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Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof 2

Multi-chain Storage Scheme Based on Hash Calendar and Recurs...

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2nd international conference on computer Science and Blockchain, CCSB 2022

作者： Wang, Chao Yao, Zhongyuan Zhang, Yong Guo, Shangkun Zhao, Lindong Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution;and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain. © 2022 IEEE.

关键词： Blockchain

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Novel Representation of Robotic Grasp Detection based on Residual Hourglass Architecture

Novel Representation of Robotic Grasp Detection based on Res...

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2022 IEEE international conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Gao, Xiyuan Tang, Wei Xie, Pengwei Wang, Guijin Tsinghua University Tsinghua Shenzhen International Graduate School Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Grasping objects is one of the most important abilities in robot system. In this paper, we propose a novel grasp representation to match the multiple grasp rectangles with different orientations in the image and predict the pixel-wise quality map to locate the corresponding grasp centers. Based on this representation, we design a residual hourglass architecture to detect multiple robust grasp poses with the fusion of shallow and deep features. Our proposed network fully leverages the features and achieves state-of-the-art accuracy of 97.8% on Cornell dataset and 95.8% on Jacquard dataset with a real-time speed, which well demonstrates the effectiveness of our proposed method. © 2022 IEEE.

关键词： Robotics

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A Deep Learning-Based Approach to Image Captioning in Bengali

A Deep Learning-Based Approach to Image Captioning in Bengal...

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2022 IEEE international conference on Current Development in Engineering and technology, CCET 2022

作者： Akash, Abdul Hady Mahi, Fabiha Faiz Hasnan, Md. Arafat Biswas, Arindam Kishor Rahat-Bin-Rafique, A.A.M. Tahmiduzzaman, K.B.M. Tarofdar, Sabuj Kumar University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665454155

One of the most popular research topics in the field of computer vision is automatic image captioning. Image captioning is essentially the process of generating textual description from an image, with the description generated based on the objects and actions in the image. A significant amount of effort has gone into creating image captions in English. However, there have only been a few previous works that have been discovered to generate Bangla image captioning for images, and Papers proposed solution has failed to provide satisfactory results. As a result, in this study, Author proposed a deep learning-based model for Bangla image captioning. This Research used a deep learning-based feature extractor named ResNet50 As a proposed model to extract feature vectors from images and an LSTM network to generate text captions using these features. The Research has deployed two datasets the BanglaLekhaImageCaptions dataset, which contains 9000 images, and the Flickr dataset, which contains 8000 images. After training and testing the model with these datasets, It discovered that the model outperforms all previous Bangla image captioning models. © 2022 IEEE.

关键词： computer vision

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Research on Cross-site Scripting Vulnerability of XSS Based on international Student Website 5

Research on Cross-site Scripting Vulnerability of XSS Based ...

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5th international conference on computer network, Electronic and Automation, ICCNEA 2022

作者： Wang, Tianma Zhao, Dongmei Qi, Jianjun International Education Institute North China Electric Power University Beijing China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

The international student website is the window of the school's external publicity. Through the website, international students can understand the school in a wider range and in all aspects. The website is often attacked by network hackers. How to ensure the security of the website? Cross site scripting attack (XSS) is the most common web application security vulnerability. Through the analysis of cross site scripting attack (XSS), this paper puts forward the preventive methods for input verification, output coding, clearly specifying the coding mode of output, paying attention to the limitations of blacklist verification mode, being alert to standardization errors, and gives examples of not inserting untrusted data in the allowed position, and HTML coding these data when inserting untrusted data between HTML tags, Before inserting untrusted data into common HTML attributes, encode HTML attributes;before inserting untrusted data into HTML JavaScript, encode JavaScript;before inserting untrusted data into HTML style attribute values, encode CSS;before inserting untrusted data into HTML URL attributes, encode URL;when using rich text, use XSS rule engine to encode and filter and other corresponding defense rules. © 2022 IEEE.

关键词： Websites

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The Music Generation Road from Statistical Method to Deep Learning 8

The Music Generation Road from Statistical Method to Deep Le...

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Xu, Jiayun Qu, Weiting Li, Dixin Zhang, Changjiang BNU-HKBU United International College Faculty of Science and Technology China

ISBN: (纸本)9781665450515

Automatic music generation is regarded as one of the most concerning research areas. However, the current music generation AI model lacks mainstream algorithms and norms, including model evaluation standards and objective music quality measurements. In this paper, we conducted experiments on traditional statistical models, machine learning models and deep learning models to generate music, respectively involving Markov Chain, SVM, Random Forest, LSTM, GAN models and their variants. In addition, we also proposed three new quantitative methods to measure the quality of computer-generated music using technical metrics: the Key Confidence Test, the Random Walk Test and the Word2Vec Similarity Test. These methods could be used to measure the quality of generated music and avoid the subjectivity of manual evaluation, thus providing reference for future music evaluation tasks. © 2022 IEEE.

关键词： Music

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A Versatility-Performance Balanced Hardware Architecture for Scene Text Detection 19

A Versatility-Performance Balanced Hardware Architecture for...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xin, Yao Tang, Guoming Chen, Donglong Zhang, Rumin Liang, Teng Cheung, Ray C. C. Koç, Çetin Kaya Peng Cheng Laboratory Shenzhen China National University of Defense Technology Changsha China BNU-HKBU United International College Zhuhai China Southern University of Science and Technology Shenzhen China City University of Hong Kong Hong Kong Uc Santa Barbara United States Nuaa China Iǧdir University Turkey

ISBN: (纸本)9798350346558

Detecting and extracting textual information from natural scene images needs Scene Text Detection (STD) algorithms. Fully Convolutional Neural networks (FCNs) are usually utilized as the backbone model to extract features in these instance segmentation based STD algorithms. FCNs naturally come with high computational complexity. Furthermore, to keep up with the growing variety of models, flexible architectures are needed. In order to accelerate various STD algorithms efficiently, a versatility-performance balanced hardware architecture is proposed, together with a simple but efficient way of configuration. This architecture is able to compute different FCN models without hardware redesign. The optimization is focused on hardware with finely designed computing modules, while the versatility of different network reconfigurations is achieved by microcodes instead of a strenuously designed compiler. Multiple parallel techniques at different levels and several complexity-reduction methods are explored to speed up the FCN computation. Results from implementation show that, given the same tasks, the proposed system achieves a better throughput compared with the studied GPU. Particularly, our system reduces the comprehensive Operation Expense (OpEx) at GPU by 46%, while the power efficiency is enhanced by 32%. This work has been deployed in commercial applications and provided stable consumer text detection services. © 2022 IEEE.

关键词： Convolutional neural networks

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Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 Digital Revolution 20

Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 ...

引用

20th OITS international conference on Information technology, OCIT 2022

作者： Mallick, Soubhagya Ranjan Sharma, Suraj Kumar Tripathy, Pradyumna Kumar Ray, Niranjan International Institute of Information Technology Computer Science and Engineering Bhubaneswar India Silicon Institute of Technology Computer Science and Engineering Bhubaneswar India Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9781665493482

Healthcare is the fastest growing sector that implements the latest technologies for providing healthcare services. Most of the IoMT healthcare systems are using cloud-based systems for providing smart healthcare services. Fog Computing is introduced in loT-based healthcare environments to reduce the additional computing and storage required by cloud servers. Healthcare 4.0 focuses on a smart environment that guarantees security, privacy, reliability, integrity, easy access, trust, transparency, faster processing, etc. Blockchain-integrated IoT-fog systems are used for providing security, and privacy and avoiding single-point failure in healthcare systems. In this paper, we have proposed a Blockchain-Fog-IoMT framework for faster processing of healthcare data and reducing the storage and processing overhead on the cloud server. We have also minimized the latency, resource utilization and response time with the implementation of fog computing at the nearest network of the user devices. Ethereum Blockchain and smart contract has been integrated with our system to address the security, privacy, scalability, transparency, reliability, and interoperability challenges. Block validation and transaction validation are done with the use of proof-of-work. Moreover, this model helps to create a decentralized platform for providing healthcare services, facilitates remote-patient tracking and monitoring, and is cost-effective. © 2022 IEEE.

关键词： Smart contract

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Rainfall Prediction based on Time-series Data and Temporal Convolutional network for Flood Forecasting in Malaysia

Rainfall Prediction based on Time-series Data and Temporal C...

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2024 IEEE international conference on Computing, ICOCO 2024

作者： Abdullah, Mohd Shahar Selamat, Ali Do, Nguyet Quang Abu, Mohd Azlan Lim, Kok Cheng Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia College of Computing and Informatics Universiti Tenaga Nasional Kajang Malaysia Skoda Auto University Mlada Boleslav Czech Republic

ISBN: (纸本)9798331530303

Extreme rainfall can lead to flood disasters that bring detrimental impacts on various aspects of human lives. Rainfall forecasting is a sophisticated time-series problem due to its nonlinear nature. Recently, deep learning algorithms, such as Long Short-Term Memory (LSTM), have appeared as effective approaches to handling time-series data. However, LSTM has limitations in dealing with long-distance dependencies. Therefore, this study aims to present a deep learning model based on Temporal Convolutional network (TCN) to address the problem of LSTM in forecasting rainfall amounts for flood prediction. The proposed model was trained and tested using observed rainfall data from multiple states in Malaysia. The performance of the proposed solution was benchmarked with various baseline architectures, including Recurrent Neural network (RNN), Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Several evaluation metrics were used to assess the performance of the proposed model, such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). Results from the comparative experiments indicated that TCN outperformed other deep learning models, achieving the lowest MSE, RMSE, and MAE scores of 0.0050, 0.0706, and 0.1467, respectively. The model also obtained the highest R2 value of 0.8592, indicating that TCN can effectively handle time-series data and provide accurate prediction of rainfall amounts. ©2024 IEEE.

关键词： Mean square error

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A Review of 5G technology: Architecture and Challenges 8

A Review of 5G Technology: Architecture and Challenges

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8th IEEE international conference on Computing, Engineering and Design, ICCED 2022

作者： Attarbashi, Zainab S. Altaleb, Abdulazeez E. Mohd Aman, Azana Hafizah Hassan Abdalla Hashim, Aisha Eker, Sebahattin International Islamic University Malaysia Information Communication Technology Kuala Lumpur Malaysia Informatics Institute Istanbul Technical University Communication Systems Department Istanbul Turkey University Kebangsaan Malaysia Faculty of Information Science Technology Bangi Malaysia International Islamic University Malaysia Computer Information Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665453899

5G networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed. © 2022 IEEE.

关键词： 5G mobile communication systems

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NiNSRAPM: An Ensemble Learning Based Non-intrusive network Security Risk Assessment Prediction Model 7

NiNSRAPM: An Ensemble Learning Based Non-intrusive Network S...

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7th IEEE international conference on Data Science in Cyberspace, DSC 2022

作者： Yang, Jun-Zheng Liu, Feng Zhao, Yuan-Jie Liang, Lu-Lu Qi, Jia-Yin Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China East China Normal University School of Computer Science and Technology Shanghai China Beijing Yuanbao Technology Co. LTD. Technical Department Beijing China Institute for Artificial Intelligence and Change Management Shanghai University of International Business and Economics Shanghai China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Cybersecurity insurance is one of the important means of cybersecurity risk management and the development of cyber insurance is inseparable from the support of cyber risk assessment technology. Cyber risk assessment can not only help governments and organizations to better protect themselves from related risks, but also serve as a basis for cybersecurity insurance underwriting, pricing, and formulating policy content. Aiming at the problem that cybersecurity insurance companies cannot conduct cybersecurity risk assessments on policyholders before the policy is signed without the authorization of the policyholder or in legal, combining with the need that cybersecurity insurance companies want to obtain network security vulnerability risk profiles of policyholders conveniently, quickly and at low cost before the policy signing, this study proposed a non-intrusive network security vulnerability risk assessment method based on ensemble machine learning. Our model uses only open source intelligence and publicly available network information data to rate cyber vulnerability risk of an organization, achieving an accuracy of 70.6% compared to a rating based on comprehensive information by cybersecurity experts. © 2022 IEEE.

关键词： Insurance

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