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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image 11

Deep Learning-based Risk Prediction Model for Recurrence-fre...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Weibin Wang, Fang Yang, Yunjun Li, Yinhao Liu, Jing Han, Xianhua Lin, Lanfen Tong, Ruofeng Hu, Hongjie Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Japan Zhejiang University Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Wenzhou Medical University Department of Radiology the First Affiliated Hospital Wenzhou China Ritsumeikan University College of Information Science and Engineering Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Yamaguchi University Yamaguchi Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781665492324

Risk prediction for recurrence is a critical task for patients with hepatocellular carcinoma (HCC). Effective prediction can evaluate treatment options and guide personalized medicine. Traditional approaches use clinical data to construct cox-regression models to predict the risk of recurrence. Recently, prognosis analysis based on image information of patient often has better performance compared with traditional approaches. In particular, deep learning has demonstrated its superiority in medical image processing. In this paper, we propose a deep learning-based model to predict the risk of recurrence. We collected 292 patients with HCC from two independent centers, which were divided into a training set, an internal validation set, and an external validation set. Our models were compared with traditional clinical models and better performance was obtained. Our proposed method achieves a C-index performance of 0.627 and 0.630 for the internal validation set and external validation set, respectively. © 2022 IEEE.

关键词： Forecasting

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Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： L. Ramalingam Raveendra N Amarnath V. Arun N. C. Sendhilkumar Rupavathy Ravi C. Srinivasan Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Mphasis Corporation Melissa Texas USA Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad India Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions.

关键词： Temperature measurement Temperature sensors Accuracy Medical services Wounds Predictive models Prediction algorithms Safety Sensors Monitoring

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Dual Contrastive Learning: Text Classification via Label-Aware data Augmentation

arXiv

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arXiv 2022年

作者： Chen, Qianben Zhang, Richong Zheng, Yaowei Mao, Yongyi SKLSDE School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Institution on Big Data and Brain Computing Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL. Copyright © 2022, The Authors. All rights reserved.

关键词： Text processing

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Can Large Language Models Serve as Evaluators for Code Summarization?

arXiv

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arXiv 2024年

作者： Wu, Yang Wan, Yao Chu, Zhaoyang Zhao, Wenting Liu, Ye Zhang, Hongyu Shi, Xuanhua Yu, Philip S. Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Big Data and Social Computing Lab University of Illinois Chicago Chicago United States Salesforce Research Palo Alto United States School of Big Data and Software Engineering Chongqing University Chongqing China

Code summarization facilitates program comprehension and software maintenance by converting code snippets into natural-language descriptions. Over the years, numerous methods have been developed for this task, but a key challenge remains: effectively evaluating the quality of generated summaries. While human evaluation is effective for assessing code summary quality, it is labor-intensive and difficult to scale. Commonly used automatic metrics, such as BLEU, ROUGE-L, METEOR, and BERTScore, often fail to align closely with human judgments. In this paper, we explore the potential of Large Language Models (LLMs) for evaluating code summarization. We propose CODERPE (Role-Player for Code Summarization Evaluation), a novel method that leverages role-player prompting to assess the quality of generated summaries. Specifically, we prompt an LLM agent to play diverse roles, such as code reviewer, code author, code editor, and system analyst. Each role evaluates the quality of code summaries across key dimensions, including coherence, consistency, fluency, and relevance. We further explore the robustness of LLMs as evaluators by employing various prompting strategies, including chain-of-thought reasoning, in-context learning, and tailored rating form designs. The results demonstrate that LLMs serve as effective evaluators for code summarization methods. Notably, our LLM-based evaluator, CODERPE, achieves an 81.59% Spearman correlation with human evaluations, outperforming the existing BERTScore metric by 17.27%. © 2024, CC BY.

关键词： Software agents

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Alzheimer Disease Detection System using AlexNet a novel Eight Layered Convolutional Neural Network

Alzheimer Disease Detection System using AlexNet a novel Eig...

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Information Technologies and Communications (GCITC), Global Conference on

作者： M. Geetha Jenifel M. Shyamala Devi D. Umanandhini Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

Alzheimer's disease (AD), an incurable brain ailment that is irreversible, affects thinking and memory while also shrinking the size of the mind as a whole. Alzheimer's is a neurological illness that causes severe memory loss and makes it difficult to carry out regular chores. The development of more effective treatments for AD depends on an early diagnosis of the condition. Finding Alzheimer's disease is a difficult and time-consuming task that requires a brain imaging report and competent people. This time-consuming and frequently inaccurate method of diagnosing Alzheimer's is traditional. There has been discussion of a different strategy that is quick, inexpensive, and more dependable. Better medical care and solutions can be provided with the use of artificial intelligence technologies. Due to distractions, technological flaws, cognitive biases, and weariness, human diagnosis performance suffers. However, AI-based diagnosis tools support physicians' safe detection and decision-making and are less prone to error. This study offers a clever and trustworthy method for identifying Alzheimer's disease and its potential early stage, moderate mental impairment. The given system accurately diagnoses Alzheimer's disease and its early stages from structural MRI images and is based on deep learning. The ability to predict whether patients with moderate cognitive impairment (MCI) may develop Alzheimer's disease is essential in clinical practice and has the potential to significantly improve clinical studies. To classify Alzheimer's disease and its prodromal stages, this initiative suggests combining MRI data with the Mini-Mental State Examination (MMSE), a neuropsychological test.

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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Quantitative Applications of Weather Satellite data for Nowcasting:Progress and Challenges

引用

Journal of Meteorological Research 2024年第3期38卷 399-413页

作者： Jun LI Jing ZHENG Bo LI Min MIN Yanan LIU Chian-Yi LIU Zhenglong LI WPaul MENZEL Timothy J.SCHMIT John L.CINTINEO Scott LINDSTROM Scott BACHMEIER Yunheng XUE Yayu MA Di DI Han LIN Innovation Centre for Fengyun Meteorological Satellite(FYSIC) National Satellite Meteorological CentreChina Meteorological AdministrationBeijing100081China School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University and Southern Laboratory of Ocean Science and EngineeringZhuhai519082China Key Laboratory of Geographic Information Science(Ministry of Education) East China Normal UniversityShanghai200241China Research Center for Environmental Changes Academia SinicaTaipei11529China Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-MadisonMadisonWI53706USA Sensors and Calibration Branch STAR/NESDISMadisonWI53706USA Wuxi University Wuxi214105China School of Computer Science Chengdu University of Information TechnologyChengdu610225China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing210044China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National and Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyFuzhou UniversityFuzhou350108China

Monitoring and predicting highly localized weather events over a very short-term period,typically ranging from minutes to a few hours,are very important for decision makers and public *** these events usually relies on radar observations through monitoring and *** advanced high-resolution imaging and sounding observations from weather satellites,nowcasting can be enhanced by combining radar,satellite,and other data,while quantitative applications of those data for nowcasting are advanced through using machine learning *** applications include monitoring the location,impact area,intensity,water vapor,atmospheric instability,precipitation,physical properties,and optical properties of the severe storm at different stages(pre-convection,initiation,development,and decaying),identification of storm types(wind,snow,hail,etc.),and predicting the occurrence and evolution of the *** observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm *** paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges,and future perspectives are also addressed and discussed.

关键词： weather satellite quantitative applications nowcasting pre-convection

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Meta Koopman Decomposition for Time Series Forecasting Under Temporal Distribution Shifts

SSRN

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SSRN 2024年

作者： Zhang, Yudong Wang, Xu Sun, Zhaoyang Wang, Pengkun Wang, Binwu Li, Limin Wang, Yang School of Data Science University of Science and Technology of China Hefei230026 China School of Software Engineering University of Science and Technology of China Hefei230026 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China

Time series forecasting facilitates various real-world applications and has attracted great research interests. In real-world scenarios, time series forecasting models confront a fundamental issue of temporal distribution shifts (TDS), i.e., the statistical properties of time series are evolving over time. In this paper, we utilize Koopman theory to address temporal distribution shifts. Koopman theory states any time series can be mapped into a Koopman space by proper measurement functions with their dynamics modeled by the infinite-dimensional linear Koopman operator. Therefore, time series under different distributions can be modeled by different Koopman operators. Considering the linearity of Koopman operators, the Koopman operators for representing time series under different distributions can be decomposed as a linear combination of a set of Koopman operators, which we termed as meta Koopman operators. We further theoretically show the infinite-dimensional Koopman operators can be approximated by finite matrix multiplications and the meta Koopman operators are equivalent to a set of matrices. Based on the analysis, we propose an auto-encoder framework for implementing the meta Koopman decomposition of time series, which is theoretically capable of handling TDS. Extensive experiments conducted on four real-world time series datasets demonstrate the superiority of the proposed model in tackling temporal distribution shifts. © 2024, The Authors. All rights reserved.

关键词： Time series

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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