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arXiv 2024年

作者： Zhang, Shupei Di, Boya Kaushik, Aryan Eldar, Yonina C. State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The Department of Computing & Mathematics Manchester Metropolitan University United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may distribute in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Quantum algorithms for sampling log-concave distributions and estimating normalizing constants 22

Quantum algorithms for sampling log-concave distributions an...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Andrew M. Childs Tongyang Li Jin-Peng Liu Chunhao Wang Ruizhe Zhang Joint Center for Quantum Information and Computer Science Department of Computer Science and Institute for Advanced Computer Studies University of Maryland Center on Frontiers of Computing Studies and School of Computer Science Peking University Simons Institute and Department of Mathematics UC Berkeley Department of Computer Science and Engineering Pennsylvania State University Department of Computer Science The University of Texas at Austin

ISBN: (纸本)9781713871088

Given a convex function f : ℝd → ℝ, the problem of sampling from a distribution ∝ e-f(x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\int_{\mathbb{R}^d}e^{-f(x)}\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number κ and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error e. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity Õ(κ1/2d) and Õ(κ1/2d3/2/ε) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in κ, d, ε over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/ε1-o(1) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in ε.

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Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine

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International Journal of Information Technology (Singapore) 2021年第5期13卷 1847-1859页

作者： Rahi, Pankaj Sood, Sanjay P. Bajaj, Rohit Kumar, Yogesh University Institute of Computing Chandigarh University Gharuan Mohali Punjab India Health Informatics Division Centre for Development of Advanced Computing Mohali Punjab India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali Punjab India Department of Computer Engineering Chandigarh Group of Colleges Landran Mohali Punjab India

The air quality monitoring system systematically monitors the level of pollutants in the air by measuring the concentration of the particular pollutant in the surrounding and the outside environment. The strategy for developing the monitoring system should ensure the acceptable quality of data, to store and record the data in the database, the analysis of data and to present the result. In this concern, we have proposed a smart system of air quality monitoring that help in preventing and controlling airborne allergies and reducing the burden of disease and the cost of treatment. The healthcare data layers and healthcare APIs for standardizing the smart health predictive analytics were derived using meta-heuristic and Machine learning algorithms. This work mainly focused on improving the existing expert systems of air quality monitoring. In concern of this, the detail study of various terms related to air quality monitoring has been done and proposed a new approach that able to gives a better outcomes. In the proposed work, the meta-heuristic firefly optimization has been applied to optimize the selected features during feature selection process and then further classified by using support vector machine which predict the index level and gives better precision and recall of 95.7% and 93.1% respectively and accuracy of 94.4% while compare it with the existing approaches. © 2021, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Air quality monitoring system eHealth System Firefly optimization Health issues Support vector machine

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Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

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IEEE Internet of Things Journal 2024年第4期12卷 4362-4374页

作者： Qingyang Ding Xiaofei Yue Qinnan Zhang Zehui Xiong Jinping Chang Hongwei Zheng School of Management Beijing Union University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Tampines Singapore Blockchain and Privacy Computing Technology R&D Department Beijing Academy of Blockchain and Edge Computing Beijing China

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

关键词： Blockchains Internet of Things Privacy Computational modeling Federated learning Data models Biological system modeling Adaptation models Accuracy Servers

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Fidelity and diversity metrics for validating hierarchical synthetic data: Application to residential energy demand

Fidelity and diversity metrics for validating hierarchical s...

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IEEE International Conference on Big Data

作者： Swapna Thorve Anil Vullikanti Henning S Mortveit Samarth Swarup Madhav V. Marathe Department of Computer Science Biocomplexity Institute University of Virginia Network Systems Science and Advanced Computing Biocomplexity Institute University of Virginia Department of Engineering Systems and Environment Biocomplexity Institute University of Virginia

ISBN: (纸本)9781665480468

Synthetic data is gaining rapid importance in many application domains due to privacy issues, bias, lack, or simply unavailability of real data. It is important that the synthetic data be a good representation of real data for successfully completing the task at hand. Thus, devising characteristic validation metrics is crucial and remains an open problem in many domains (e.g., image generation). Good validation metrics must be able to disentangle the differences between the quality and the variability coverage of the synthetic data. We propose to use a 3-dimensional metric (precision α, recall β, coverage γ) to describe the fidelity and diversity of the synthetic data. In this paper, we improve on existing definitions of precision, recall, and coverage to extend to large scale time series data. Traditional nearest neighbor manifolds from the literature are replaced by unsupervised learning techniques such as clustering to deal with large scale fine resolution time series while computing the validation metrics. The proposed metrics are employed to validate synthetic data in the domain of residential energy demand. In addition, we extend these definitions to datasets that have a natural hierarchical structure. We propose a hierarchical data-tree model in which precision, recall, and coverage can be computed at multiple inherent (and/or custom) levels of groupings of the data.

关键词： Measurement Manifolds Image synthesis Time series analysis Machine learning Big Data Data models

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An IoT based Voice Assisted Smart Residential Appliance Control Mechanism using Intelligent Node MCU Controller

An IoT based Voice Assisted Smart Residential Appliance Cont...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Aswathy K Cherian H. Mercy R. Reshma K. Sivakami R. Krishnamoorthy R. Thiagarajan Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science And Technology Chennai India Department of Computer Science and Engineering Jerusalem College of Engineering Chennai India Dept of Computer science Justice Basheer Ahmed Sayeed college for Women Chennai India Department of ECE Nehru Institute of Engineering and Technology Coimbatore India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of IT Prathyusha Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The idea of a gadget operated by a remote or switch is today considered antiquated. This is the technology of the future. The article presents a novel approach of managing smart appliances with voice commands, which is referred to as Voice Assisted Smart Appliance Control (VASAP). In order to assess the efficacy and performance of the suggested system, it is compared against the traditional GPRS Assisted Smart Appliance Control (GPRSSAC). An Android app requires the user to speak commands. Voice instructions may be recognized and transmitted to the system using this program. These commands are decoded by a receiver and decoder, which then send them over a serial connection to the Arduino controllers. The system's software and hardware are developed in accordance with this plan, and the system's wireless communication technique, which integrates Wi-Fi and RF (radio frequency), is also established. The systems functional needs inform the design of two sets of control schemes-one for voice control and one for button control-with the goal of making the system more compassionate. For the voice control solution to work, the user must first read out from the smart terminal the control instructions that have been pre-programmed into the system's voice library. Once the recognition is confirmed, the user will be able to command their household appliances. Under the key control scheme, smart terminal keys are used to operate household appliances. In order to collect and store audio, verify family members, and recognize voice commands, the upper computer uses an algorithm that was studied in this study. The lower computer collects environmental information in real-time and wirelessly controls home facilities. Upon startup, family members are asked to confirm their voice commands. Following this, the system uses real-time environmental information and the results of voice command recognition to formulate a control command. Through the serial port, the command to operate the ass

关键词： Wireless communication Home appliances Bluetooth Home automation Operating systems Wheelchairs Speech recognition Real-time systems Relays Wireless fidelity

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On-site Testing and Preventive Maintenance for a Fire Automation System Employing PSO, FA and ANN Algorithms

On-site Testing and Preventive Maintenance for a Fire Automa...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： B. Rubini G Kalaiarasi S. Vasuki S. Pradeep Kumar Suganiya Murugan C. Arul Stephen Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Advanced Computer Science and Engineering Vignan Foundation for Science Technology and Research Deemed to be University Guntur Andhrapradesh India Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Kaattankulathur Chennai India Department of Electronics and Communication Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

Engineering elements influencing in commercial and government buildings fire safety rendition buildings have been deliberated. For both new and existing buildings, all variables such as structure of building, damage due to fire from prevention compartments, Time of incidents evacuation routes should be control leverage on people’s evacuation routes, controlled, and additional standard fire protection equipment are imposed. This page outlines the classes of diesel fire pumps, batteries, and pumping systems, besides the elements prompting fire rendition. Onsite diesel generator pump must be connected to a hydrant and the water pumping pressure range must be customized to 8 bar using a pressure gauge. The substantive intent of this concert is to reinforce firefighter safety. Only experienced technicians and training professionals in fire guard are responsible for this part. To simulate the battery tightness position, an extra-critical condition has been considered. Owing to the position of the fire safety properties and battery reaction in buildings, the Firefly and Particle Swarm optimization algorithms have been exerted on the iterations with objective values used for analysis in the iterative process using Matlab 2021 software. Two well-known algorithms are compared to see which provides the best results. The ANN serves as a tool for analyzing and optimizing the pump’s rendition in maintaining an adequate water supply for fire emergencies.

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Voting in Watts-Strogatz Small-World Network

Voting in Watts-Strogatz Small-World Network

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International Conference on Frontiers in computing and Systems, COMSYS 2020

作者： Ray, Soujanya Chatterjee, Kingshuk Majumdar, Ritaji Ganguly, Debayan Department of Computer Science and Engineering Netaji Subhash Engineering College Kolkata India Department of Computer Science Government College of Engineering and Ceramic Technology Kolkata India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science Government College of Engineering and Leather Technology Kolkata India

ISBN: (纸本)9789811578335

Social network science and social interaction science is an interesting and steadily growing field of research in current time. Social network influences the lives of millions, and therefore the propagation of influence in such a network deserves study. In this paper, we have studied voting in a Watts-Strogatz small-world network for two parties. In our model, each node has an initial bias towards one of the parties (or can be neutral) and are influenced by their neighbours to vote for a particular party. We show via simulation that (i) for linear of logarithmic voting function, the small-scale variation is minimum, but the majority of the nodes tend to align towards one of the parties in the long term, (ii) for periodic voting function, the small-scale variations are sharp and oscillating, but in the long term the number of voters in each party roughly remains the same, (iii) the degree of the graph does not seem to have a strong influence on the result, and (iv) networks with higher small-world probability tend to resist the alignment of voters towards a particular party for a longer time as compared to networks with lower small-world probability. This majority voting model seems to efficiently capture the potential behaviour of voters on small-world network over a campaigning period. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Small-world networks

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Contextual adaptative interfaces for industry 4.0 17th

Contextual adaptative interfaces for industry 4.0

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17th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2020

作者： Canito, Alda Mota, Daniel Marreiros, Goreti Corchado, Juan M. Martins, Constantino GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto Portugal Department of Computer Science University of Salamanca Salamanca Spain

ISBN: (纸本)9783030538286

Information technologies are intrinsically connected to the manufacturing processes, with more data generated each second. To efficiently operate machines, users must sort out information that is relevant to them in specific moments and contexts. In this paper, we propose an architecture that combines context – e.g. location, type of order, available assets, previous actions – with information established through user stereotypes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Industry 4.0

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Boosting Cybersecurity Effectiveness through Machine Learning for Proactive Detection and Mitigation of New Threats

Boosting Cybersecurity Effectiveness through Machine Learnin...

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International Conference on Advances in Information Technology (ICAIT)

作者： N. Duraimutharasan N.Venkatesvara Rao N. Poongavanam K.V. Kanimozhi S.P. Manikandan Faculty of Advanced Computing Sciences AMET UNIVERSITY Chennai Tamil Nadu India Department of Computer Science and Engineering J.N.N.INSTITUTE OF ENGINEERING Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Engineering and Technology CMR UNIVERSITY Bengaluru Karnataka

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the.. p roactive” recognition and the “mitigation” of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats.

关键词： Deep learning Heuristic algorithms Prevention and mitigation Malware Data models Real-time systems computer crime Anomaly detection Monitoring Resilience

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