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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： Measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

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How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey

arXiv

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arXiv 2024年

作者： Qi, Yayun Li, Hongxi Song, Yiqi Wu, Xinxiao Luo, Jiebo Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China Guangdong Provincial Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen518172 China Department of Computer Science University of Rochester RochesterNY14627 United States

The exploration of various vision-language tasks, such as visual captioning, visual question answering, and visual commonsense reasoning, is an important area in artificial intelligence and continuously attracts the research community’s attention. Despite the improvements in overall performance, classic challenges still exist in vision-language tasks and hinder the development of this area. In recent years, the rise of pre-trained models is driving the research on vision-language tasks. Thanks to the massive scale of training data and model parameters, pretrained models have exhibited excellent performance in numerous downstream tasks. Inspired by the powerful capabilities of pretrained models, new paradigms have emerged to solve the classic challenges. Such methods have become mainstream in current research with increasing attention and rapid advances. In this paper, we present a comprehensive overview of how vision-language tasks benefit from pre-trained models. First, we review several main challenges in vision-language tasks and discuss the limitations of previous solutions before the era of pre-training. Next, we summarize the recent advances in incorporating pretrained models to address the challenges in vision-language tasks. Finally, we analyze the potential risks associated with the inherent limitations of pre-trained models and discuss possible solutions, attempting to provide future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Fairness Testing of Machine Learning Models using Combinatorial Testing in Latent Space 18

Fairness Testing of Machine Learning Models using Combinator...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Dahal, Arjun Shree, Sunny Lei, Yu Kacker, Raghu N. Richard Kuhn, D. The University of Texas at Arlington Department of Computer Science and Engineering Arlington United States National Institute of Standards and Technology Information Technology Laboratory Gaithersburg United States

ISBN: (纸本)9798331534677

Decision-making by Machine Learning (ML) models can exhibit biased behavior, resulting in unfair outcomes. Testing ML models for such biases is essential to ensure unbiased decision-making. In this paper, we propose a combinatorial testing-based approach in the latent space of a generative model to generate instances that assess the fairness of black-box ML models. Our approach involves a two-step process: generating t-way test cases in the latent space of a Variational AutoEncoder and performing fairness testing using the instances reconstructed from these test cases. We experimentally evaluated our approach against an approach that generates t-way instances in the input space for fairness testing. The results indicate that the latent-space approach produces more natural test cases while detecting the first fairness violation faster and achieving a higher ratio of discriminatory instances to the total number of generated instances. © 2025 IEEE.

关键词： Variational techniques

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Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

arXiv

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arXiv 2024年

作者： He, Yicheng Chen, Guangcheng Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The joint optimization of Neural Radiance Fields (NeRF) and camera trajectories has been widely applied in SLAM tasks due to its superior dense mapping quality and consistency. NeRF-based SLAM learns camera poses using constraints by implicit map representation. A widely observed phenomenon that results from the constraints of this form is jerky and physically unrealistic estimated camera motion, which in turn affects the map quality. To address this deficiency of current NeRF-based SLAM, we propose in this paper TS-SLAM (TS for Trajectory Smoothness). It introduces smoothness constraints on camera trajectories by representing them with uniform cubic B-splines with continuous acceleration that guarantees smooth camera motion. Benefiting from the differentiability and local control properties of B-splines, TS-SLAM can incrementally learn the control points end-to-end using a sliding window paradigm. Additionally, we regularize camera trajectories by exploiting the dynamics prior to further smooth trajectories. Experimental results demonstrate that TS-SLAM achieves superior trajectory accuracy and improves mapping quality versus NeRF-based SLAM that does not employ the above smoothness constraints. © 2024, CC BY.

关键词： Mapping

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Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response

Comprehensive analysis of gene expression profiles to radiat...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Luo, Xihaier McCorkle, Sean Park, Gilchan Lopez-Marrero, Vanessa Yoo, Shinjae Dougherty, Edward R. Qian, Xiaoning Alexander, Francis J. Yoon, Byung-Jun Computational Science Initiative Brookhaven National Laboratory UptonNY United States Texas A&m University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9781665468190

There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene expression is modulated after ionizing radiation exposure and investigating the presence of any dose-dependent gene expression patterns have broad implications for health risks from radiotherapy, medical radiation diagnostic procedures, as well as other environmental exposure. In this paper, we perform a comprehensive pathway-based analysis of gene expression profiles in response to low-dose radiation exposure, in order to examine the potential mechanism of gene regulation underlying such responses. To accomplish this goal, we employ a statistical framework to determine whether a specific group of genes belonging to a known pathway display coordinated expression patterns that are modulated in a manner consistent with the radiation level. Findings in our study suggest that there exist complex yet consistent signatures that reflect the molecular response to radiation exposure, which differ between low-dose and high-dose radiation. © 2022 IEEE.

关键词： Ionizing radiation

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High-performance computing at a crossroads

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science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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An Ensemble Learning Approach for COVID-19 Fact Verification 3

An Ensemble Learning Approach for COVID-19 Fact Verification

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Liu, Yangming Zhou, Yunlai National University of Singapore Department of Computer Science Singapore Xi'an Jiaotong University State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Engineering Xi'an710049 China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

The rapid development of social media platforms has resulted in a fast-paced spread of misinformation, which is especially common in the COVID-19 pandemic. In the global pandemic, the amount of COVID-19 related fake news generated online becomes enormous, which negatively results in public tension. Moreover, rumours are spread across platforms from different countries in such a global pandemic. Thus, automated fact-checking, which refers to automatically verifying the correctness of a claim, is of great importance. In this paper, we propose and examine ensemble learning approaches that exploit the power of multiple large-scale pre-trained language models. We conduct extensive experiments on traditional approaches, learning-based approaches, and our proposed ensemble methods. We successfully advance state-of-the-art performance by a significant margin. Further, we show that our ensemble method is especially suited to tasks with scarce training data, making it more suitable for many real-world applications. © 2022 IEEE.

关键词： COVID-19

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Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling under High-penetration of Renewable Energy

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IEEE Transactions on Sustainable Energy 2025年

作者： Wang, Jinning Li, Fangxing Fang, Xin Cui, Hantao She, Buxin Shuai, Hang Zhang, Qiwei Tomsovic, Kevin University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States NC State University Department of Electrical and Computer Engineering RaleighNC27606 United States Pacific Northwest National Laboratory RichlandWA99352 United States Johns Hopkins University Department of Environmental Health and Eng. BaltimoreMD21205 United States

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy. © 2025 IEEE.

关键词： high-penetration renewable energy open-source tool power system digital twin Power system scheduling stability constraints symbolic modeling

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STGAN: Spatial-temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction

arXiv

引用

arXiv 2025年

作者： Tong, Shilin Wu, Difei Liu, Xiaona Zheng, Le Du, Yuchuan Zou, Difan Department of Computer Science The University of Hong Kong Hong Kong Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University China Institute of IoT Shenzhen Polytechnic University China School of Information and Electronics Beijing Institute of Technology China Department of Computer Science Insitute of Data Science The University of Hong Kong Hong Kong

Pavement distress, manifested as cracks, potholes, and rutting, significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN [1], since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network (GNN) model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by the proposed STGAN model. Utilizing the ConTrack dataset [2], which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience. © 2025, CC BY.

关键词： Graph neural networks

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H8Control of Linear Uncertain Systems with Positivity Constraints 13

H8Control of Linear Uncertain Systems with Positivity Constr...

引用

13th Asian Control Conference, ASCC 2022

作者： Yang, Nachuan Li, Yuzhe Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9788993215236

This paper fills the literature gap by considering the worst-case H8 performance of continuous-time positive linear systems with structured uncertainty. The necessary and sufficient conditions on the positivity of structured uncertain systems are characterized using second-order cone programming (SOCP). More specifically, we reformulate the worst-case H8 control problem as a min-max semidefinite programming (SDP) based on the extended bounded real lemma for positive systems. Then we equivalently transform the min-max SDP into an SDP with linear matrix inequality (LMI) constraints, which turns out to be a convex programming. Finally, we provide several simulations to verify the effectiveness of the proposed results. © 2022 ACA.

关键词： H-infinity positive systems uncertain systems

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