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arXiv 2022年

作者： Shi, Kaiyan Herrman, Rebekah Shaydulin, Ruslan Chakrabarti, Shouvanik Pistoia, Marco Larson, Jeffrey Mathematics and Computer Science Division Argonne National Laboratory United States Dept. of Computer Science University of Maryland United States Industrial and Systems Engineering University of Tennessee Knoxville United States Global Technology Applied Research JPMorgan Chase United States

Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) significantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamiltonian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Redundancy

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Firefly Optimized Federated SVM Model for Breast Cancer Prediction

Firefly Optimized Federated SVM Model for Breast Cancer Pred...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Supriya Y Chemmalar Selvi G Gokul Yenduri Gautam Srivastava Thippa Reddy Gadekallu School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cancer is a leading cause of morbidity and mortality worldwide, with an estimation of 10 million deaths attributed to cancer each year. Among various types of cancer, Breast cancer is the most common cancer diagnosed among women, accounting for approximately 30% of all new cancer cases among women. Periodic clinical checks and self-tests will assist in the early identification of Breast cancer. Breast cancer detection at an early stage helps the patients to receive suitable treatment, which can increase the chances of their survivability. Automated clinical solutions, such as computer-Aided detection and AI-based algorithms, have shown increasing promise in improving the accuracy and efficiency of Breast cancer screening and diagnosis. Several serious challenges have to be addressed such as lack of standardization and regulation of these technologies, the need for large amounts of data for training and validation, as well as patients’ privacy and data security. To address these challenges, a novel Federated Learning architecture that, rather than sharing data, enables knowledge integration by sharing the model parameters of each client during the federated training process. In this paper, we proposed a federated Support Vector Machine for the early detection of Breast cancer. This work utilized the potential benefits of Federated Learning, Firefly algorithm, and Support Vector Machine to study the early detection of Breast cancer disease. The proposed model leverages a distributed computing approach, allowing the SVM model to be trained across multiple clients while preserving data privacy. In addition, we used the Firefly algorithm as a feature selection technique to identify the most relevant features for Breast cancer detection. We evaluated the performance of the proposed model using publicly available Breast cancer datasets namely Wisconsin dataset. The experimental results of this proposed work showed that our proposed model has achieved an accuracy of 95.68%.

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AI-Aided Kalman Filters

arXiv

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arXiv 2024年

作者： Shlezinger, Nir Revach, Guy Ghosh, Anubhab Chatterjee, Saikat Tang, Shuo Imbiriba, Tales Dunik, Jindrich Straka, Ondrej Closas, Pau Eldar, Yonina C. The School of ECE Ben-Gurion University of the Negev Beer-Sheva Israel The Institute for Signal and Information Processing D-ITET ETH Zürich Switzerland The School of EE and CS KTH Royal Institute of Technology Stockholm Sweden The Faculty of Applied Science University of West Bohemia Czech Republic The Dept. of CS University of Massachusetts Boston BostonMA United States The Dept. of ECE Northeastern University BostonMA United States The Math and CS Faculty Weizmann Institute of Science Rehovot Israel

The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Kalman filters

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A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

A Blockchain-Based Data-Sharing Framework for Cloud Based In...

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IEEE International Conference on Communications Workshops, ICC

作者： Kadiyala Ramana R.Madana Mohana C. Kishor Kumar Reddy Gautam Srivastava Thippa Reddy Gadekallu Department of Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Department of CSE Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Math and Computer Science Brandon University Brandon MB Canada Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

As the Internet of Things (IoT) has advanced, data sharing has become a crucial function of cloud computing. However, data security remains a significant challenge in this field. This research proposes a blockchain-based data-sharing system that prioritizes data security and efficiency. The system includes efficient smart contracts and security gateways that record data in the cloud using blockchain. If suspicious behaviour is detected, the blockchain is checked by the centralized cloud, and the responsible party for any malicious gateway behaviour is held accountable. Authentication and data exchange algorithms are used to ensure data security. Additionally, to reduce the burden on end-users, smart contracts in blockchain use highly complex partial decryption algorithms. To satisfy data restriction safety criteria, blockchain achieves traceability of historical actions through open and transparent supervision. Experimental findings demonstrate that the proposed technique is effective in ensuring the safety and efficiency of information exchange between various clients.

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Deep learning models for fast ultrasound localization microscopy

Deep learning models for fast ultrasound localization micros...

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2020 IEEE International Ultrasonics Symposium, IUS 2020

作者： Youn, Jihwan Luijten, Ben Bo Stuart, Matthias Eldar, Yonina C. Van Sloun, Ruud J. G. Arendt Jensen, Jorgen Technical University of Denmark Dept. of Health Technology Lyngby Denmark Eindhoven University of Technology Philips Research Dept. of Electrical Engineering Eindhoven Netherlands Weizmann Institute of Science Dept. of Computer Science and Applied Mathematics Rehovot Israel

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154480

Ultrasound localization microscopy (ULM) can surpass the resolution limit of conventional ultrasound imaging. However, a trade-off between resolution and data acquisition time is introduced. For microbubble (MB) localization, centroid detection is commonly used. Therefore, low-concentrations of MBs are required to avoid overlapping point spread functions (PSFs), leading to a long data acquisition time due to the limited number of detectable MBs in an image frame. Recently, deep learning-based MB localization methods across high-concentration regimes have been proposed to shorten the data acquisition time. In this work, a data-driven encoder-decoder convolutional neural network (deep-ULM) and a model-based deep unfolded network embedding a sparsity prior (deep unfolded ULM) are analyzed in terms of localization accuracy and computational complexity. The results of simulated test data showed that both deep learning methods could handle overlapping PSFs better than centroid detection. Additionally, thanks to its model-based approach, deep unfolded ULM needed much fewer learning parameters and was computationally more efficient, and consequently achieved better generalizability than deep-ULM. It is expected that deep unfolded ULM will be more robust in-vivo. © 2020 IEEE.

关键词： Data acquisition

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A Process-Oriented Deadlock Recovery Policy for Flexible Manufacturing Systems

A Process-Oriented Deadlock Recovery Policy for Flexible Man...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Andrei Karatkevich Iwona Grobelna Dept. of Applied Computer Science AGH University of Science and Technology Kraków Poland Autom. Control Electronics and Electr. Eng. University of Zielona Góra Zielona Góra Poland

ISBN: (纸本)9781665476881

This paper proposes a deadlock recovery method for flexible manufacturing systems modeled with Petri nets. Recovery transitions are added to the net allowing to return from all the deadlock markings to the legal ones. Unlike similar methods based on the recovery transitions, the proposed recovery policy focuses on process instances of the system. Its aim is to reset the lowest number of them, with minimal executed number of steps in a process instance. The proposed approach is formally analysed regarding its correctness and complexity, as well as illustrated with some widely used examples and compared with the similar known methods. The comparison shows that the presented method is efficient, despite the slightly different optimization criteria than used in other similar methods.

关键词： Law Firing Machine vision Petri nets System recovery Explosions Flexible manufacturing systems

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study

Detecting Worker Attention Lapses in Human-Robot Interaction...

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International Conference on Automation and Computing (ICAC)

作者： Zhuangzhuang Dai Jinha Park Aleksandra Kaszowska Chen Li Dept. of Computer Science Engineering and Physical Science School of Engineering and Applied Science Aston University Birmingham United Kingdom SDU Robotics The Maersk Mc Kinney Moller Institute University of Southern Denmark Odense Denmark Department of Electronic Systems Aalborg University Aalborg Denmark The Department of Materials and Production Aalborg University Aalborg Denmark

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness.

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UAV-Assisted Succulent Farmland Monitoring with Improved YOLOv10 and MambaIR

UAV-Assisted Succulent Farmland Monitoring with Improved YOL...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Hui Li Feng Xue Jiaqi Wang Dianhan Xi Yongying Liu Mowen Zhang Guocheng Zhang Danhua Liu Chenyu Zhang Jianghan Tao Zhijie Zhang Yinyin He Qingyang Wu Yijia Chen Graduate School of Frontier Science The University of Tokyo Kashiwa Japan Dept. Math and Applied Mathematics China University of Petroleum-Beijing Karamay China Graduate School of Business and Finance Waseda University Tokyo Japan Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Graduate School of Global Environmental Studies Sophia University Tokyo Japan Dept. Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong China Dept. Environmental Health Sciences University of California Los Angeles US

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Succulent plant image categorization suffers from subjectivity and limited number of available images, and these difficulties are intensified by the apparent lack of field surveys. In this study, we present a new approach to classify succulents using consumer-grade Unmanned Aerial Vehicles (UAVs) and deep learning. We applied various super-resolution algorithms with different image enhancement design strategies. In addition, we develop a new object detection model based on MambaIR and YOLOv10, achieving a mean average precision (mAP) of 0.851, outperforming other state-of-the-art object detection algorithms. The application of UAV s and super-resolution technology greatly facilitates the detection of succulents and provides a practical solution for precision crop monitoring.

关键词： Deep learning Surveys Superresolution Object detection Reconstruction algorithms Autonomous aerial vehicles Image categorization Classification algorithms Monitoring Image enhancement

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Forecasting Electricity Consumption of Electrical Machines of a Coal Industry Enterprise Using the Wavelet Transform

Forecasting Electricity Consumption of Electrical Machines o...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vadim Z. Manusov Dmitry V. Orlov Vitaly S. Karmanov Alexander O. Khusnutdinov Elena S. Tretyakova Dmitry V. Korolev Dept. of Industrial Power Supply Systems Novosibirsk State Technical University Novosibirsk Russian Federation Dept. of Theoretical and Applied Computer Science Novosibirsk State Technical University Novosibirsk Russian Federation Dept. of Power supply for industrial enterprises Angarsk State Technical University Angarsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

Electricity consumption forecasting is an integral part of the workflow in most industries that does not lose its relevance. Modern international energy-control systems do not function without forecast models of electricity consumption. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The main criteria are the accuracy of the forecast values and the trainability of this model based on the actual data on electricity consumption. A high-quality forecast of electricity consumption for a given planning horizon can significantly increase the energy efficiency of enterprise operation in real time with the possibility of production management of an industrial enterprise by taking into account the possible conditions of the object. In the paper, a mathematical model of short-term forecasting based on the wavelet transform for each hour for the day ahead is proposed. The selection of the model structure taking into account the features of the described process help to find and work with noise and outlier values in the data. The forecast values obtained using eleven basis wavelet functions were compared, and the average percentage values of relative forecast errors for each hour were obtained. The predicted values of electricity consumption for a given planning horizon were verified on test data in order to assess the relevance of the forecast model.

关键词： Wavelet transforms Schedules Time series analysis Predictive models Signal processing Harmonic analysis Data models

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Security Analysis of Docker Containers for ARM Architecture

Security Analysis of Docker Containers for ARM Architecture

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Md Sadun Haq Ali Şaman Tosun Turgay Korkmaz Department of Computer Science University Of Texas At San Antonio San Antonio USA Dept. of Math. and Comp. Sci University Of North Carolina at Pembroke Pembroke USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Containers provide a better performance, faster deployment than virtual machines and provide near-native performance, with isolation and security drawbacks. Although the security of containers for the Intel architecture has been investigated in more detail, there is limited work on the security of containers for the ARM architecture. In this paper, we address this gap in research and focus on the security of containers designed for the ARM architecture, which is heavily used in IoT devices. Edge computing offers many advantages, including reduced latency and resource requirements at the cloud because data can be processed at the edge before it is sent to the cloud. Using containers at the edge nodes of IoT-Edge-Cloud systems can enhance such advantages at the cost of increasing security vulnerabilities in such systems. Therefore, it is essential to investigate the security of containers designed for the ARM architecture. Accordingly, we obtained official ARM images from DockerHub and used various security tools to scan these ARM images. We found that 72% of all the vulnerabilities show varying severity levels and each tool seems to work best for particular base images. We investigated how each tool detects sub-packages and achieves a different hit ratio while none of them alone can detect at least 80% of all the vulnerabilities. In addition, we also investigated how the Docker images and their vulnerability landscape change over a period of six months by running the scanning tools twice. Finally, we also conducted a dynamic analysis of some of the images on the Raspberry Pi and study their effects. We believe this paper will facilitate the use of ARM containers at the ARM-based edge nodes by addressing security issues.

关键词： Cloud computing Program processors Image edge detection computer architecture Containers Virtual machining Libraries

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