检索结果-内蒙古大学图书馆

WT Werkstattstechnik 2023年第4期113卷 158-164页

作者： Vollenkemper, Lukas Grumbach, Felix Kohlhase, Martin Reusch, Pascal Fachhochschule Bielefeld Center for Applied Data Science Schulstr. 10 Gütersloh33330 Germany

来源：评论

学校读者我要写书评

暂无评论

TOWARDS ENHANCING TIME SERIES CONTRASTIVE LEARNING: A DYNAMIC BAD PAIR MINING APPROACH 12

TOWARDS ENHANCING TIME SERIES CONTRASTIVE LEARNING: A DYNAMI...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Lan, Xiang Yan, Hanshu Hong, Shenda Feng, Mengling Saw Swee Hock School of Public Health Institute of Data Science National University of Singapore Singapore ByteDance China National Institute of Health Data Science Peking University China

Not all positive pairs are beneficial to time series contrastive learning. In this paper, we study two types of bad positive pairs that can impair the quality of time series representation learned through contrastive learning: the noisy positive pair and the faulty positive pair. We observe that, with the presence of noisy positive pairs, the model tends to simply learn the pattern of noise (Noisy Alignment). Meanwhile, when faulty positive pairs arise, the model wastes considerable amount of effort aligning non-representative patterns (Faulty Alignment). To address this problem, we propose a Dynamic Bad Pair Mining (DBPM) algorithm, which reliably identifies and suppresses bad positive pairs in time series contrastive learning. Specifically, DBPM utilizes a memory module to dynamically track the training behavior of each positive pair along training process. This allows us to identify potential bad positive pairs at each epoch based on their historical training behaviors. The identified bad pairs are subsequently down-weighted through a transformation module, thereby mitigating their negative impact on the representation learning process. DBPM is a simple algorithm designed as a lightweight plug-in without learnable parameters to enhance the performance of existing state-of-the-art methods. Through extensive experiments conducted on four large-scale, real-world time series datasets, we demonstrate DBPM's efficacy in mitigating the adverse effects of bad positive pairs. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of Echo State Networks From Design to Application

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 23-37页

作者： Sun, Chenxi Song, Moxian Cai, Derun Zhang, Baofeng Hong, Shenda Li, Hongyan School of Intelligence Science and Technology Beijing100871 China Peking University Health Science Center National Institute of Health Data Science Institute of Medical Technology Beijing100871 China

A recurrent neural network (RNN) has demonstrated its outstanding ability in sequence tasks and has achieved state of the art in many applications, such as industrial and medical. An echo state network (ESN) is a simple type of RNN and has emerged in the last decade as an alternative to gradient descent training-based RNN. The ESN is practical, conceptually simple, and easy to implement with a strong theoretical ground. It can avoid nonconverging and computationally expensive issues in gradient descent RNN methods. Since the ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced deep ESN opened the way to uniting the merits of deep learning and reservoir computing. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive. Successfully applying ESNs needs some experience. Thus, we reviewed over 300 related papers and provided a systematic overview for the first time. In this article, we categorize the related methods into classical ESN, DeepESN, and combination. Then, we analyze them from the perspective of network designs and specific applications. Finally, we discuss the challenges and opportunities by proposing open problems and future work. © 2020 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Plastic Waste Hotspot Detection System and Its Application to Measuring Environmental Improvement 1

A Plastic Waste Hotspot Detection System and Its Application...

引用

1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Sasaki, Shiori Nakamura, Ryota Wibowo, Prasetyo Musashino University Department of Data Science Tokyo Japan Musashino University Faculty of Data Science Tokyo Japan Politeknik Elektronika Negeri Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an 'Environmental AI-Human Actions integration' with marine plastic waste reduction actions. In this project, various local conditions are obtained to the system to realize an environmentally resilient society. The nature conservation activities such as marine plastic waste collection and reduction actions are implemented based on these conditions. In the process of integration of Environmental AI and Human Actions, the effects of these activities and activists' contribution to environmental improvement and global-scale knowledge are accumulated, shared, analyzed, and visualized through global-scale analysis. This paper describes the Plastic Waste Hotspot Detection system as one of the systems that integrate AI systems and human actions. This paper examines the feasibility and effectiveness of the system through the fieldwork tests in Phuket, Thailand and Surabaya, Indonesia. © 2024 IEEE.

关键词： Plastic recycling

来源：评论

学校读者我要写书评

暂无评论

data-Faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables 38

Data-Faithful Feature Attribution: Mitigating Unobservable C...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sun, Qiheng Xia, Haocheng Liu, Jinfei Zhejiang University China Institute of Blockchain and Data Security China Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

The state-of-the-art feature attribution methods often neglect the influence of unobservable confounders, posing a risk of misinterpretation, especially when it is crucial for the interpretation to remain faithful to the data. To counteract this, we propose a new approach, data-faithful feature attribution, which trains a confounder-free model using instrumental variables. The cluttered effects of unobservable confounders in a model trained as such are decoupled from input features, thereby aligning the output of the model with the contribution of input features to the target feature in the data generation. Furthermore, feature attribution results produced by our method are more robust when focusing on attributions from the perspective of data generation. Our experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approaches. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of Autonomous Weed Elimination using Maching Learning Techniques

Design of Autonomous Weed Elimination using Maching Learning...

引用

2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Giradkar, Aditya Adpawar, Rahul Agrawal, Rahul Dhule, Chetan Morris, Nekita Chavan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Department of Data Science Nagpur440016 India

ISBN: (纸本)9798350333602

The abstract presents a system that employs deep learning techniques to automatically detect weeds in agricultural fields. While the system's efficiency in identifying the presence of weeds is highlighted, the challenges faced by such systems are not explicitly mentioned. Recent techniques such as convolutional neural networks (CNN) are used in this system to teach the model using a dataset of images of weeds and crops. However, the challenges faced by such systems, such as the need for large annotated datasets and the potential for misclassifying weeds, are not addressed. The proposed objective of this study is to demonstrate the effectiveness of using deep learning methods to improve weed management techniques in agriculture. By accurately identifying weeds, farmers can use fewer herbicides and reduce the negative environmental impact of weed control. Overall, this study highlights the potential of deep learning methods in enhancing agricultural practices and reducing the negative environmental effects of weed control. © 2023 IEEE.

关键词： Weed control

来源：评论

学校读者我要写书评

暂无评论

Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

引用

IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

来源：评论

学校读者我要写书评

暂无评论

JobViz:Skill-driven visual exploration of job advertisements

引用

Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

来源：评论

学校读者我要写书评

暂无评论

DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： Expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Weakly-Supervised Keypoint Detector for Fishing Ground Estimation

Weakly-Supervised Keypoint Detector for Fishing Ground Estim...

引用

2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Takasan, Kazuki Iiyama, Masaaki Graduate School of Data Science Shiga University Shiga Japan Graduate School of Data Science Shiga University Japan Shiga Japan

ISBN: (纸本)9798218142186

Fishing ground estimation is an important task in the fishing industry. In this study, we focus on fisher's decision-making process based on sea surface temperature patterns, which we regard as a pattern recognition problem. Our method involves treating fishing grounds as keypoints and employing a deep learning-based keypoint detector to estimate their locations from sea surface temperature maps. However, the available amount of catch data, which is essential for annotation purposes, is insufficient. To tackle this issue, we utilize fishing vessel trajectory data as weak supervision, despite its limited information, owing to its abundance. Through experimental results, we demonstrated the superior performance of our method compared to approaches relying solely on catch data. © 2023 The Marine Technology Society (MTS).

关键词： Surface waters

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：