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Distributed Timestamp Mechanism Based on Verifiable Delay Functions

computer Systems Science & engineering 2023年第2期44卷 1633-1646页

作者： Qiang Wu Zhaoyang Han Ghulam Mohiuddin Yongjun Ren Engineering Research Center of Digital Forensics of Ministry of Education School of Computer ScienceNanjing University of Information Science and TechnologyNanjing210044China Nanjing University of Aeronautics and Astronautics Nanjing210008China Department of Cyber Security at VaporVM Abu Dhabi999041United Arab Emirates

In the data communication system,the real-time information interaction of communication device increases the risk of privacy sensitive data being tam-pered ***,maintaining data security is one of the most important issues in network data *** the timestamp is the most impor-tant way to authenticate data in information interaction,it is very necessary to pro-vide timestamp service in the data communication ***,the existing centralized timestamp mechanism is difﬁcult to provide credible timestamp ser-vice,and users can conspire with timestamping servers to forge ***,this paper designs a distributed timestamp mechanism based on contin-uous veriﬁable delay *** utilizes multiple independent timestamp servers to provide timestamp services in a distributed model and appends the timestamp to the data once the data is ***,it can prove that the data already exists at a certain time and ensure the accuracy of the ***,a digital blind signature based on elliptic curve cryptography is utilized to solve the problem of timestamp forgery in timestamp ***,the security ana-lysis of the scheme ensures the data security of data communication system and the concurrency rate of *** experimental results also show that the scheme greatly improves the efﬁciency of digital signatures.

关键词： Timestamp veriﬁable delay functions blockchain

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LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D 38

LCGen: Mining in Low-Certainty Generation for View-consisten...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Tao, Zeng Yang, Tong Lin, Junxiong Mai, Xinji Wang, Haoran Wang, Beining Zhou, Enyu Wang, Yan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect. Project page is [here]. © 2024 Neural information processing systems foundation. All rights reserved.

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Variational Bayesian Kalman filter using natural gradient

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Chinese Journal of Aeronautics 2022年第5期35卷 1-10页

作者： Yumei HU Xuezhi WANG Quan PAN Zhentao HU Bill MORAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China The Key Laboratory of Information Fusion Technology Ministry of EducationXi’an 710072China Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVIC 3010Australia School of Engineering RMIT UniversityMelbourneVIC 3000Australia School of Computer and Information Engineering Henan UniversityKaifeng 475001China

We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman *** natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of *** a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error *** results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.

关键词： Kullback-Leibler divergence Natural gradient Nonlinear Kalman filter Target tracking Variational Bayesian optimization

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Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

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IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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Sentiment Analysis for Curriculum of Independent Learning Based on Naïve Bayes with Laplace Estimator 2

Sentiment Analysis for Curriculum of Independent Learning Ba...

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2nd International Conference on information technology Research and Innovation, ICITRI 2023

作者： Ariefah, Amelia Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia State University of Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798350324945

Curriculum changes have a significant impact on the field of education in Indonesia, especially in universities. This is because the curriculum is used as a means to achieve the goals of educational success as well as guidelines in the implementation of teaching at every level of education. This study aims to determine public sentiment regarding the implementation curriculum of independent learning (MBKM) through Twitter based on Naïve Bayes with Laplace Estimator. Tweet search results use the keywords 'kurikulum mbkm' and 'mbkm'. The data used in this study amounted to 2500 tweet data which will be divided into 2000 data as training data with details of 1000 positive tweets and 1000 negative tweets and 500 data as testing data. The results showed the performance of Naïve Bayes using Laplace Estimator which resulted in an accuracy value of 80.20%, precision value of 82.00%, and recall value of 79.00%. Meanwhile, the performance of Naïve Bayes without Laplace Estimator resulted in an accuracy value of 77.00%, precision value of 80.00%, and recall value of 74.00%. Naive Bayes with Laplace Estimator reached the highest value in all metrics. © 2023 IEEE.

关键词： Curricula

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Leveraging Machine Learning in Real-Time Automation for Predictive Maintenance in Industry 4.0 15

Leveraging Machine Learning in Real-Time Automation for Pred...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhabliya, Dharmesh Anandhasilambarasan, D. Singh, Amanveer Waiker, Vinod Paonipagar, Pratik Thomas, Shaji Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Datta Meghe Institute of Management Studies Nagpur India School of Mangement - PG Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

The transformation of modern industries by innovative technologies drives the need for Industry 4.0, where a connected machinery system designed to create smart factories will be established. Due to this shift, demand for realtime automation and predictive maintenance is increasing to maintain operations efficiently without any breakdown. Predictive maintenance through sensors' real-time data analysis and historical records for correct failure detection has shown promising results with machine learning. this grip answers the query, including machine learning in realtime automation (Industry 4.0) and predictive maintenance to understand various relevant terminologies. Sensor data is constantly monitored, and intelligent algorithms based on machine learning can even predict where machines could fail before one has to report downtimes. In addition to extending the machinery's life, this decreases downtime and maintenance costs. As we know, machine learning is used in real-time automation, giving us dynamic control of production processes, making it more profitable and less wasteful. Machine learning can deliver value across the supply chain and enhance factory operations by improving past data served to a machine or production line. The introduction of machine learning into Industry 4.0 allows companies to take a step toward preventative maintenance for their operations, which reduces costs and increases productivity countless times over. One of the benefits of machine learning for Industry 4.0 is real-time readiness, making it a great tool to help us make our factories truly smart and optimize them continuously with proper IT maintenance from specialists such as Noble1 Technologies. © 2024 IEEE.

关键词： Predictive maintenance

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QAIC: Quality-assured image crowdsourcing via blockchain and deep learning 26

QAIC: Quality-assured image crowdsourcing via blockchain and...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Baowei Yuan, Yi Li, Bin Dai, Changyu Wu, Yufeng Zheng, Weiqian Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Ministry of Education Engineering Research Center of Digital Forensics Nanjing China Nanjing China

ISBN: (纸本)9798350331684

Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy. © 2023 IEEE.

关键词： Blockchain

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Enhancing information Freshness via Knowledge Graph-Aided Semantic Communication 11

Enhancing Information Freshness via Knowledge Graph-Aided Se...

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2023 IEEE 11th International Conference on information, Communication and Networks, ICICN 2023

作者： Chen, Jie Yang, Shuai Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Hong Kong

ISBN: (纸本)9798350314014

This paper investigates semantic communication to enhance the information freshness of status update systems, using time division multiple access (TDMA) as an example. Age of information (AoI) is used to characterize information freshness, defined as the time elapsed since the generation of the last successfully received status update. Prior works on analyzing and optimizing AoI have focused on the conventional bit-oriented communication paradigm, aiming to efficiently and reliably deliver each bit of the update message. When a large number of users take turns transmitting their update messages over a wireless channel to a common access point (AP), conventional bit-oriented TDMA systems may result in a high average AoI because users have to wait a long time for the next update opportunity. To address this problem, we propose a knowledge graph-aided semantic communication for TDMA systems, referred to as KGSC-TDMA, to achieve higher information freshness. With the aid of the shared knowledge graph, KGSC-TDMA transmits the meaning of update messages in the form of triplets instead of exact bits, thus reducing the amount of information transmitted and the time to receive update messages. Furthermore, an update in KGSC-TDMA is successful, even with bit errors, as long as the AP can recover the meaning of the message received. Experimental results indicate that KGSC-TDMA outperforms conventional bit-oriented TDMA in average AoI, thanks to shorter packet durations and robust semantic recovery. © 2023 IEEE.

关键词： Time division multiple access

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Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis 21

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

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21st International computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Kulevome, Delanyo Kwame Bensah Man, Qiu Mawuli, Ernest Smith Opoku-Mensah, Edward Qilu Institute of Technology Department of Computer and Information Engineering Shandong Jinan250200 China Qilu Institute of Technology Department of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Department of Electrical/Electronic Technology Education Kumasi Ghana HEC Department of Information Technologies Montréal Canada

ISBN: (纸本)9798331519254

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems. © 2024 IEEE.

关键词： Federated learning

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