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检索条件"任意字段=2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022"

共 962 条记录，以下是261-270 订阅

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Deep Learning Based Intrusion Detection for Internet of Things and Edge Devices

Deep Learning Based Intrusion Detection for Internet of Thin...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mohamad Moawad Vong Wan Tze Patrick Then Hang Hui Jason Yong Hsien Ming Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Malaysia

The rapid expansion of Internet of Things (IoT) and Edge devices has introduced a variety of security challenges, particularly within resource-constrained computing environments. This paper aims to address these challenges by proposing a resource-efficient anomaly-based intrusion detection system tailored for IoT and Edge Devices, leveraging the capabilities of Binary Neural Networks (BNNs). Anomaly-based systems have demonstrated their efficacy in identifying potential cyber-attacks, but their conventional development methods often demand substantial storage and computational resources. Therefore, this research investigates the viability of BNNs for anomaly detection, offering a compelling alternative to full-precision approaches. Three distinct models are developed for this study using TensorFlow and Keras: Full-binary, Binary-weighted, and Full-precision. The models are trained and evaluated on the NSL-KDD dataset and achieved accuracy rates of 82%,87%, and 97% for these respective models. While binary networks experience a minor performance reduction due to aggressive quantization, they maintain notable levels of accuracy and precision. This observation takes into consideration the anticipated reduction in resource requirements, affirming the feasibility of BNNs' utilization for efficient anomaly detection.

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Choosing Suitable Object Detection Method to Examine Pigment Powders of Similar Shape

Choosing Suitable Object Detection Method to Examine Pigment...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Naoya Wakabayashi Hiromitsu Shimakawa Information Science and Engineering Graduate School of Ritsumeikan Uni. Kusatsu Shiga Japan

In recent years, a variety of paints have been developed in response to the diversification of color design. Due to the powder manufacturing process, different sizes of powders may randomly attach to each powder. Since the objects attached to larger ones affect the quality of the product, they are specifically referred to as asteroid powder. Asteroid powders are similar in shape have many problems and it difficult to detect. This paper investigates whether an object detection method can automatically detect asteroid powders. It also tries to choose a suitable detection method. The paper compared two typical object detection methods YOLO and SSD. As a result, it has turned out YOLO works far better than SSD.

关键词： Training Powders Manufacturing processes Shape Image color analysis Computational modeling Object detection

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A Secure Authentication and Key Establishment Scheme using PUF in Vehicular Ad-Hoc Network

A Secure Authentication and Key Establishment Scheme using P...

引用

computer science and data engineering (csde), ieee asia-pacific conference on

作者： Myeonghyun Kim Kilhoum Park School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea

In VANET, a vehicle can exchange information from other vehicles and facilities around the road. However, the problems such as transmitting data over insecure wireless channels and storing information in insecure memory can pose serious security threats. To solve this problem, research on authentication and key establishment using PUF is being actively conducted. In 2021, Umar et al. proposed an ID-based authentication and key establishment scheme using PUF in VANETs. However, their scheme has some security vulnerabilities and does not provide secure mutual authentication. In this paper, we analyze the security vulnerabilities of Umar et al. 's scheme and propose a secure authentication and key establishment scheme to solve the problems.

关键词： Wireless communication computer science Roads Authentication Vehicular ad hoc networks data engineering Security

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Governance token distributions of Layer-One.X blockchain

Governance token distributions of Layer-One.X blockchain

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Kevin Coutinho Pornpit Wongthongtham Alan McCarthy Zahid Hasan Alphabright Digital Australia University of Western Australia University of Notre Dame Australia

Layer-One.X (L1X) blockchain provides the infrastructure layer on which decentralized applications can be created. L1X's governance token distribution is important and requires a proportional balance between token distribution, nurturing user activity and engagement, and financial incentives. In this paper, we present research in progress of L1X tokenomics describing key concepts and implementations including token velocity and value, incentive scheme, and broad distribution. Particularly the economic design of the native token of the L1X blockchain is presented.

关键词： Economics computer science Friction Computational modeling Decentralized applications data engineering Consensus protocol

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A Multi-Agent Deep Reinforcement Learning Model of Internet-Related Markets Evolution 2

A Multi-Agent Deep Reinforcement Learning Model of Internet-...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Liu, Wentao Han, Kun Department of Computer Science Chaoyang Beijing China Beihang University Department of Computer Science Beijing China

ISBN: (纸本)9781665400343

Internet economic markets have upgraded the traditional ways of product, service, consumption, and processing transactions and prompted the reforming of traditional multisided markets. With this background, our paper presents a multi-agent deep reinforce learning method with group hybrid abstraction called GHA-DQN to build Internet marketplace model and observe the evolution process including initiation and competition periods from the areas of e-transport, e-commerce, e-catering, and other e-markets. The experiment results provide references to support networking, financing, and competition equilibrium in this competitive e-economic world. © 2022 ieee.

关键词： Reinforcement learning

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Frame Attention Mechanism and Transfer Learning Network for Classification on Motion Video

Frame Attention Mechanism and Transfer Learning Network for ...

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2022 ieee asia-pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Liu, Kechuan Zhou, Gaohao Cheng, Xin Xi'an Technological University School of Sciences Xi'an China Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University School of Mechatronic Engineering Xi'an China

ISBN: (纸本)9781665409025

Video content classification has a wide range of application scenarios. In situations such as public place security and behavior prediction, the type of behavior of a person is inferred from the continuous images in the video. Video data adds the concept of time compared to image data, which inevitably increases the overall computational effort. A typical research tool uses 3D convolution for feature map extraction in the time domain, but such methods exhibit significant performance loss due to frame down sampling. Inspired by the human judgment of video content, this study computes features on a single frame by transfer learning and then encodes the position of the resulting set of features. An attention mechanism is used to determine the keyframe locations. Finally, a multilayer perceptron is combined to achieve video content classification. The results of the study show that with the chosen dataset. Our model outperforms the 3D convolutional model in discriminative accuracy, confusion matrix, and down sampling conditions. © 2022 ieee.

关键词： Video recording

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Predicting Forest Crimes through Machine Learning

Predicting Forest Crimes through Machine Learning

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Cheng Chen Richard O. Sinnott School of Computing and Information Systems The University of Melbourne Melbourne Australia

Crime is a major factor that adds unnecessary financial stress to government. If agencies are able to intelligently and strategically use their resources, e.g., for crime prevention, then many benefits would arise. A variety of statistical methodologies had been explored to predict crime. However, few studies have explored and compared machine learning solutions. In this paper we utilize a range of machine learning and natural language process (NLP) approaches to build models that are able to predict offence categories, as well as their time and location. We focus specifically on environmental offences based on incident report data from the Department of the Environment, Land, Water and Planning (DELWP) in Victoria.

关键词： computer science Statistical analysis Natural languages Government Machine learning Forestry Predictive models

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An Image Processing and Utilization System Using Deep Learning for Tourism Promotion

An Image Processing and Utilization System Using Deep Learni...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Yunhao Tu Masato Kinugasa Mayu Urata Mamoru Endo Takami Yasuda Dept. of Computer Science Chubu University Kasugai Japan Graduate School of Informatics Nagoya University Nagoya Japan

In recent years, digitalization has made significant developments in all areas of modern society. data analysis and the application of information technology are becoming increasingly important in the tourism industry, which is an important pillar industry in Japan. Therefore, we propose a system with four main functions to support local governments in processing and using image data and deep learning to promote tourism. Function 1: The system classifies images by detecting faces and selects images without faces and personal information that can be used for local tourism promotion. Function 2: The system protects personal information by transforming image styles or adding mosaics, and the processed data can be used for image publishing and display. Function 3: The system classifies images according to season and other factors to organize tourism promotion materials. Function 4: The system expands the application by adding captions to the images, such as using the original image and captions to promote local scenery and festivals. These functions can help users to better utilize the image data for tourism promotion in local governments. Our goal is to reduce the workload of local governments and improve the efficiency of image data utilization. We also use image data to promote urban promotion and achieve regional revitalization. We have confirmed the usefulness of the proposed system for data processing and tourism promotion through cooperation with local govemments.

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Weighted Semi-supervised MRI Segmentation with Paired Consistency and Entropy Minimization

Weighted Semi-supervised MRI Segmentation with Paired Consis...

引用

2022 ieee asia-pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Ma, Ao Liu, Jieyan Zhou, Yimin Lu, Ke University of Electronic Science and Technology of China Department of Computer Scince and Engineering Chengdu China Chengdu University of Information Technology School of Cybersecurity Chengdu China

ISBN: (纸本)9781665409025

U-net is very popular in medical image segmentation. However, the labels of training sets are always lacking for supervised learning. Thus, semi-supervised learning which utilizes unlabeled data shows advantages when dealing with such label-scarce situations. In this paper, we utilize U-net to tackle the challenge of segmenting MR images in semi-supervised setting. Specifically, we introduce paired consistency (PC) as data augmentation method. Also, we propose an entropy minimization (EM) term which is flexible and easy for calculation to boost the semi-supervised learning. Specifically, experiments show that our semi-supervised framework with PC and EM outperforms supervised U-net by %0.3 and %0.5 on OASIS and Hammers datasets, respectively. Also, entropy minimization accelerates the convergence of training, namely, reducing nearly %50 of training time, which is beneficial to real-world application. © 2022 ieee.

关键词： Entropy

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Phase Equilibrium for Clathrate Hydrate Formed in Deuterium Oxide + CO2+ Cyclopentane System: Application to Low-Concentration Tritium Separation

Phase Equilibrium for Clathrate Hydrate Formed in Deuterium ...

引用

computer science and data engineering (csde), ieee asia-pacific conference on

作者： Meku Maruyama Takuma Misawa Ryo Ohmura Department of Mechanical Engineering Keio University Yokohama Japan

This paper reports pressure-temperature conditions for liquid + hydrate + vapor phase equilibrium of hydrate formed with deuterium oxide (D 2 O), CO 2 and cyclopentane. The aim of this study is to obtain the equilibrium properties of the formed hydrate for the application to hydrate-based tritium separation process. Measurements with batch isochoric method were performed. The pressure and temperature ranges of measurement were from 1.63 MPa to 2.46 MPa and from 290.6 K to 293.4 K. The obtained results indicate that D 2 O + CO 2 + cyclopentane hydrate is formed in milder conditions than H 2 O + CO 2 + cyclopentane hydrate by 2.1 K at the given pressure conditions.

关键词： Temperature measurement Water Deuterium computer science Temperature distribution data engineering Pressure measurement

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