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arXiv 2023年

作者： Yan, Yuping Shao, George Song, Dennis Song, Mason Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou310024 China Mind Network Singapore Faculty of Informatics Department of Computeralgebra Budapest1117 Hungary Ethereum Foundation Zug Switzerland Department of Computer Science University of Surrey Surry GuildfordGU2 7XH United Kingdom

Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheless, this technique introduces various privacy concerns, including pseudonymity, Sybil attacks, and potential susceptibilities to quantum computing, to name a few. In response to these challenges, innovative privacy-enhancing solutions like zero-knowledge proofs, homomorphic encryption, and stealth addresses (SA) have been developed. Among the various schemes, SA stands out as it prevents the association of a blockchain transaction's output with the recipient's public address, thereby ensuring transactional anonymity. However, the basic SA schemes have exhibited vulnerabilities to key leakage and quantum computing attacks. To address these shortcomings, we present a pioneering solution ' Homomorphic Encryption-based Dual-Key Stealth Address Protocol (HE-DKSAP), which can be further extended to Fully HE-DKSAP (FHE-DKSAP). By leveraging the power of homomorphic encryption, HE-DKSAP introduces a novel approach to safeguarding transaction privacy and preventing potential quantum computing attacks. This paper delves into the core principles of HE-DKSAP, highlighting its capacity to enhance privacy, scalability, and security in programmable blockchains. Through a comprehensive exploration of its design architecture, security analysis, and practical implementations, this work establishes a privacy-preserving, practical, and efficient stealth address protocol via additively homomorphic encryption. © 2023, CC BY.

关键词： Blockchain

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Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations

arXiv

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arXiv 2023年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We consider a class of high-dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging as not only is high-dimensional, but the model for the signal yields longer-range time dependencies through the observation locations. Motivated by this model we revisit a lesser-known and provably convergent computational methodology from [4, 11, 28] that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on Linear Gaussian state space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high-dimensional rotating shallow water model (of about 104 - 105 dimensions) with real and synthetic data. The data is provided by the National Oceanic and Atmospheric Administration (NOAA) and contains observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals [-51◦,-41◦], [17◦,27◦] *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2023, CC BY.

关键词： Location

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Exploring the Structure of IoT Data: A Symbolic Analysis Perspective

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Shen, Yinghua Pedrycz, Witold Li, Wentao Xiao, Zhi Chen, Tianhua Hu, Xuan Chen, Yuan School of Economics and Business Administration Chongqing University Chongqing400044 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada College of Artificial Intelligence Southwest University Chongqing400715 China Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Public Policy and Administration Chongqing University Chongqing400044 China College of Management and Economics Tianjin University Tianjin300072 China

With the development of different kinds of techniques, especially the Internet of Things (IoT), a large amount of quantitative (either numeric or categorical) data have been generated, transmitted, and stored in the modern society. People hope to understand the interested phenomenon from the collected quantitative data by utilizing different data analysis methods. Exploring the structure of data (e.g., the cluster centers or prototypes) has always been a hot spot in the domain of data mining and knowledge discovery, yet it seems that the modeling and analyzing process still focus on a low-level abstraction of the data because normally, the structure found is only represented by some numeric data points. In this study, we highlight that a low-level abstraction may not be a user-friendly way for people to grasp the knowledge contained in the data. Instead, we explore the structure of the data from a perspective of symbolic analysis. Specifically, two modes of abstraction are proposed. In the vertical mode (i.e., values of each feature are abstracted), the numeric prototypes are characterized by the symbolic prototypes such that people could get rid of being stuck in minor details of each feature. In the horizontal mode (i.e., values of each prototype are abstracted), the linguistic summarization is used to describe all the features of each symbolic prototype such that people could immediately grasp the essential information conveyed in the symbolic prototype. We conduct comprehensive experimental studies on the publicly available data to illustrate the feasibility and validity of the proposed symbolic analysis process. © 2023 Yinghua Shen et al.

关键词： Internet of things

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Research on Main Steam Temperature Prediction Model Based on Improved LSTM Algorithm 2

Research on Main Steam Temperature Prediction Model Based on...

引用

2nd International Conference on Artificial Intelligence and computer science, AICS 2020

作者： Zheng, Ling Ye, Xin Chen, Fei Department of Computer Science and Technology North China Electric Power University Faculty of Control and Computer Engineering room E1112 No. 2 Beinong Road Huilongguan Town Changping District Beijing China

In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved LSTM is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the LSTM structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the LSTM model, and the improved LSTM model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering. © Published under licence by IOP Publishing Ltd.

关键词： Correlation methods

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A Screening Assessment of the Impact of Sedimentological Heterogeneity on Co2 Migration and Stratigraphic-Trapping Potential: Johansen and Cook Formations, Northern Lights Project, Offshore Norway

SSRN

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SSRN 2022年

作者： Jackson, William A. Hampson, Gary J. Jacquemyn, Carl Jackson, Matthew D. Petrovskyy, Dmytro Geiger, Sebastian Machado Silva, Julio D. Judice, Sicilia Rahman, Fazilatur Sousa, Mario Costa Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Institute of Geoenergy Engineering Heriot-Watt University EdinburghEH14 4AS United Kingdom Department of Computer Science University of Calgary CalgaryTN2 1N4 Canada

We use a method combining experimental design, sketch-based reservoir modelling, and flow diagnostics to rapidly screen the impact of sedimentological heterogeneities on CO 2 migration and storage by stratigraphic trapping in the Johansen and Cook formations at the Northern Lights CO 2 storage site. The types and spatial organisation of sedimentological heterogeneities in the wave-dominated deltaic sandstones of the Johansen-Cook storage unit are constrained using core data from the 31/5-7 (Eos) well, previous interpretations of seismic data and regional well-log correlations, and outcrop and subsurface analogues. Delta planform geometry, clinoform dip, and facies-interfingering extent along clinoforms control the distribution and connectivity of high-permeability medial and proximal delta-front sandstones, effective horizontal and vertical permeability characteristics of the storage unit, and pore volumes injected at breakthrough time (which approximates stratigraphic storage efficiency). In addition, the lateral continuity of carbonate-cemented concretionary layers along transgressive surfaces impacts effective vertical permeability, and bioturbation intensity impacts effective horizontal and vertical permeability. The combined effects of these and other heterogeneities are also influential. Our results suggest that more detailed modelling studies should in future incorporate the effects of sedimentological heterogeneity on CO 2 migration and stratigraphic-trapping potential, as a precursor for later capillary, dissolution and mineral trapping. © 2022, The Authors. All rights reserved.

关键词： Stratigraphy

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Three-dimensional Chaotic Fractional Maps without Fixed Points: Dynamics, Coexisting Hidden Attractors and Hardware Implementation

Three-dimensional Chaotic Fractional Maps without Fixed Poin...

引用

International Conference on Fractional Differentiation and Its Applications (ICFDA)

作者： Giuseppe Grassi Amina Aicha Khennaoui Adel Ouannas Viet-Thanh Pham Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy TLSI Department NTIC Faculty University of Constantine 2 Constantine Algeria Department of Mathematics and Computer Science University of Oum El-Bouaghi Algeria Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton DucThang University Ho Chi Minh City Vietnam

In recent years there has been a great interest in investigating the dynamic properties of fractional systems, described by differential or difference equations of non-integer order. A number of manuscripts have been written regarding the presence of chaos in fractional maps characterized by the existence of both “self-excited attractors” and “hidden attractors However, most of the papers have dealt with the underlying theory in chaotic dynamics, rather than the hardware implementation of the maps. Specifically, referring to the realization of fractional maps with “hidden attractors no paper has been published so far. The paper aims to bridge the gap between theory and implementation by presenting the first example of hardware realization of a three-dimensional (3D) fractional map characterized by coexisting chaotic hidden attractors. The manuscript exploits first the Grunwald-Letnikov fractional difference operator to introduce a new class of chaotic discrete systems without fixed points. Then, the coexistence of several chaotic hidden attractors is shown, along with the coexistence of a number of bifurcations, depending on the values of the initial conditions. Finally, the conceived 3D fractional map is implemented in hardware via a microcontroller. The motivation is to probe the presence of coexisting chaotic hidden attractors in real working systems described by non-integer order difference equations.

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Energy Consumption Analysis for Adaptive Transmission of Big Data over Fading Channels: A Statistical Characterization

Energy Consumption Analysis for Adaptive Transmission of Big...

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作者： Wang, Wen-Jing Yang, Hong-Chuan Alouini, Mohamed-Slim School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China Department of Electrical and Computer Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

We study the energy consumption of the adaptive transmission of big data over fading channels. Transmission of big data usually lasts over multiple channel coherence time periods. With adaptive transmission, the transmission rate and/or power is adaptively adjusted according to the channel realization. The energy comsumption varies with channel realizations. We propose a novel analytical framework to statistically evaluate the energy consumption of adaptive big data transmission. For the slow fading scenario, we derive the exact probability density function (PDF) and cumulative distribution function (CDF) of energy consumed over Markov channels. For the fast fading cases, we apply the statistical mixture model to estimate the PDF of energy consumption for wireless transmission of big data adaptively. Selected numerical results are presented to illustrate and to validate the mathematical formulations. These analytical results will greatly benefit the study of big data applications in the wireless environment. © 2017 IEEE.

关键词： Fading channels

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Highlighting a Common Confusion in the Computation of Capacitance of Electrochemical Energy Storage Devices

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JOURNAL OF PHYSICAL CHEMISTRY C 2021年第18期125卷 9591-9592页

作者： Allagui, Anis Elwakil, Ahmed S. Eleuch, Hichem Department of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Center for Advanced Materials Research Research Institute of Sciences and Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical and Materials Engineering Florida International University Miami Florida 33174 United States Department of Electrical and Computer Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Nanoelectronics Integrated Systems Center (NISC) Nile University Cairo 12677 Egypt Department of Electrical and Computer Engineering University of Calgary Calgary Alberta T2N 1N4 Canada Department of Applied Physics and Astronomy University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Institute for Quantum Science and Engineering Texas A&M University College Station Texas 77843 United States

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FLAME-STATE MONITORING BASED ON VERY LOW NUMBER OF VISIBLE OR INFRARED IMAGES VIA FEW-SHOT LEARNING

arXiv

引用

arXiv 2022年

作者： Kang, Ruiyuan Liatsis, Panos Kyritsis, Dimitrios C. Department of Mechanical Engineering Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Mechanical Engineering Research and Innovation Center on CO2 and Hydrogen Khalifa University Abu Dhabi United Arab Emirates

The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this conflict, we introduce few-shot learning in order to achieve combustion monitoring and classification for the first time. Two algorithms, Siamese Network coupled with k Nearest Neighbors (SN-kNN) and Prototypical Network (PN), were tested. Rather than utilizing solely visible images as discussed in previous studies, we also used Infrared (IR) images. We analyzed the training process, test performance and inference speed of two algorithms on both image formats, and also used t-SNE to visualize learned features. The results demonstrated that both SN-kNN and PN were capable to distinguish flame states from learning with merely 20 images per flame state. The worst performance, which was realized by PN on IR images, still possessed precision, accuracy, recall, and F1-score above 0.95. We showed that visible images demonstrated more substantial differences between classes and presented more consistent patterns inside the class, which made the training speed and model performance better compared to IR images. In contrast, the relatively low quality of IR images made it difficult for PN to extract distinguishable prototypes, which caused relatively weak performance. With the entrire training set supporting classification, SN-kNN performed well with IR images. On the other hand, benefitting from the architecture design, PN has a much faster speed in training and inference than SN-kNN. The presented work analyzed the characteristics of both algorithms and image formats for the first time, thus providing guidance for their future utilization in combustion monitoring *** Codes 80A25, 68T30, 68T45 © 2022, CC BY-NC-SA.

关键词： Inference engines

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A Dynamic Grouping Strategy for Beyond Diagonal Reconfigurable Intelligent Surfaces with Hybrid Transmitting and Reflecting Mode

arXiv

引用

arXiv 2022年

作者： Li, Hongyu Shen, Shanpu Clerckx, Bruno The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong GrazA-8010 Austria

Beyond diagonal reconfigurable intelligent surface (BD-RIS) is a novel branch of RIS which breaks through the limitation of conventional RIS with diagonal scattering matrices. However, the existing research focuses on BD-RIS with fixed architectures regardless of channel state information (CSI), which limit the achievable performance of BD-RIS. To solve this issue, in this paper, we propose a novel dynamically group-connected BD-RIS based on a dynamic grouping strategy. Specifically, RIS antennas are dynamically divided into several subsets adapting to the CSI, yielding a permuted block-diagonal scattering matrix. To verify the effectiveness of the proposed dynamically group-connected BD-RIS, we propose an efficient algorithm to optimize the BD-RIS with dynamic grouping for a BD-RIS-assisted multi-user multiple-input single-output system. Simulation results show that the proposed dynamically group-connected architecture outperforms fixed group-connected architectures. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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