检索结果-内蒙古大学图书馆

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Hang, Haitian Tang, Xiu Sun, Jianling Bao, Lingfeng Lo, David Wang, Haoye College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer and Computing Science Hangzhou City University China

ISBN: (纸本)9798350317152

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we enable the implementation of auto-scaling strategies with customizable levels of robustness, which can be determined by considering various quantile levels of forecasts. Moreover, we enhance the adaptability of our strategy by incorporating different quantile levels through-out the entire decision horizon, allowing for dynamic adjustments in the conservatism of our auto-scaling decisions. This enables us to strike a balance between resource efficiency and system robustness. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving robust auto-scaling in cloud databases, while maintaining reasonable resource efficiency. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Conditional Prototypical Optimal Transport for Enhanced Clue Identification in Multiple Choice Question Answering 37th

Conditional Prototypical Optimal Transport for Enhanced Clu...

引用

37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Yang, Wangli Yang, Jie Li, Wanqing Guo, Yi School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Penrith Australia

ISBN: (纸本)9789819603473

This paper introduces the Conditional Prototypical Optimal Transport (CPOT) algorithm for clue identification in Multiple Choice Question Answering (MCQA) tasks. Existing clue-based methods suffer from inefficiencies, often relying on pseudo-labels or external resources, which introduce noise and additional computational demands. By contrast, the proposed CPOT method formulates clue identification as a sentence-oriented prototyping task, then further identifies sentences closest to prototype centroids as clues. Additionally, by leveraging the question and options as contextual guides, CPOT extends traditional Optimal Transport (OT) theory with constraints for unique assignment and uniform distribution across prototypes. This approach ensures semantically similar features converge within their prototypes and also maintains diversity among identified clues, enhancing answer accuracy. Empirical studies on several competitive benchmarks consistently demonstrate the superiority of our proposed method over different traditional approaches, with a substantial average improvement of 1.1—3.5 absolute percentage points in answering accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clue Identification Conditional Prototypical Optimal Transport Multiple Choice Question Answering

来源：评论

学校读者我要写书评

暂无评论

Analyze Factors Affecting Thread Consumption in a Garment and Develop a Machine Learning-based Prediction Model 8

Analyze Factors Affecting Thread Consumption in a Garment an...

引用

8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Ubayawickrema, Vindya Arachchi, Samantha Mathara Pallewatta, Pandula Karunanayaka, Kasun Maithripala, Akila University of Colombo School of Computing Colombo Sri Lanka Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798331509170

The garment manufacturing industry faces challenges related to excess thread stock, leading to increased write-off expenses and environmental impact. This research utilizes Machine Learning (ML) techniques to predict sewing thread consumption in producing full underwear briefs, considering key factors such as garment style, fabric/seam thickness, stitch length, seam type, and estimated wastage. Multiple ML models, including Linear Regression, Ensemble Models (Random Forest, Gradient Boosting), and Artificial Neural Networks (ANN), were employed to predict thread consumption for two thread types (TKT 120 and TKT 160). These models are integrated into a user-friendly Streamlit interface to make real-Time predictions based on user input, improving inventory management decision-making. ANN models outperformed others, with a validation dataset MAPE of \mathbf{1. 2 1 \%} for TKT 120 and 1.99 \% for TKT 160. This study shows how ML optimises thread usage, reduces inventory costs, and supports garment manufacturing sustainability. The ANN model performs well, but refinement and feature exploration could improve prediction accuracy and generalization. The research emphasises ML and usercentric tools for industry resource management efficiency. © 2024 IEEE.

关键词： Garment industry

来源：评论

学校读者我要写书评

暂无评论

Bird Detection in Microlight Aircraft Strip Using YOLOv8for Adventure Tourism 5th

Bird Detection in Microlight Aircraft Strip Using YOLOv8for ...

引用

5th International Conference on Advances in Distributed computing and Machine Learning, ICADCML 2024

作者： Paulson, Joemon John, Jaini Maria Asha, K. Department of Advanced Computing School of Information Technology St. Joseph’s University Karnataka Bangalore India

ISBN: (纸本)9789819735228

Microlight flying in India has given tourists an opportunity to experience the exhilaration of soaring in the sky, miles above the ground, without the high expenses of the usual flying options available. Flying birds poses significant threat to aviation safety, especially for smaller aircraft like microlight, due to the small size, bird targets are challenging to efficiently detect and identify in large-field observation. Advanced computer vision techniques have been used to detect and track birds near airfields and flight paths to reduce the risk. But in the tourism industry no such precautions are adapted with respect to microlight aircraft hence it’s a major concern to give attention. In this research, we examine the identification algorithm's use for real-time bird detection and its effects on the safety of microlight flying. The aim is to improve the capability of spotting birds near microlight aircraft by utilizing the advanced deep learning model, giving pilots timely knowledge to make wise decisions and take defensive action when required. The data has collected a wide range of bird photographs using web scraping techniques, including various species, lighting settings, and backgrounds. The findings of research highlight the importance of data gathering and analysis in aviation safety technology and show the smart computer vision system can protect both aircraft and human lives while adventure tourism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

Prediction of environmental factors in seafood cold chain transportation based on IAGA-ELM algorithm

引用

International Journal of Simulation and Process Modelling 2023年第3期20卷 159-171页

作者： Xu, E. Jin, Yujia Chen, Yinong Zhao, Shenghui Wang, Yang School of Information Science and Technology Bohai University Liaoning Province Jinzhou121013 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China School of Information Science and Technology Bohai University Liaoning Jinzhou121013 China School of Computing and Augmented Intelligence Arizona State University TempeAZ85287 United States

Cold chain transportation is often used to ensure the quality of seafood. In order to reduce the impact of environmental factors on product quality during cold chain transportation, an environmental factor prediction method based on improved adaptive genetic algorithm (IAGA) and extreme learning machine (ELM) is developed to predict the environmental factors generated in cold chain transportation. The environmental factor data composed of the gases released in the decay mechanism of cold fresh seafood is collected for training ELM with fast training speed and strong generalisation ability. Furthermore, the prediction model of environmental factors in the process of seafood cold chain transportation is established. Because initial connection weights and threshold values of traditional ELM have the characteristics of randomness, IAGA is used to address this issue. Compared with the existing ELM, GA-ELM, AGA-ELM and other models, our experimental results show that the proposed model obtains higher prediction accuracy and lower error rate. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predicting Employee Attrition & Employee Retention Period using Supervised Learning 6

Predicting Employee Attrition & Employee Retention Period us...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Muthugala, Dilina Arachchi, Samantha Mathara Pallewatta, Pandula Maithripala, Akila Seneviratne, Gihan University of Colombo School of Computing Colombo Sri Lanka Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798331517878

This paper employs machine learning (ML) to predict employee retention and attrition during recruitment. Predictions assist decision-makers in selecting the most suitable long-term candidates while reducing the likelihood of losing current employees unexpectedly. The study used a mid-scale apparel company's corporate human resource dataset, which included pre- and post-recruitment information. The algorithms 'SVM,' 'Decision Tree,' and 'Random Forest' were chosen for model construction based on the literature. Relevant metrics from six experiments using different inputs were presented. The Random Forest (RF) classifier outperformed the others in five of the six experiments. The RF algorithm outperformed both models, with an accuracy of 94.5% for retention and 89.7% for attrition prediction. Additional metrics, including Balanced Accuracy, Recall, Precision, and F1 Score, confirmed that RF is the most effective algorithm. A user interface predicted employee retention. A dashboard of active employees was created using the attrition prediction model. Demographic and job factors are distinguished to determine employee turnover causes. Gender was the most important demographic factor, while salary was the most important job factor. © 2024 IEEE.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples 41

Et Tu Certifications: Robustness Certificates Yield Better A...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Cullen, Andrew C. Liu, Shijie Montague, Paul Erfani, Sarah M. Rubinstein, Benjamin I.P. School of Computing and Information Systems University of Melbourne Parkville Australia Defence Science and Technology Group Adelaide Australia

In guaranteeing the absence of adversarial examples in an instance's neighbourhood, certification mechanisms play an important role in demonstrating neural net robustness. In this paper, we ask if these certifications can compromise the very models they help to protect? Our new Certification Aware Attack exploits certifications to produce computationally efficient norm-minimising adversarial examples 74% more often than comparable attacks, while reducing the median perturbation norm by more than 10%. While these attacks can be used to assess the tightness of certification bounds, they also highlight that releasing certifications can paradoxically reduce security. Copyright 2024 by the author(s)

关键词：

来源：评论

学校读者我要写书评

暂无评论

Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) for Depression Recognition with Facial Expression and Pupil Diameter

引用

IEEE Journal of Biomedical and Health Informatics 2023年第6期29卷 1-12页

作者： Yang, Minqiang Wu, Yushan Tao, Yongfeng Hu, Xiping Hu, Bin School of Information Science and Engineering Lanzhou University Lanzhou China Beijing Institute of Technology Beijing China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Gansu China

Facial expressions have been widely used for depression recognition because it is intuitive and convenient to access. Pupil diameter contains rich emotional information that is already reflected in facial video streams. However, the spatiotemporal correlation between pupillary changes and facial behavior changes induced by emotional stimuli has not been explored in existing studies. This paper presents a novel multimodal fusion algorithm - Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) to optimize the feature space and build a more robust depression recognition model, which innovatively combines the spatiotemporal relevance and complementarity between facial expression and pupil diameter features. TSTCCA explores the interaction between trials and obtains an effective fusion representation of two modalities from a trial subset related to depression. The experimental results show that TSTCCA achieves the highest accuracy of 78.81% with the subset of 25 trials. IEEE

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

OFDM-structure based waveform designs for integrated sensing and communication

引用

Science China(information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

来源：评论

学校读者我要写书评

暂无评论

What Worlds are We Designing For? 24

What Worlds are We Designing For?

引用

2024 Halfway to the Future Symposium, HttF 2024

作者： Liu, Jen Lin, Cindy Information Science Cornell University United States School of Interactive Computing Georgia Institute of Technology United States

ISBN: (纸本)9798400710421

Which relations matter for designing more just and habitable worlds? In this short paper, we address Ron Wakkary’s provocation, "How do we design for the planet and not just humans?" Our response assesses how more-than-human design can address existing patterns of exclusion and marginalization. We draw from HCI and related literature that have developed important entry points into uncovering what existing more-than-human relations require repair, what relations can be unmade, and what enduring relations we should refuse that have prevented longstanding and ongoing efforts for decolonization, justice, and liberation. © 2024 Copyright held by the owner/author(s).

关键词： Design for manufacturability

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：