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arXiv 2024年

作者： Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long School of Integrated Circuits Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Physics and Electronic Science East China Normal University Shanghai200241 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Neuromorphic computing, which seeks to replicate the brain’s ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Multicarrier modulation

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Semantic data Management in data Lakes

arXiv

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arXiv 2023年

作者： Hoseini, Sayed Theissen-Lipp, Johannes Quix, Christoph Department of Electrical Engineering and Computer Science Hochschule Niederrhein University of Applied Sciences Reinarzstrasse 49 NRW Krefeld47805 Germany Databases and Information Systems RWTH Aachen University Ahornstraße 55 Aachen NRW Aachen52074 Germany Department of Data Science and Artificial Intelligence Fraunhofer Institute for Applied Information Technology FIT Schloss Birlinghoven NRW St. Augustin53757 Germany

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big data and Semantic Web technologies. © 2023, CC BY.

关键词： Big data

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SUBDIFFERENTIALLY POLYNOMIALLY BOUNDED FUNCTIONS AND GAUSSIAN SMOOTHING-BASED ZEROTH-ORDER OPTIMIZATION

arXiv

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arXiv 2024年

作者： Lei, Ming Pong, Ting Kei Sun, Shuqin Yue, Man-Chung College of Applied Mathematics Chengdu University of Information Technology Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Key Laboratory of Optimization Theory and Applications China West Normal University of Sichuan Province School of Mathematics Education China West Normal University Nanchong China Musketeers Foundation Institute of Data Science Department of Data and Systems Engineering The University of Hong Kong Hong Kong

We study the class of subdifferentially polynomially bounded (SPB) functions, which is a rich class of locally Lipschitz functions that encompasses all Lipschitz functions, all gradient- or Hessian-Lipschitz functions, and even some non-smooth locally Lipschitz functions. We show that SPB functions are compatible with Gaussian smoothing (GS), in the sense that the GS of any SPB function is well-defined and satisfies a descent lemma akin to gradient-Lipschitz functions, with the Lipschitz constant replaced by a polynomial function. Leveraging this descent lemma, we propose GS-based zeroth-order optimization algorithms with an adaptive stepsize strategy for minimizing SPB functions, and analyze their convergence rates with respect to both relative and absolute stationarity measures. Finally, we also establish the iteration complexity for achieving a (δ, ϵ)-approximate stationary point, based on a novel quantification of Goldstein stationarity via the GS gradient that could be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

引用

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate

Short-term Portfolio Optimization using Doubly Regularized E...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Quanlong Guan Jinneng He Zhao-Rong Lai Yuyu Zhou Quming Jiang Ziliang Chen College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory Zhuhai China Chutian Dragon Co. Ltd Dongguan China School of Intelligent Systems Science and Engineering Jinan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. This paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. The approach uniquely combines ℓ 1 and ℓ 2 -regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. This algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Computational modeling Benchmark testing Market research Robustness Regulation Computational efficiency Resource management

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Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum Software Development Project

arXiv

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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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The Enhanced N-Butanol Gas Sensing Properties Based on Perovskite-Type Znsno3 Nanoflowers

SSRN

引用

SSRN 2024年

作者： Meng, Fanli Zhou, Fangling Mu, Zhuangzhuang Yuan, Zhenyu Zhang, Renze Li, Yudong Zhu, Hongmin Yan, Xin College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

N-butanol is a volatile organic compound (VOC) that is harmful to human health, and the development of gas sensors for n-butanol vapor detection is necessary. In this work, the nanoflowers-like ZnSnO3 was synthesized via a one-step hydrothermal route followed by an annealing process. The results revealed that optimizing the hydrothermal time to 12 hours resulted in ZnSnO3-2 exhibiting the highest gas response (40.48) towards 100ppm n-butanol at 275°C, boasting a detection limit of 500 ppb. Furthermore, ZnSnO3-2 exhibits excellent gas selectivity and long-term stability. The improved sensing performance of ZnSnO3-2 was attributed to its large specific surface area and abundant oxygen vacancies. Additionally, the gas sensing mechanism for enhancing n-butanol detection was comprehensively analyzed. These results highlight the potential of nanoflower-like ZnSnO3 as a promising candidate for n-butanol gas sensing material, offering both high sensitivity and selectivity. Moreover, this research provides valuable guidance for the development and optimization of gas sensing materials for applications in environmental monitoring and industrial safety. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Natural Language Communication with a Conversational Teachable Agent

arXiv

引用

arXiv 2022年

作者： Love, Rachel Law, Edith Cohen, Philip R. Kulić, Dana The Department of Electrical and Computer Systems Engineering Monash University Australia The David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada Openstream Inc. The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia The Department of Electrical and Computer Systems Engineering Department of Mechanical and Aerospace Engineering Monash University Australia

Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher. This framing is significant for its ability to elicit the Protégé effect in the student-teacher, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, teachable agents often take a passive role in the learning interaction, and there are few studies in which the agent and student engage in natural language dialogue during the teaching task. This work investigates the effect of teaching modality when interacting with a virtual agent, via the web-based teaching platform, the Curiosity Notebook. A method of teaching the agent by selecting sentences from source material is compared to a method paraphrasing the source material and typing text input to teach. A user study has been conducted to measure the effect teaching modality on the learning outcomes and engagement of the participants. The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement. Furthermore, increased paraphrasing effort, as measured by the similarity between the source material and the material the teacher conveyed to the robot, improves learning outcomes for participants. © 2022, CC BY.

关键词： Students

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Agricultural data Privacy: Emerging platforms & strategies

Food and Humanity

引用

Food and Humanity 2025年 4卷

作者： Gavai, Anand K. Bouzembrak, Yamine Xhani, Donika Sedrakyan, Gayane Meuwissen, Miranda P.M. Souza, Renata Guizzardi-Silva Marvin, Hans J.P. van Hillegersberg, Jos Industrial Engineering & Business Information Systems University of Twente Enschede Netherlands Information Technology Group Wageningen University and Research Wageningen Netherlands Business Economics Group Wageningen University and Research Wageningen Netherlands Research department of HAYAN Group B.V Rhenen Netherlands Jheronimus Academy of Data Science (JADS) s-Hertogenbosch Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven 5612 AP Netherlands

Background: In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. data privacy within these sectors is increasingly significant, as the integration of advanced digital platforms becomes essential for improving efficiency and sustainability. Scope and Approach: This systematic literature review (SLR) explores the development and application of privacy-preserving data platforms specifically tailored to the agricultural and food supply chains. The review focuses on the evolving landscape of data architectures, data sovereignty, and advanced privacy-preserving techniques. Techniques such as anonymization, encryption, differential privacy, and federated learning are examined, along with the legal and ethical considerations surrounding data sharing in the context of global energy and climate-related challenges. Key Findings and Conclusions: The review synthesizes findings from a broad spectrum of studies published over the last decade, uncovering significant advancements in privacy-preserving technologies which may show their benefit for the agricultural and food sectors. It identifies dominant research trends and promising future directions for enhancing data security and sustainability. The key findings underscore the vital importance of safeguarding data privacy in these sectors, highlighting the potential of advanced privacy techniques to protect sensitive agricultural data while balancing the need for transparency and operational effectiveness. The review concludes that adopting these innovative privacy methods is crucial for fostering a more sustainable and secure future for agriculture and food systems, contributing to the global Sustainable Development Goals such as SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by improving precision agriculture, optimizing yields (crop and animal) and reducing food waste. © 2025 The

关键词： data Infrastructure data platforms data privacy Food systems Sustainability

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