检索结果-内蒙古大学图书馆

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

What Drives Success in Data Science Projects: A Taxonomy of Antecedents

What Drives Success in Data Science Projects: A Taxonomy of ...

引用

International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Gökay, Gonca Tokdemir Nazlıel, Kerem Şener, Umut Gökalp, Ebru Gökalp, Mert Onuralp Gençal, Nergiz Dağdaş, Gizemnur Eren, P. Erhan Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Computer Engineering Hacettepe University Ankara Turkey Data Analytics Center Tupras İstanbul Turkey

ISBN: (纸本)9783031270987

Organizations have been trying to reshape their business processes and transform them into a smart environment to attain sustainable competitive advantage in their markets. Data science enables organizations to define interconnected and self-controlled business processes by analyzing the massive amount of unstandardized and unstructured high-speed data produced by heterogeneous Internet of Things devices. However, according to the latest research, the success rate of data science projects is lower than other software projects, and the literature review conducted reveals a fundamental need for determining success drivers for data science projects. To address these research gaps, this study investigates the determinants of success and the taxonomy of antecedents of success in data science projects. We reviewed the literature systematically and conducted an expert panel by following a Delphi method to explore the main success drivers of data science projects. The main contributions of the study are twofold: (1) establishing a common base for determinants of success in data science projects (2) guiding organizations to increase the success of their data science projects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Project management

来源：评论

学校读者我要写书评

暂无评论

Active Data Reconstruction Attacks in Vertical Federated Learning

Active Data Reconstruction Attacks in Vertical Federated Lea...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Vu, Minh N. Jeter, Tre'R. Alharbi, Raed Thai, My T. University of Florida Department of Computer and Information Science and Engineering GainesvilleFL32611 United States Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350324457

Vertical Federated Learning (VFL) stands out as a promising approach to safeguard privacy in collaborative machine learning, allowing multiple entities to jointly train models on vertically partitioned datasets without revealing private information. While recent years have seen substantial research on privacy vulnerabilities and defense strategies for VFL, the focus has primarily been on passive scenarios where attackers adhere to the protocol. This perspective undermines the practical threats since the attackers can deviate from the protocol to improve their inference capabilities. To address this gap, our study introduces two innovative data reconstruction attacks designed to compromise data privacy in an active setting. Essentially, both attacks modify the gradients computed during the training phase of VFL to breach privacy. Our first attack uses an Active Inversion Network exploiting a small portion of known data in the training set to coerce the passive participants into training an auto-encoder for the reconstruction of their private data. The second attack, Active Generative Network, utilizes the knowledge of the training data distribution to guide the system into training a conditional generative network (C-GAN) for feature inferences. Our experiments confirm the efficacy of both attacks in inferring private features from real-world datasets. © 2023 IEEE.

关键词： Data Privacy Data Reconstruction Attacks Vertical Federated Learning

来源：评论

学校读者我要写书评

暂无评论

A Study on Modified Walsh-Hadamard Code Division Multiplexing with Enhanced Anti-Periodic-Blockage Capability

A Study on Modified Walsh-Hadamard Code Division Multiplexin...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Toshiharu Kojima Shinya Sotome Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

In helicopter satellite communications, the periodic blockage (PB) of the transmission channel occurs due to rotor blades. Modified Walsh-Hadamard code division multiplexing (MWHCDM) is suitable for helicopter satellite communications because it has the anti-PB capability, corresponding to 2-branch time diversity. This paper proposes MWHCDM with enhanced anti-PB capability. The proposed MWHCDM implicitly performs 4-branch time diversity processing through multiplexing by trading off with spectral efficiency. computer simulations show that the proposed MWHCDM achieves excellent bit error rate (BER) performance close to the lower bound in the PB channel with zero-forcing (ZF) and minimum mean square error (MMSE) detection. As a result, the proposed MWHCDM outperforms the conventional MWHCDM and the simple (non-multiplexed) 4-branch time diversity.

关键词： Lower bound Code division multiplexing Spectral efficiency computer simulation Blades Bit error rate Helicopters Rotors Mean square error methods Satellite communications

来源：评论

学校读者我要写书评

暂无评论

A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

A Traffic Model Integrating Long Short-Term Memory Networks ...

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： K. Binjaku C. Pasquale S. Sacone E. K. Meçe Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on/off ramps, to enhance traffic state prediction accuracy. We develop both univariate and multivariate versions of the physics-informed multi-class LSTM model, enabling comprehensive exploration of traffic variables such as speed and flow for passenger and heavy vehicles. Experimental results demonstrate the superior predictive capabilities of our approach compared to traditional LSTM models, particularly in capturing the intricate relationship between speed and flow variables. Our findings underscore the significant benefits of incorporating physics-based principles into deep learning architectures for traffic prediction, facilitating more robust and reliable traffic state estimations for real-world applications.

关键词： Accuracy Urban planning Transportation Predictive models Reliability engineering Mathematical models Robustness State estimation Vehicle dynamics Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Diabetic Foot Ulcer Detection on Mobile Platforms through Thermal Imaging and Deep Learning

Diabetic Foot Ulcer Detection on Mobile Platforms through Th...

引用

2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Arnia, Fitri Saddami, Khairun Muharar, Rusdha Dwi Pratiwi, Dea Ananda Nurdin, Yudha Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia Universitas Syiah Kuala Telematics Research Center Indonesia FernUniversität in Hagen Faculty of Mathematics and Informatics Germany

ISBN: (纸本)9798350382822

Diabetes mellitus, causing substantial global mortality with millions affected in 2019, necessitates early detection methods to aid Diabetic Foot Ulcer (DFU) patients. We proposed a mobile application for early DFU identification using a Convolutional Neural Network (CNN) classifier. The application embedded a fine-tuned MobileNetV2 model specifically adapted for DFUs, trained on a plantar thermogram dataset through transfer learning. We evaluate the model's classification performance pre- and post-embedded into the application. Results reveal that the pre-embedded model achieved 90% accuracy, 100% specificity, and 80% sensitivity. After embedding, the model retained 85% accuracy, 80% specificity, and 90% sensitivity. Notably, a slight accuracy decrease post-embedded is likely attributed to the normalization of the input image. This study emphasizes that the interplay between model performance and mobile embedded might influence the model's performance. It underscores the meticulous consideration of mobile platform-specific processes crucial for accurate and effective early DFU detection. © 2023 IEEE.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Joint CFO and STO Estimation for Modified Walsh-Hadamard Code Division Multiplexing

Joint CFO and STO Estimation for Modified Walsh-Hadamard Cod...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Masaki Kitagawa Toshiharu Kojima Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

In helicopter satellite communications, the periodic blockage of the transmission channel arises due to rotor blades. Modified Walsh-Hadamard code division multiplexing (MWHCDM) overcomes the periodic blockage. In MWHCDM, estimating the symbol timing offset (STO) is necessary to perform demultiplexing. The carrier frequency offset (CFO) significantly degrades the accuracy of STO estimation. Thus, STO estimation requires estimating and removing the CFO, although CFO estimation is only possible without STO. This paper proposes a joint CFO and STO estimation method to resolve this dilemma, combining the CFO and STO estimation to complement each other. computer simulation shows that the proposed method accurately estimates both CFO and STO.

关键词： Code division multiplexing computer simulation Helicopters Estimation Symbols Rotors Frequency estimation Timing Frequency division multiplexing Satellite communications

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

引用

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

来源：评论

学校读者我要写书评

暂无评论

Twister2 Cross-platform resource scheduler for big data

Twister2 Cross-platform resource scheduler for big data

引用

作者： Uyar, Ahmet Gunduz, Gurhan Kamburugamuve, Supun Wickramasinghe, Pulasthi Widanage, Chathura Govindarajan, Kannan Perera, Niranda Abeykoon, Vibhatha Akkas, Selahattin Fox, Geoffrey School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Engineering Faculty of Engineering Mugla Sitki Kocman University Mugla Turkey

Twister2 is an open-source big data hosting environment designed to process both batch and streaming data at scale. Twister2 runs jobs in both high-performance computing (HPC) and big data clusters. It provides a cross-platform resource scheduler to run jobs in diverse environments. Twister2 is designed with a layered architecture to support various clusters and big data problems. In this paper, we present the cross-platform resource scheduler of Twister2. We identify required services and explain implementation details. We present job startup delays for single jobs and multiple concurrent jobs in Kubernetes and OpenMPI clusters. We compare job startup delays for Twister2 and Spark at a Kubernetes cluster. In addition, we compare the performance of terasort algorithm on Kubernetes and bare metal clusters at AWS cloud. © 2021 John Wiley & Sons, Ltd.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

引用

Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Dimitrios Danopoulos Georgios Zervakis Dimitrios Soudris School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Computer Engineering & Informatics University of Patras Patras Greece

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets.

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：