检索结果-内蒙古大学图书馆

10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2020

作者： Jurado, Sergio Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789897584442

In this position paper a discussion is performed related to the importance of Energy Prediction Models (EPM) in the context of trading renewable energy in smart grids and the need to develop a new generation of Smart Meters (SMs) based on edge computing. If the electricity currently produced and consumed in the low voltage is expected to grow due to installations of PVs and the electrification of the system, we need to incentivise local energy trading and provide tools to make it possible. Currently, all energy trading mechanisms in the literature heavily rely on predictions, therefore, inaccuracy would be translated in low profitability of prosumer's investments or even worse, disappointment with the local energy markets. To guarantee robustness and reliability of predictions, we propose Flexible FIR as EPM to be used during the trading process and to integrate them in a Next Generation of SMs (NGSMs). In this document some reflections about new functionalities of NGSMs and first steps of a prototype are also addressed. © 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved

关键词： Smart meters

来源：评论

学校读者我要写书评

暂无评论

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED data CLASSIFICATION 37

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA...

引用

37th Annual European Simulation and Modelling Conference, ESM 2023

作者： Zhang, Xiao Paz, Iván Nebot, Àngela Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789492859280

When confronted with imbalanced datasets, traditional classifiers frequently struggle to correctly categorize samples from the minority class, adversely impacting the overall predictive performance of machine learning models. Current oversampling techniques generally focus on data interpolation through neighbor selection, often neglecting to uncover underlying data structures and relationships. This study introduces a novel application for RuLer, an algorithm originally developed for identifying sound patterns in the artistic domain of live coding. When adapted for data oversampling (as Ad-RuLer), the algorithm shows significant promise in addressing the challenges associated with imbalanced class distribution. We undertake a thorough comparative evaluation of Ad-RuLer against established oversampling algorithms such as SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE. The evaluation employs various classifiers including logistic regression, random forest, and XGBoost, and is conducted over six real-world biomedical datasets with varying degrees of imbalance. © 2023 ESM. All Rights Reserved.

关键词： Classification data synthesis Imbalanced data Oversampling Rule-based approach

来源：评论

学校读者我要写书评

暂无评论

Advantages of two quantum programming platforms in quantum computing and quantum chemistry

引用

Journal of cheminformatics 2025年第1期17卷 77页

作者： Pei-Hua Wang Wei-Yeh Wu Che-Yu Lee Jia-Cheng Hong Yufeng Jane Tseng Undergraduate Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Physics Division National Center for Theoretical Sciences No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://***/wangpeihua1231/quantum-programming-platform .

关键词： PennyLane Qiskit Quantum chemistry Quantum computing Quantum programming language

来源：评论

学校读者我要写书评

暂无评论

Path analysis of influencing factors on the masses of celebrities carrying goods based on the AMOS model 21

Path analysis of influencing factors on the masses of celebr...

引用

4th International Conference on Information Management and Management science, IMMS 2021

作者： Qu, Ying Yin, Shaoya School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Shijiazhuang050000 China

ISBN: (纸本)9781450384278

Under the Internet celebrity economy, live streaming has become a hot topic. This article explores the impact of the introduction of celebrity marketing methods in the live broadcast room on the consumption of the masses. Through the user behavior analysis model, find out the influence factors of the "star guest live room"model on the public's purchase intention. Based on AMOS model and factor analysis, Through 7 dimensions, Get live recognition, Celebrity popularity, Degree of trust, product quality, They are positively influencing people's purchase and consumer sharing. Consumer viewing attitude as an intermediary variable positively affects the results shared by consumers. By increasing the visibility of the live broadcast space, Pay attention to live broadcast quality, Increase interaction, etc. It can promote the purchase of consumers, Provide reference for the construction of the platform, and formulate reasonable marketing strategies in the live broadcast room, Reduce the blind purchase of consumers and promote rational purchase. Open up a new situation of live streaming shopping. © 2021 ACM. Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Factor analysis

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based hyperspectral and multispectral fusion techniques: Review, optimization, and perspectives

引用

Information Fusion 2025年

作者： Sen Luo Yurong Qian Lu Bai Yingying Fan Yuanxu Wang WeiQuan Kong Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi 830091 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China

Hyperspectral and multispectral image fusion (HMIF) can acquire high spatial and spectral resolution images, which overcomes the accuracy limitations of a single imaging device and effectively extends the application range of spectral images on various downstream tasks. In recent years, deep learning (DL)-based image fusion technology breaks through the many limitations of traditional methods, rapidly develops a variety of neural network fusion models and optimization strategies, and gradually overcomes a series of practical difficulties. However, a comprehensive overview of these deep learning fusion methods and various optimization techniques is still missing. In this paper, we comprehensively review the overall situation of deep learning techniques on HMIF, organize the fusion paradigms according to different researchers’ understanding and hypotheses of the problem, and sort out the common optimization strategies of neural network models, such as attention mechanism, multi-feature learning, combined with the variational and unmixing models of deep learning, in addition, we also make a detailed compendium of the actual problems and corresponding optimization schemes for the first time, which involves the spatial–temporal registration, generalizability enhancement and high-magnification fusion problems. Finally, we sort out and categorize the commonly used evaluation metrics for data ensembles, and look forward to the potential research directions in the HMIF problem. Through these works, we hope to provide a quick and clear reference for aspiring researchers in this area.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems 24

Trace-based Multi-Dimensional Root Cause Localization of Per...

引用

44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Zhang, Chenxi Dong, Zhen Peng, Xin Zhang, Bicheng Chen, Miao Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9798400702174

Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multidimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multidimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multidimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution. © 2024 ACM.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

SCTAP: Supporting Scenario-Centric Trigger-Action Programming based on Software-Defined Physical Environments 23

SCTAP: Supporting Scenario-Centric Trigger-Action Programmin...

引用

2023 World Wide Web Conference, WWW 2023

作者： Sun, Bingkun Shen, Liwei Peng, Xin Wang, Ziming Fudan University China School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9781450394161

The physical world we live in is accelerating digitalization with the vigorous development of Internet of Things (IoT). Following this trend, Web of Things (WoT) further enables fast and efficient creation of various applications that perceive and act on the physical world using standard Web technologies. A popular way for creating WoT applications is Trigger-Action Programming (TAP), which allows users to orchestrate the capabilities of IoT devices in the form of "if trigger, then action". However, existing TAP approaches don't support scenario-centric WoT applications which involve abstract modeling of physical environments and complex spatio-temporal dependencies between events and actions. In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. SCTAP defines a structured and conceptual representation for physical environments, which provides the required programming abstractions for WoT applications. Based on the representation, SCTAP defines a grammar for specifying scenario-centric WoT applications with spatio-temporal dependencies. Furthermore, we design a service-based architecture for SCTAP which supports the integration of device access, event perception, environment representation, and rule execution in a loosely-coupled and extensible way. We implement SCTAP as a WoT infrastructure and evaluate it with two case studies including a smart laboratory and a smart coffee house. The results confirm the usability, feasibility and efficiency of SCTAP and its implementation. © 2023 ACM.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

引用

7th International Conference on Internet Multimedia computing and Service, ICIMCS 2015

作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & Cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Gradient Amplification: An Efficient Way to Train Deep Neural Networks

引用

Big data Mining and Analytics 2020年第3期3卷 196-207页

作者： Sunitha Basodi Chunyan Ji Haiping Zhang Yi Pan Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural *** are several approaches proposed to address these challenges,one of which is to increase the depth of the neural *** deeper networks not only increase training times,but also suffer from vanishing gradients problem while *** this work,we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning *** perform experiments on VGG-19 and Resnet models(Resnet-18 and Resnet-34),and study the impact of amplification parameters on these models in *** proposed approach improves performance of these deep learning models even at higher learning rates,thereby allowing these models to achieve higher performance with reduced training time.

关键词： deep learning gradient amplification learning rate backpropagation vanishing gradients

来源：评论

学校读者我要写书评

暂无评论

IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

引用

Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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：