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共 386 条记录，以下是71-80 订阅

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distributed cracking system of encrypted currency based on blockchain 2

Distributed cracking system of encrypted currency based on b...

引用

2nd international conference on Cloud computing and Mechatronic Engineering, I3CME 2022

作者： Huang, Siyu Li, Zihe Liu, Shanshan Wang, Zhanglin Beijing Institude of Technology Beijing100081 China Southwest University Chongqing400715 China Institute of Disaster Prevention Sanhe065201 China Yangtze University Wuhan442300 China

ISBN: (纸本)9781510657502

With the development of computer and internet technology, cryptocurrency has begun to be widely understood and accepted by people, and it has become a hot spot in the field of technology and finance. Bitcoin is a typical cryptocurrency. Its official wallet does not have a password retrieval function. When the password is forgotten, the wallet private key cannot be decrypted, and the property in the wallet cannot be used. It is possible to crack the wallet file. The demand is getting bigger and bigger. Therefore, this article aims to study the distributed cracking system of encrypted currency based on blockchain. Based on the analysis of cryptographic technology, block chain structure classification brute force cracking and distributed system, a system cracking scheme based on distributed computing network is proposed. Then the distributed cracking system for encrypted currency is designed and implemented. Finally, the system design was tested for performance. The test results show that the system realizes the work of distributed cracking using the idle hardware resources of ordinary personal computers, and the cracking efficiency will increase with the increase of the number of terminals, achieving the goal of system design. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Blockchain

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Construction of user Portrait System Based on information warehouse under big data 2

Construction of user Portrait System Based on information wa...

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2022 2nd international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Fang, Yaxin Liu, Zhonglin Li, Changbao Pan, Shuang North China Institute of Computing Technology Big Data Analysis Department Beijing100083 China

ISBN: (纸本)9783800760268

With the continuous development of internet technology, the scale of data volume shows an explosive growth *** this case, the traditional user portrait system will have problems such as insufficient data storage and slow calculation. Based on this, this paper designs a user portrait technology based on information warehouse under big data. Through the data acquisition module, the distributed information collection is carried out to complete data acquisition, data cleaning and zoning. Through the data warehouse hierarchical operation, the data is accumulated and summarized, and complex tasks are resolved into multiple simple tasks. It is convenient to locate problems, improve the reusability of calculation results, and decouple original data and statistical data. Through user portrait management platform module, the labels are classified, customized[For the labels of statistical classes are distributed and aggregated through spark. For the labels of mining classes, K-means algorithm is used to build a multi-level user portrait model. The storage of portrait data and the screening of multi label combination are realized through the user clustering module. The experimental results show that the system is suitable for the construction of user portrait in the case of large amount of data[The advantages of the system are lower delay and stable accuracy. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Big data

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Real-Time Edge computing Services for internet of Things-based Cloud Networks

Real-Time Edge Computing Services for Internet of Things-bas...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and internet of Things (AIMLA), international conference on

作者： Manish Srivastava Sunil M P Arun Kumar Marandi Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The internet of Things (IoT) has enabled the development of the real-time edge computing institute for distributed cloud networks. This technology makes it possible for devices connected to the cloud to process elevens and respond to problems or requests in a timely manner. Edge computing provides a distributed, low-latency platform for elevens processing at the edge of the network, closer to the point where elevens bestial collected. Spill a result, this reduces the cost associated with cloud-based services while also minimizing the latency, ensuring fast and reliable responsiveness. Furthermore, edge computing provides a verging for performing complex analytics and machine learning tasks at the edge of the cloud network, reducing the burden on cloud-based institute. This allows distributed cloud networks to scale easily, with reliability and scalability maintained through edge computing.

关键词： Wide area networks Cloud computing Scalability Machine learning Medical services Real-time systems Reliability

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M-Trust based Security Protocol (MTSP) for Confidential Data Forwarding in IoT 2

M-Trust based Security Protocol (MTSP) for Confidential Data...

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2nd international conference on Signal and Information Processing, IConSIP 2022

作者： Renjith, P.N. Ramesh, K. VIT Chennai Dept. of Computer Science Chennai India

ISBN: (纸本)9781728168852

The internet of Things (IoT) is a promising technology helping humankind by extending its support to varieties of applications like health care monitoring, forest fire, traffic management, and many more. IoT utilizes the WSN network to sense geographical information and forward it to the destination for decision making. WSN works with the cooperation of distributed sensor nodes that are supplied with limited computing and battery resources. This limitation makes the nodes prone to malicious attacks resulting in loss of critical information and failure of the entire network. In this paper, a novel M-Trust based Security Protocol (MTSP) is designed for secure and confidential data transmission in the network. The simulation results prove improved Security and Secure Data transmission when compared to existing protocols. © 2022 IEEE.

关键词： internet of things

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Big Data distributed Smart Storage Management System Used for Financial technology Enterprises

Big Data Distributed Smart Storage Management System Used fo...

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Data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Shilong Zhu Zhengzhou University Zhengzhou China

In order to overcome the difficulties encountered by small and medium-sized financial technology enterprises in transformation, this paper proposes a novel big data distributed management system for small and medium-sized financial technology enterprises. The system fully combines the development and transformation difficulties of small and medium-sized science and technology enterprises under the background of the current information age, and pertinently combines a variety of advanced science and technology, such as big data technology, cloud computing technology, internet technology and so on. The results show that the system can indeed comprehensively improve the data management efficiency of small and medium-sized financial technology enterprises, provide better business services for customer groups, and greatly promote the rapid transformation and upgrading of small and medium-sized financial technology.

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Resource Allocation Algorithm Based on Modeling of Ubiquitous Network Node Capability 2

Resource Allocation Algorithm Based on Modeling of Ubiquitou...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Song, Dai Chen, Xushan Rui, Lanlan Li, Wenjing Du, Zongpeng Li, Zhiqiang Lu, Lu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Infrastructure Network Technology Research Department China Mobile Research Institute Beijing China

With the continuous development and progress of the internet, computing power is gradually moving from the center to the edge. In order to perceive, measure, schedule and manage the distributed computing resources, it is necessary to model and evaluate the node capability. Based on the development trend of network computing convergence and the demand of network computing service scheduling, this paper studies the unified measurement model and resource allocation algorithm for the integration of network computing and heterogeneous resources. The node resources are divided into four dimensions: computing, communication, memory and storage, and on this basis, the comprehensive ability evaluation index is proposed. Through simulation analysis, the effectiveness of the resource allocation algorithm based on node capability modeling is verified. © Published under licence by IOP Publishing Ltd.

关键词： computing power

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A survey on fog computing applications in internet of vehicles 2

A survey on fog computing applications in internet of vehicl...

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2nd international conference on computing and Data Science, CDS 2021

作者： Zhao, Wenyuan School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665404280

As the technology of network, wireless transmission and big data computing develops rapidly, the society has entered the Information Era. internet of Vehicles, the important part of the internet of Things, is the inevitable trend of urban traffic in the future. Fog computing emerges as a new technology of distributed computing, which is suitable for applications in the internet of Vehicles. internet of Vehicles based on fog computing can solve the problems of traffic congestion, transportation efficiency and security. In this paper, we review the recent work in this topic and analyze the architecture and scenario of fog computing in the internet of Vehicles. Three aspects of applications are proposed in this paper: VANET, big data processing and security. At the same time, we discuss the technical details and challenges in these applications. Fog computing has excellent processing power in real-time big data and a high mobility environment, which reduces the latency of data processing, makes the deployment between vehicles and roadside units more reasonable and enhances the security of information interchanged in the internet of Vehicles. Fog computing devices will become an indispensable part of the future construction of internet of Vehicles. © 2021 IEEE.

关键词： Fog computing

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A collaborative approach to support interoperability and awareness of internet of Everything (IoE) enablers

A collaborative approach to support interoperability and awa...

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2nd IEEE international conference on Human-Machine Systems (ICHMS)

作者： da Costa, Viviane Cunha Farias Oliveira, Luiz Felipe de Souza, Jano Univ Fed Rio de Janeiro Syst Engn & Comp Sci Program COPPE Rio De Janeiro Brazil Fed Inst Rio de Janeiro Niteroi Campus Niteroi Brazil

ISBN: (纸本)9781665401708

internet of Everything (IoE) is a promising paradigm that integrates the internet of Things (IoT), Industrial internet, internet of People, and many internet-based paradigms to transform the industry, society, and people lives. It provides seamless integration of intelligent devices -- with sensing, identification, processing, communication, networking capabilities, Big Data (machine learning, analytics, and distributed computing), and human sensors (collaboration, intelligent cognition, and social networks). IoE brings excellent opportunities to improve changes in society with collaborative intelligent systems. However, these new technologies also bring significant challenges and do not address major existing issues, including interoperability, reliability, and intelligence management. Awareness of these issues is required before IoE applications can be widely deployed. This work introduces an observatory for cataloging IoE applications. Registering these applications will support researchers, developers, and users to exchange more information, design improved IoE applications, facilitating the composition of different enablers (human and non-humans). The main contributions of this work include the proposal of a technology platform, the IoE Database (IoEDB), that enables the distributed and collaborative cataloging of IoE initiatives and provides the evolution of a 'live' IoE knowledge-based taxonomy to support interoperability and awareness of IoE enablers.

关键词： IoE internet of Everything taxonomy applications

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Towards Feature Selection for Detecting LDDoS in SD-IoT of Smart Grids: A Multi-correlation Information EA-based Method 2

Towards Feature Selection for Detecting LDDoS in SD-IoT of S...

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2nd international Symposium on computing and Artificial Intelligence, ISCAI 2023

作者： Huan, Xuanyu Zhao, Jianpeng Chen, Guolin Li, Xianchao Cui, Yunhe Yang, Hongzhen Guizhou Xiangming Technology Co. Ltd. Guiyang China State Key Laboratory of Big Data College of Computer Science and Technology Guizhou University Guiyang China State Grid Zhejiang Electric Power Co. ltd. Information and Telecommunication Branch Hangzhou China

ISBN: (纸本)9798400708954

Abstract. internet of Things (IoT) plays an important role in smart grids. Software-Defined internet of Things (SD-IoT) employs the Software-Defined Networking (SDN) as the network architecture of IoT in smart grids. Hence, in smart grids, SD-IoT can achieve flexible network management using the network programmability. Nevertheless, IoT devices in smart grids face a number of security issues. The Low-rate distributed Denial of Service (DDoS) is one of such a security issue. Detecting LDDoS is one of the most important steps in defending LDDoS. When detecting LDDoS in SD-IoT, a large number of network features are useless or even have side effects on the detection of LDDoS. Therefore, how to choose the feature subset from all features to improve the LDDoS detection performance is a key problem. In order to solve these problems, in this work, we propose ENTER, a multi-correlation information EA-based feature selection method, used to detect LDDoS for SD-IoT in smart grids. ENTER calculates the multi-correlation information of different individuals and adjusts the partial variation of the population through the multi-correlation information. ENTER is consisted of a novel multi-correlation information-based gene mutation strategy and a novel local optimum bounce strategy. The evaluation results indicate that ENTER can achieve high feature compression ratio while improve the detection performance of the LDDoS detector, in terms of detection precision, accuracy, recall, f-score and detection time. © 2023 ACM.

关键词： internet of things

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Unmasking Emotions: Deep Neural Networks for Image-Based Emotion Recognition

Unmasking Emotions: Deep Neural Networks for Image-Based Emo...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Mohammad Aman Sohel Raghupatruni Sai Madhukar Sai Muralidhar Batchu Jyotsna C. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

Recognizing human emotions is a multifaced field that uses the technology, especially deep learning and Convolutional neural networks, to recognize and understand emotions such as happiness, sadness, anger, disgust and fear. Convolutional Neural Networks are designed for visual data explorations and they are effectively capture facial expressions for emotion recognition. The process involves the collection of labeled data, and also preprocesses it, creating an appropriate deep neural network, training the model with constant updates, and assessing its performance using metrics on a testing dataset. Proposed work tackles emotion detection in irregularly distributed datasets of preprocessed 48x48 grayscale emotion images. Significant performance gains are achieved through targeted data augmentation for marginalized classes, giving valuable insights for robust emotion recognition models. Optimizing hyperparameters enhances the model, enabling its deployment in real-world scenarios by ensuring integration, data handling, and real-time functionality. The proposed model primarily focuses on offline image and video capturing for emotion detection, and highlighting on video-emotion recognition.

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