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A clinical EEG research platform to support progressive model construction 15

A clinical EEG research platform to support progressive mode...

引用

15th international Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2022

作者： Chen, Canbin Mei, Zhen Huang, Zhihua College of Computer and Data Science College of Software Fuzhou University Fuzhou China Fujian Medical University Epilepsy Center First Affiliated Hospital Fujian China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Machine learning algorithms have been widely used in clinical electroencephalography (EEG) studies to help diagnose various diseases and classify patient states. The application of machine learning algorithms in clinical EEG usually requires a large amount of annotated data, and annotating long-term clinical EEG is very expensive. Active learning algorithms can actively query the user for labels, and the number of samples to train a model can often be much lower than the number required in normal supervised learning. We have designed and developed a clinical EEG research platform to support progressive model construction, enabling researchers to focus on clinical EEG data analysis and algorithm design. The platform provides general auxiliary functions for various research tasks, including data format conversion, EEG visualization, annotation, dynamic loading algorithms, and progressive model construction. In our experiments, progressively building models can achieve the same or better performance by labeling only about 18% of the samples as compared to randomly selecting samples for labeling. © 2022 IEEE.

关键词： Electroencephalography

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PROBABILISTIC FINE-GRAINED URBAN FLOW INFERENCE WITH NORMALIZING FLOWS 47

PROBABILISTIC FINE-GRAINED URBAN FLOW INFERENCE WITH NORMALI...

引用

47th IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhong, Ting Yu, Haoyang Li, Rongfan Xu, Xovee Luo, Xucheng Zhou, Fan Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665405409

Fine-grained urban flow inference (FUFI) aims at enhancing the resolution of traffic flow, which plays an important role in intelligent traffic management. Existing FUFI methods are mainly based on techniques from image super-resolution (SR) models, which cannot fully capture the influence of external factors and face the ill-posed problem in SR tasks. In this paper, we propose UFI-Flow - Urban Flow Inference via normalizing Flow, a novel model for addressing the FUFI problem in a principled manner by using a single probabilistic loss. UFI-Flow therefore directly accounts for the ill-posed nature of the problem and learns spatial correlations between urban flow maps. In addition, an augmented distribution fusion mechanism is further proposed to reinforce the influence of external factors in the joint distribution inference. We conduct comprehensive experiments on real-world datasets to show the superiority of the proposed model compared to the state-of-the-art baseline approaches.

关键词： Spatial-temporal data mining urban flow inference normalizing flow mobile sensing

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Budget-Aware Scheduling for Hyperparameter Optimization Process in Cloud Environment 21st

Budget-Aware Scheduling for Hyperparameter Optimization Proc...

引用

21st international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Yao, Yan Yu, Jiguo Cao, Jian Liu, Zengguang Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan Peoples R China Qilu Univ Technol Big Data Inst Shandong Acad Sci Jinan Peoples R China Shandong Lab Comp Networks Jinan Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Hyperparameter optimization, as a necessary step for majority machine learning models, is crucial to achieving optimal model performance. Unfortunately, the process of hyperparameter optimization is usually computation-intensive and time-consuming due to the large searching space. To date, with the popularity and maturity of cloud computing, many researchers leverage public cloud services (i.e. Amazon AWS) to train machine learning models. Time and monetary cost, two contradictory targets, are what cloud machine learning users are more concerned about. In this paper, we propose HyperWorkflow, a workflow engine service for hyperparameter optimization execution, that coordinates between hyperparameter optimization job and cloud service instances. HyperWorkflow orchestrates the hyperparameter optimization process in a parallel and cost-effective manner upon heterogeneous cloud resources, and schedules hyperparameter trials using bin packing approach to make the best use of cloud resources to speed up the tuning processing under budget constraint. The evaluations show that HyperWorkflow can speed up hyperparameter optimization execution across a range of different budgets.

关键词： Hyperparameter optimization Cloud computing Resource scheduling

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MUST-AI: Multisource Surveillance Tool - Avian Influenza 28th

MUST-AI: Multisource Surveillance Tool - Avian Influenza

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Trevennec, Carlène Pompidor, Pierre Bououda, Samira Rabatel, Julien Roche, Mathieu INRAE ASTRE Univ Montpellier CIRAD INRAE Montpellier France LIRMM Université Montpellier CNRS Montpellier France ASTRE Univ Montpellier CIRAD INRAE Montpellier France CIRAD MontpellierF-34398 France CIRAD TETIS Univ Montpellier AgroParisTech CIRAD CNRS INRAE MontpellierF-34398 France

The multisource surveillance tool (MUST) is a platform for collecting, gathering, and visualizing different sources of information related to health events and highly pathogenic avian influenza in mammals (HPAIM). MUST-AI constitutes the first part of the MUST tool, which centralizes health information relating to cases of HPAIM since January 1, 2021, and comes from 3 different notification sources, an official notification source confirmed by public health institutions (i.e., WAHIS) and two other alternative unofficial sources that collect events from online media (PADI-web) and expert networks (ProMED). Owing to the use of natural language processing (NLP) algorithms, HPAIM events are represented on an interactive map associated with a graph that represents their distribution over a given time interval. This paper presents new tools and approaches for data fusion and experiments for selecting data to integrate into MUST that are related to HPAIM events. © 2024 The Authors.

关键词： data fusion

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Generation of Synthetic Trajectory Microdata from Language models

Generation of Synthetic Trajectory Microdata from Language M...

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international conference on Privacy in Statistical databases (PSD)

作者： Blanco-Justicia, Alberto Jebreel, Najeeb Moharram Manjon, Jesus A. Domingo-Ferrer, Josep Univ Rovira & Virgili Dept Comp Engn & Math Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783031139451;9783031139444

Releasing and sharing mobility data, and specifically trajectories, is necessary for many applications, from infrastructure planning to epidemiology. Yet, trajectories are highly sensitive data, because the points visited by an individual can be identifying and also confidential. Hence, trajectories must be anonymized before releasing or sharing them. While most contributions to the trajectory anonymization literature take statistical approaches, deep learning is increasingly being used. We observe that natural language sentences and trajectories share a sequential nature that can be exploited in similar ways. In this paper, we present preliminary work on generating synthetic trajectories using machine learning models typically used for natural language processing. Our empirical results attest to the quality of the generated synthetic trajectories. Furthermore, our methods allow discovering natural neighborhoods based on trajectories.

关键词： Privacy Synthetic data generation Mobility data

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An Overview of Tools and algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR data 26th

An Overview of Tools and Algorithms Used to Classify, Detect...

引用

26th international conference on Pattern Recognition, ICPR 2022

作者： Amakhchan, Wijdan El Kharki, Omar Cherif, El Khalil Wahbi, Miriam Yazidi Alaoui, Otmane Maatouk, Mustapha Boulaassal, Hakim GéoTéCa FSTT Abdelmalek Essaadi University Km 10 Ziaten. BP: 416 Tetouan Morocco Insituto Superior Technico Lisbon1049-001 Portugal

ISBN: (纸本)9783031377419

LIght Detection and Ranging (LIDAR) is gaining popularity more and more among scientists for developing predictive models in forest areas. Lidar point cloud data has a strong potential for application to manage forest resources thanks to its high accuracy. Obviously, the forest should be given more concern, to not be destroyed, causing economic and ecological damage which affects human lives as well. Therefore, using the developed technologies to protect it is crucial. The Lidar technology is one of the most used recently to meet this requirement. To highlight the big interest of Lidar data in the forest monitoring issue, this article introduces a summary of Lidar data sources Airborne Laser scanning (ALS), Terrestrial Laser scanning (TLS) and mobile mapping system (MMS) algorithms and methods used to classify and filter the point cloud lidar data in forest areas. © 2023, Springer Nature Switzerland AG.

关键词： Optical radar

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

Online Container Scheduling for Low-Latency IoT Services in ...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Beijing Normal University School of Artificial Intelligence Beijing100875 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

ISBN: (纸本)9798350345384

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning 40

Contrastive Energy Prediction for Exact Energy-Guided Diffus...

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40th international conference on Machine Learning

作者： Lu, Cheng Chen, Huayu Chen, Jianfei Su, Hang Li, Chongxuan Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech Tsinghua Bosch Joint ML Ctr T Inst AIBNRist CtrTHBI Lab Beijing Peoples R China Renmin Univ China Gaoling Sch AI Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Pazhou Lab Huangpu Guangzhou Peoples R China

Guided sampling is a vital approach for applying diffusion models in real-world tasks that embeds human-defined guidance during the sampling procedure. This paper considers a general setting where the guidance is defined by an (unnormalized) energy function. The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate. To address this challenge, we propose an exact formulation of the intermediate guidance as well as a novel training objective named contrastive energy prediction (CEP) to learn the exact guidance. Our method is guaranteed to converge to the exact guidance under unlimited model capacity and data samples, while previous methods can not. We demonstrate the effectiveness of our method by applying it to offline reinforcement learning (RL). Extensive experiments on D4RL benchmarks demonstrate that our method outperforms existing state-of-the-art algorithms. We also provide some examples of applying CEP for image synthesis to demonstrate the scalability of CEP on high-dimensional data. Code is available at https://***/thu-ml/CEP-energy-guided-diffusion

关键词： Reinforcement learning

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Development of a smart tool for the monitoring urban biodiversity using earth observation data, in-situ sensors and citizen science in the city of Thessaloniki 9

Development of a smart tool for the monitoring urban biodive...

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9th international conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Georgiadis, Charalampos Mallinis, Georgios Patias, Petros School of Civil Engineering The Aristotle University of Thessaloniki Greece School of Rural and Surveying Engineering The Aristotle University of Thessaloniki Greece

ISBN: (纸本)9781510668225

The aim of this study is the design and development of a conceptual and methodological framework that can be used for monitoring the condition of urban and sub urban green infrastructure (UGI) and the associated anthropogenic or natural induced pressures and threats to UGI biodiversity. Within this framework this manuscript describes in detail the development of a digital smart toolbox, which integrates data and information from satellite imagery, in-situ sensors and a smart mobile application developed to collect scientific data from public. The platform emphasizes the public participation of the urban residents for the protection and conservation of the environment. All this multi-source data can used for the sustainable management and protection of the biodiversity of urban and sub-urban UGI through the web based digital platform. In detail the following research and development activities were implemented. Originally the potential pressures and threats for the UGI biodiversity were identified and evaluated. Robust and transferable indicators for monitoring urban biodiversity using earth observation (EO) data were also formulated and developed. Different satellite EO data processing algorithms for the monitoring of potential pressures and threats to UGI biodiversity were also explored. Mobile sensors for data acquisition through citizen science to monitor biodiversity pressures and threats in UGI were also deployed. Finally, the integrated web-based platform was developed, that can be used to integrate data and provide information to the public and the authorities. © 2023 SPIE.

关键词： Remote sensing

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Comparative Analysis of Solar Power Forecasting Using Machine Learning Techniques

Comparative Analysis of Solar Power Forecasting Using Machin...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Amit Kumar Mittal Kirti Mathur Computer Engineering IET-DAVV Indore Madhya Pradesh India IIPS-DAVV Indore Madhya Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The research focuses on the development of a solar power forecasting system using machine learning techniques, with a primary focus on the intricate task of accurately predicting solar power generation. Drawing from historical data, the research employs various machine learning algorithms, including K-Nearest Neighbours (KNN), Gradient Boosting Regression Trees (GBRT), Long Short-Term Memory (LSTM), Multiple Layer Perceptron (MLP) to achieve highly precise forecasts. The proposed method includes data pre-processing and including techniques such as standardization and normalization. This ensures that the data is in an optimal state for utilization in machine learning models. This research study highlights the importance of data quality, as well as the reliability and availability of data from external sources, emphasizing their pivotal role in enhancing the accuracy of solar power predictions. This study utilizes data preprocessing, thorough model evaluation, and data visualization techniques for comprehending data patterns and results.

关键词： Accuracy Standardization Nearest neighbor methods Boosting data models Reliability Forecasting Long short term memory Regression tree analysis Meteorology

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