patterns and hourly wages are concentrated amongst workers with children.

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Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji Jan 2025

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …

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Last Digit Tendency: Lucky Number And Psychological Rounding In Mobile Transactions, Hai Wang, Tian Lu, Yingjie Zhang, Yue Wu, Yiheng Sun, Jingran Dong, Wen Huang Jan 2025

Last Digit Tendency: Lucky Number And Psychological Rounding In Mobile Transactions, Hai Wang, Tian Lu, Yingjie Zhang, Yue Wu, Yiheng Sun, Jingran Dong, Wen Huang

Research Collection School Of Computing and Information Systems

The distribution of digits in numbers obtained from different sources reveals interesting patterns. The well-known Benford’s law states that the first digits in many real-life numerical data sets have an asymmetric, logarithmic distribution in which small digits are more common; this asymmetry diminishes for subsequent digits, and the last digit tends to be uniformly distributed. In this paper, we investigate the digit distribution of numbers in a large mobile transaction data set with 835 million mobile transactions and payments made by approximately 460,000 users in more than 300 cities. Although the first digits of the numbers in these mobile transactions …

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Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim Jan 2025

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

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Automated Program Refinement: Guide And Verify Code Large Language Model With Refinement Calculus, Yufan Cai, Zhe Hou, David Sanan, Xiaokun Luan, Yun Lin, Jun Sun, Jin Song Dong Jan 2025

Automated Program Refinement: Guide And Verify Code Large Language Model With Refinement Calculus, Yufan Cai, Zhe Hou, David Sanan, Xiaokun Luan, Yun Lin, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Recently, the rise of code-centric large language models (LLMs) appears to have reshaped the software engineering world with low-barrier tools like Copilot that can generate code easily. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into …

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Recdreamer: Consistent Text-To-3d Generation Via Uniform Score Distillation, Chenxi Zheng, Yihong Lin, Bangzhen Liu, Xuemiao Xu, Yongwei Nie, Shengfeng He Jan 2025

Recdreamer: Consistent Text-To-3d Generation Via Uniform Score Distillation, Chenxi Zheng, Yihong Lin, Bangzhen Liu, Xuemiao Xu, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

Current text-to-3D generation methods based on score distillation often suffer from geometric inconsistencies, leading to repeated patterns across different poses of 3D assets. This issue, known as the Multi-Face Janus problem, arises because existing methods struggle to maintain consistency across varying poses and are biased toward a canonical pose. While recent work has improved pose control and approximation, these efforts are still limited by this inherent bias, which skews the guidance during generation. To address this, we propose a solution called RecDreamer, which reshapes the underlying data distribution to achieve more consistent pose representation. The core idea behind our method …

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Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun Jan 2025

Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to be long-tailed distributed and some dishes of different categories exhibit subtle variations that are difficult to distinguish visually. In this paper, we present a framework empowered with large language models (LLMs) to address these challenges in food recognition. We first leverage LLMs to parse food images to generate food titles and ingredients. Then, we project the …

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Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu Jan 2025

Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of …

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Dims: Distributed Index For Similarity Search In Metric Spaces, Yifan Zhu, Chengyang Luo, Tang Qian, Lu Chen, Yunjun Gao, Baihua Zheng Jan 2025

Dims: Distributed Index For Similarity Search In Metric Spaces, Yifan Zhu, Chengyang Luo, Tang Qian, Lu Chen, Yunjun Gao, Baihua Zheng

Research Collection School Of Computing and Information Systems

Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metric spaces has gained significant attention. Metric spaces can accommodate any type of data and support flexible distanc e metrics, making similarity search in metric spaces beneficial for many real-world applications, such as multimedia retrieval, personalized recommendation, trajectory analytics, data mining, decision planning, and distributed servers. However, existing studies mostly focus on indexing metric spaces on a single machine, which faces efficiency and scalability limitations with increasing data volume and …

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Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw Jan 2025

Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains …

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Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun Jan 2025

Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time consuming, and labor intensive. Weakly supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully supervised semantic segmentation. In this article, our focus is on the WSSS with image-level labels, which is the most challenging form of WSSS. Our work has two parts. First, we conduct …

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Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun Jan 2025

Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun

Research Collection School Of Computing and Information Systems

Human pose estimation (HPE) models underperform in recognizing rare poses because they suffer from data imbalance problems (i.e., there are few image samples for rare poses) in their training datasets. From a data perspective, the most intuitive solution is to synthesize data for rare poses. Specifically, the rule-based methods apply manual manipulations (such as Cutout and GridMask) to the existing data, so the limited diversity of the data constrains the model. An alternative method is to learn the underlying data distribution via deep generative models (such as ControlNet and HumanSD) and then sample “new data” from the distribution. This works …

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Cold Atmospheric Plasma Induced Degradation Of Organophosphate Pesticides On Kevlar Swatches, Ta Chun Lin, Victor Somtochukwu Mbanugo, Boluwatife Stephen Ojo, Yue-Wern Huang, Marek Locmelis, Frank Daoru Han Jan 2025

Cold Atmospheric Plasma Induced Degradation Of Organophosphate Pesticides On Kevlar Swatches, Ta Chun Lin, Victor Somtochukwu Mbanugo, Boluwatife Stephen Ojo, Yue-Wern Huang, Marek Locmelis, Frank Daoru Han

Biological Sciences Faculty Research & Creative Works

Cold atmospheric plasma (CAP) was evaluated for degrading organophosphate pesticide (OP) residues (acephate, malathion, and dimethoate) from Kevlar fabrics. Fourier transform-Infrared (FTIR) spectroscopy, scanning electron microscope (SEM) imaging and tensile strength testing confirmed that CAP treatment preserved Kevlar's structural integrity and mechanical properties. Results show degradation efficiency increased with higher power, shorter discharge gaps, and longer exposure duration. Hyperspectral imaging supported the detection of plasma-induced spectral changes, reinforcing the presence of reactive species. The strong oxidative potential of CAP facilitated the rapid breakdown of OPs into nontoxic byproducts. These findings demonstrate the potential of CAP as a portable, energy-efficient solution …

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