检索结果-内蒙古大学图书馆

A robust optimization approach for measuring efficiency in stochastic DEA

AIP Conference Proceedings 2023年第1期2714卷

作者： Hengki Tamando Sihotang Syahril Efendi Muhammad Zarlis Herman Mawengkang Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara. FEBO Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara. FEBO Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara. FEBO Indonesia

The classic data envelopment analysis (DEA) concept assumes that decision making unit (DMU) inputs and outputs can be clearly measured because they have definite values. However, there are times when there is uncertainty in a situation. This study looks at how to build a DEA model with input and output uncertainty. In the DEA model, the uncertainty situation is approached using a stochastic concept. Furthermore, the robust optimization method, which can solve the uncertainty problem, is introduced. As a result, at the conclusion of this study, a robust optimization model based on the Stochastic DEA concept is introduced.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Framework for tackling stochastic DEA based on data-driven approach

引用

AIP Conference Proceedings 2023年第1期2714卷

作者： Hengki Tamando Sihotang Syahril Efendi Muhammad Zarlis Herman Mawengkang Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

Data Envelopment Analysis (DEA) is a linear programming-based technique used to evaluate the relative efficiency of multi-input and multi-output decision-making units based on observed data by comparing one decision-making unit (DMU) with another DMU. The efficacy assessment process sometimes involves a stochastic approach due to the uncertainty inherent in many real life problems, so the DEA model discussed in this paper is the stochastic DEA (SDEA) model. Data-driven decision-making is based entirely on the data involved. This paper proposes a framework for the completion of SDEA based on a data-driven approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

来源：评论

学校读者我要写书评

暂无评论

Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

引用

Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

来源：评论

学校读者我要写书评

暂无评论

CapST: An Enhanced and Lightweight Method for Deepfake Video Classification

arXiv

引用

arXiv 2023年

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Shahzad, Sahibzada Adil Institute of Information Science Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei116 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Dire Dawa University Ethiopia Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

The proliferation of deepfake videos, synthetic media produced through advanced Artificial Intelligence techniques, has raised significant concerns across various sectors, encompassing realms such as politics, entertainment, and security. In response, this research introduces an innovative and streamlined model designed to classify deepfake videos generated by five distinct encoders adeptly. Our approach not only achieves state-of-the-art performance but also optimizes computational resources. At its core, our solution employs part of a VGG19_bn as a backbone to efficiently extract features, a strategy proven effective in image-related tasks. We integrate a Capsule Network coupled with a Spatial-Temporal attention mechanism to bolster the model’s classification capabilities while conserving resources. This combination captures intricate hierarchies among features, facilitating robust identification of deepfake attributes. Delving into the intricacies of our innovation, we introduce an existing video-level fusion technique that artfully capitalizes on temporal attention mechanisms. This mechanism serves to handle concatenated feature vectors, capitalizing on the intrinsic temporal dependencies embedded within deepfake videos. By aggregating insights across frames, our model gains a holistic comprehension of video content, resulting in more precise predictions. Experimental results on an extensive benchmark dataset of deepfake videos (DFDM) showcase the efficacy of our proposed method. Notably, our approach achieves up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models, all while demanding fewer computational resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

来源：评论

学校读者我要写书评

暂无评论

Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) Anywhere

Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) An...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESCICOPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Tras-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska Omaha USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

In this paper, we continue our investigations on digital nomadism and the impact of COVID-19 pandemic on the work-related aspects and lifestyle of digital nomads (DN). The findings presented in this empirical study reflect the analysis of the impact of COVID-19 outbreak (and its waves) on the market economy and work-life boundaries of DNs as perceived from posts and comments gathered from a Reddit community during the period of early March 2020 until the end of 2022. From this point, our results indicate that the massification of remote work among formal workers in response to COVID-19 pandemic has impacted both the formal labor market and the DN ecosystem. As a consequence, we argue that digital nomadism tends to play a critical role beyond work from (almost) anywhere (WFA) in a post-COVID-19 era taking into account the novel facets of nomadic work-lifestyle.

关键词：

来源：评论

学校读者我要写书评

暂无评论

White blood cell subtype detection and classification

arXiv

引用

arXiv 2021年

作者： Praveen, Nalla Punn, Narinder Singh Sonbhadra, Sanjay Kumar Agarwal, Sonali Syafrullah, M. Adiyarta, Krisna Dept. of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

—Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the medical diagnosis. In practise white blood cell classification is performed by the haematologist by taking a small smear of blood and careful examination under the microscope. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The computer aided detection and diagnosis of the white blood cells tend to avoid the human error and reduce the time taken to classify the white blood cells. In the recent years several deep learning approaches have been developed in the context of classification of the white blood cells that are able to identify but are unable to localize the positions of white blood cells in the blood cell image. Following this, the present research proposes to utilize YOLOv3 object detection technique to localize and classify the white blood cells with bounding boxes. With exhaustive experimental analysis, the proposed work is found to detect the white blood cell with 99.2% accuracy and classify with 90% accuracy. © 2021, CC BY.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

引用

TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Reduction of the dataset of Baitul Mal aid recipients using the principal component analysis (PCA) method to improve C5.0 algorithm classification results

引用

AIP Conference Proceedings 2024年第1期3222卷

作者： Evi Susanti Sawaluddin Poltak Sihombing Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and develop. Selection is also needed to prevent aid from being distributed incorrectly. Therefore, the proposed solution is to utilize data mining techniques, especially classification methods (clustering) to identify potential aid recipients who truly deserve to receive aid. This research also uses the Principal Component Analysis (PCA) method to reduce attribute dimensions in the dataset to increase the accuracy of the classification process. By optimizing the classification process through appropriate attribute reduction, this research is expected to make a positive contribution to increasing the efficiency and accuracy of credit risk assessment in the banking sector. The results of this research are: accuracy after the dataset has been reduced by 2 (two) attributes has a higher value when compared to the dataset that has not been reduced, this happens because the attribute reduction process is carried out to reduce attributes that are not or less relevant. So it can be said that the reduction process influences the accuracy of the C5.0 algorithm classification results.

关键词：

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：