检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Verhelst, Théo Mercier, Denis Shrestha, Jeevan Bontempi, Gianluca Machine Learning Group Department of Computer Science Université Libre de Bruxelles Brussels Belgium Data Science Team Orange Belgium Brussels Belgium

Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets. © 2023, CC BY-NC-ND.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Civil Engineering Design in IoT: Leveraging Improved Swarm Intelligence Optimization 17

Civil Engineering Design in IoT: Leveraging Improved Swarm I...

引用

IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

作者： Fu, Weiheng Chen, Rongzhong Chen, Kaiwen Xie, Xiaojun Fuzhou University College of Computer and Data Science Fuzhou China Xiamen Meteorological Bureau Xiamen Key Laboratory of Straits Meteorology Xiamen China Florida Transportarion Engineeeing Inc Roadway Design Department Punta Gorda United States Fuzhou University Zhicheng College Office of Network Security and Information Fuzhou China

ISBN: (纸本)9798350351637

Nowadays, swarm intelligence algorithms are used to solve various problem in IoT environments because of their excellent performance, and the particle swarm algorithm(PSO) is a superior algorithm in swarm intelligence algorithms. The original PSO, however, exhibits certain limitations. For instance, the utilization of random initialization results in a less diverse population distribution for PSO. During the algorithm iteration process, excessively large or small weight factors can also detrimentally impact the performance of the algorithm. Moreover, when dealing with multi-modal problems, the PSO algorithm can quickly converge to local optimal because of its "precocious". In this paper, a Double-Population Particle Swarm Optimization(DP-PSO) algorithm is proposed. Firstly, the chaotic mechanism is used to initialize the population to make its distribution more uniform and obtain better convergence results. Secondly, the oscillation characteristic of the sine function is used to improve the updating method of weight by adding the sine factor, and the search area of the particle is expanded. Finally, the double-population variation mechanism is introduced to enhance the variation ability and improve the solving accuracy in the later iteration. The experimental results show that the DP-PSO is superior to other algorithms in performance and has obvious improvement in the ability to expand the search space of the population and jump out of the local optimal solution. When applied to cantilever beam design, DP-PSO also has good competitiveness in terms of stability, robustness, and scalability. © 2024 IEEE.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

SoMeLVLM: A Large Vision Language Model for Social Media Processing

arXiv

引用

arXiv 2024年

作者： Zhang, Xinnong Kuang, Haoyu Mou, Xinyi Lyu, Hanjia Wu, Kun Chen, Siming Luo, Jiebo Huang, Xuanjing Wei, Zhongyu Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China Department of Computer Science University of Rochester United States School of Computer Science Fudan University China Research Institute of Intelligent Complex Systems Fudan University China

The growth of social media, characterized by its multimodal nature, has led to the emergence of diverse phenomena and challenges, which calls for an effective approach to uniformly solve automated tasks. The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks. In this paper, we introduce a Large Vision Language Model for Social Media Processing (SoMeLVLM), which is a cognitive framework equipped with five key capabilities including knowledge & comprehension, application, analysis, evaluation, and creation. SoMeLVLM is designed to understand and generate realistic social media behavior. We have developed a 654k multimodal social media instruction-tuning dataset to support our cognitive framework and fine-tune our model. Our experiments demonstrate that SoMeLVLM achieves state-of-the-art performance in multiple social media tasks. Further analysis shows its significant advantages over baselines in terms of cognitive abilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision 23

Trillion Parameter AI Serving Infrastructure for Scientific ...

引用

Proceedings of the IEEE/ACM 10th International Conference on Big data Computing, Applications and Technologies

作者： Nathaniel C Hudson J. Gregory Pauloski Matt Baughman Alok Kamatar Mansi Sakarvadia Logan Ward Ryan Chard André Bauer Maksim Levental Wenyi Wang Will Engler Owen Price Skelly Ben Blaiszik Rick Stevens Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago Illinois United States Data Science and Learning Division Argonne National Laboratory Lemont Illinois US Data Science and Learning Division Argonne National Laboratory Lemont Illinois United States Globus University of Chicago Chicago Illinois United States Globus The University of Chicago Chicago Illinois United States Department of Computer Science University of Chicago Chicago Illinois USA

ISBN: (纸本)9798400704734

Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters---such as Huawei's PanGu-Σ. We describe a vision for the ecosystem of TPM users and providers that caters to the specific needs of the scientific community. We then outline the significant technical challenges and open problems in system design for serving TPMs to enable scientific research and discovery. Specifically, we describe the requirements of a comprehensive software stack and interfaces to support the diverse and flexible requirements of researchers.

关键词： artificial intelligence grid computing deep learning applications systems design survey

来源：评论

学校读者我要写书评

暂无评论

Default Prediction on Commercial Credit Big data Using Graph-based Variable Clustering

Default Prediction on Commercial Credit Big Data Using Graph...

引用

International Conference on Semantic Computing

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

Credit-lending organizations have resorted to the use of machine learning (ML) algorithms in the recent past to predict the probability of the default of a business. Explainability of the decisions made by the traditional statistical algorithms like Logit models brings transparency to every stakeholder involved in the process. On the other hand, machine learning models like XGBoost and Neural Nets have achieved better accuracy scores, but their decisions are not easily comprehensible. In this paper, we propose a graph based variable clustering (GVC) method as a filter based approach to select prominent features while retaining as much variance as possible. Our experiments show that our GVC approach is not only almost 40 times faster than the existing variable clustering methods but retains retains 5% more variance than the existing *** feature set from GVC approach has performed better with an increase of 6% accuracy on an average. The predictions on the feature set from GVC were 98% accurate using XGBoost algorithm.

关键词： Machine learning algorithms Runtime Semantics Neural networks Clustering algorithms Machine learning Organizations

来源：评论

学校读者我要写书评

暂无评论

A Multi-Language Toolkit for the Semi-Automated Checking of Research Outputs

IEEE Transactions on Privacy

引用

IEEE Transactions on Privacy 2025年第1期2卷 55-66页

作者： Preen, Richard J. Albashir, Maha Davy, Simon Smith, Jim University of the West of England Department of Computer Science and Creative Technologies BristolBS16 1QY United Kingdom University of Oxford Bennett Institute for Applied Data Science OxfordOX1 2JD United Kingdom

This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches. The toolkit is composed of a lightweight Python package that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This package adds functionality to (i) automatically identify potentially disclosive outputs against a range of commonly used disclosure tests;(ii) apply optional disclosure mitigation strategies as requested;(iii) report reasons for applying SDC;and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow and maintain auditable records. This creates an explicit change in the dynamics so that SDC is something done with researchers rather than to them, and enables more efficient communication with checkers. A graphical user interface supports human checkers by displaying the requested output and results of the checks in an immediately accessible format, highlighting identified issues, potential mitigation options, and tracking decisions made. The major analytical programming languages used by researchers (Python, R, and Stata) are supported by providing front-end packages that interface with the core Python back-end. © 2024 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Evaluation of CAN Bus Security Vulnerabilities and Potential Solutions

Evaluation of CAN Bus Security Vulnerabilities and Potential...

引用

Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Asma Alfardus Danda B. Rawat Department of Electrical Engineering and Computer Science Data Science and Cybersecurity Center (DSC2) Howard University Washington D.C USA

The past decade has seen a significant increase in the automobile industry, which has come with some serious challenges and threats. Modem vehicles are now made up of complex mechanical systems, as well as sophisticated electronic devices and connections to the outside world. Various electronic devices utilize standard communication protocols, including the Controller Area Network (CAN), to establish communication with each other. Unfortunately, CAN lacks some fundamental security features, such as encryption and authentication, which makes it vulnerable to security attacks. This can lead to accidents and financial losses for the users of these vehicles. To address this issue, researchers have proposed a number of security measures, such as cryptography and Intrusion Detection Systems (IDS). This paper addresses the security vulnerabilities associated with CAN and proposes potential solutions to overcome its limitations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective

arXiv

引用

arXiv 2023年

作者： Seyedi, Zahra Rahmati, Farhad Ali, Mohammad Liu, Ximeng Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme’s security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. © 2023, CC0.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

引用

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar datasets?

Self-supervised Benchmark Lottery on ImageNet: Do Marginal I...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Utku Ozbulak Esla Timothy Anzaku Solha Kang Wesley De Neve Joris Vankerschaver Center for Biosystems and Biotech Data Science Ghent University Global Campus Republic of Korea Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark lottery". An important benchmark in the field of machine learning and computer vision is the ImageNet where newly proposed models are often showcased based on their performance on this dataset. Given the large number of self-supervised learning (SSL) frameworks that has been proposed in the past couple of years each coming with marginal improvements on the ImageNet dataset, in this work, we evaluate whether those marginal improvements on ImageNet translate to improvements on similar datasets or not. To do so, we investigate twelve popular SSL frameworks on five ImageNet variants and discover that models that seem to perform well on ImageNet may experience significant performance declines on similar datasets. Specifically, state-of-the-art frameworks such as DINO and Swav, which are praised for their performance, exhibit substantial drops in performance while MoCo and Barlow Twins displays comparatively good results. As a result, we argue that otherwise good and desirable properties of models remain hidden when benchmarking is only performed on the ImageNet validation set, making us call for more adequate benchmarking. To avoid the "benchmark lottery" on ImageNet and to ensure a fair benchmarking process, we investigate the usage of a unified metric that takes into account the performance of models on other ImageNet variant datasets.

关键词： Measurement computer vision Computational modeling Neural networks Machine learning Self-supervised learning Benchmark testing

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：