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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Information security for big data using the NTRUEncrypt method

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Measurement: Sensors 2023年 27卷

作者： Khalid Yousif, Mohammed Dallalbashi, Zena Ez Kareem, Shahab Wahhab Department of Electronic Techniques Mosul Technical Institute Northern Technical University Iraq Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Erbil Iraq

Cloud computing processes vast quantities of data and offers a variety of flexible, secure, on-demand, and cost-effective collaboration options for consumers. Due to the increasing prevalence of hosted services, data security has become an increasingly critical concern. Hadoop, the engine at the heart of cloud computing, causes serious problems for the cloud. Any public, private, or hybrid cloud environment can use this security solution without any hassle (IaaS). Furthermore, it is compatible with the vast majority of Cloud computing's capabilities. Increase cloud security using NTRU encryption. This study made advantage of the (NTRUEncrypt) algorithms residing in Hadoop to speed up the file encryption and decryption processes. If HDFS is engaged in the Map Task, then HDFS will take care of both the encryption and decryption processes. Data on the cloud can be kept private and secure thanks to the proposed protection technique, which makes use of cryptography. Combining the proposed technique with preexisting infrastructure and web-based. © 2023 The Authors

关键词： Big data

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Sow posture and feeding activity monitoring in a farrowing pen using ground vibration 10

Sow posture and feeding activity monitoring in a farrowing p...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Codling, J.R. Dong, Y. Bonde, A. Bannis, A. Macon, A. Rohrer, G. Miles, J. Sharma, S. Brown-Brandl, T. Noh, H.Y. Zhang, P. Electrical Engineering and Computer Science Department University of Michigan Ann ArborMI United States Civil and Environmental Engineering Department Stanford University StanfordCA United States Department of Electrical and Computer Engineering Carnegie Mellon University–Silicon Valley Moffet FieldCA United States Biological Systems Engineering Department University of Nebraska–Lincoln LincolnNE United States USDA-ARS U.S. Meat Animal Research Center Clay Center NE United States

ISBN: (纸本)9788396536006

Automated monitoring of sow welfare and behaviors is a crucial tool in precision swine farming, giving farmers access to continuous streams of sow health information. Monitoring the activity of the sows helps farmers detect stress, sickness and signs of farrowing, which enables the farmers to provide timely care. Prior work in swine monitoring frequently uses video cameras, which have lighting and large storage and processing requirements. Alternatively, other work has used wearable sensors, which have limited longevity due to durability and battery requirements and suffer from scalability challenges due to the need for individual sensors worn by each sow. The objective of the study was to determine the effectiveness of geophone sensors mounted under the floor that measure the structural vibration of a farrowing pen to determine posture changes and animal feeding activity. A total of 6 farrowing/lactating sows and litters have been used in these studies. The data were collected from a minimum of 3 days before farrowing to approximately 25 days post-farrow. Up to five geophones were used for activity classification. Machine learning classification methods are used to detect the position and feeding activity of the sow and her piglets, including tree classifiers and principal component analysis. Accuracies of over 95% were achieved in sow posture and feeding activity classification, indicating the potential of monitoring ground vibration as a source of health information. © ECPLF 2022. All rights reserved.

关键词： Feeding

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Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

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computer Modeling in engineering & sciences 2023年第7期136卷 323-345页

作者： Smita Khade Shilpa Gite Sudeep D.Thepade Biswajeet Pradhan Abdullah Alamri Symbiosis International(Deemed University) Symbiosis Institute of TechnologyPune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Computer Engineering Pimpri Chinchwad College of EngineeringPune411044India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School for Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNew SouthWales2007Australia Earth Observation Center Institute of Climate ChangeUniversity Kebangsaan MalaysiaUKMBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from *** systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print *** work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris *** assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and *** predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and *** standard datasets were used in the *** main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature *** fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%***,conduct an extensive experiment on cross datasets for detailed *** results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

关键词： Iris images liveness identification Haar transform machine learning biometric feature formation ensemble model

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

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computers, Materials & Continua 2021年第10期69卷 679-694页

作者： Ayman Abdulhadi Althuwayb Fazirulhisyam Hashim Jiun Terng Liew Imran Khan Jeong Woo Lee Emmanuel Ampoma Affum Abdeldjalil Ouahabi Sébastien Jacques Department of Electrical Engineering Jouf UniversitySakakaAljouf72388Kingdom of Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)Serdang43400Malaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan School of Electrical and Electronics Engineering Chung-Ang UniversitySeoul06974Korea Electrical and Electronic Department Kwame Nkrumah University of Science and TechnologyKumasiGhana UMR 1253 iBrainUniversitéde ToursINSERMToursFrance LIMPAF Laboratory Department of Computer ScienceUniversity of BouiraBouira10000Algeria University of Tours(France) GREMAN UMR 7347CNRSINSA Centre Val-de-LoireTours37100France

With the rapid development of the mobile internet and the internet of things(IoT),the fifth generation(5G)mobile communication system is seeing explosive growth in data *** addition,low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency *** wave(mmWave)technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless ***,it has an abundant resource spectrum,which can significantly increase the communication rate of a mobile communication *** such,it is now considered a key technology for future mobile *** communication technology also has a more open network architecture;it can deliver varied services and be applied in many *** contrast,traditional,all-digital precoding systems have the drawbacks of high computational complexity and higher power *** paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy *** primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition(GMD).In this process,the objective function of the spectral efficiency is derived,then the basic tracking principle and least square(LS)techniques are deployed to design the proposed hybrid *** results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45%compared to traditional algorithms.

关键词： 5G mmWave phased array algorithm antenna beamforming

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FEDERATED LEARNING IN HEALTHCARE: MODEL MISCONDUCTS, SECURITY, CHALLENGES, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS-A SYSTEMATIC REVIEW

arXiv

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arXiv 2024年

作者： Ali, Md Shahin Tasnim, Lamia Ahsan, Md Manjurul Afrin, Sadia Biswas, Koushik Hossain, Md Maruf Ahmed, Md Mahfuz Islam, Md Khairul Hashan, Ronok Raman, Shivakumar Department of Biomedical Engineering Islamic University Kushtia7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka1205 Bangladesh Department of Industrial and Systems Engineering University of Oklahoma Norman OK73071 United States Department of Computer Science and Engineering IIIT Delhi New Delhi Delhi110020 India Department of Electrical and Electronic Engineering Islamic University Kushtia7003 Bangladesh

Data privacy has become a major concern in healthcare due to the increasing digitization of medical records and data-driven medical research. Protecting sensitive patient information from breaches and unauthorized access is critical, as such incidents can have severe legal and ethical complications. Federated Learning (FL) addresses this concern by enabling multiple healthcare institutions to collaboratively learn from decentralized data without sharing it. FL’s scope in healthcare covers areas such as disease prediction, treatment customization, and clinical trial research. However, implementing FL poses challenges, including model convergence in non-IID (independent and identically distributed) data environments, communication overhead, and managing multi-institutional collaborations. A systematic review of FL in healthcare is necessary to evaluate how effectively FL can provide privacy while maintaining the integrity and usability of medical data analysis. In this study, we analyze existing literature on FL applications in healthcare. We explore the current state of model security practices, identify prevalent challenges, and discuss practical applications and their implications. Additionally, the review highlights promising future research directions to refine FL implementations, enhance data security protocols, and expand FL’s use to broader healthcare applications, which will benefit future researchers and practitioners. © 2024, CC BY.

关键词： Clinical research

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A UVM-based Verification Approach for MIPI DSI Low-Level Protocol layer

A UVM-based Verification Approach for MIPI DSI Low-Level Pro...

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International Conference on Microelectronics, ICM

作者： Ahmed Mohamed Ali Ahmed Shalaby Sherif Saif Mohamed Taher Computer and Systems Engineering Department Ain Shams University Cairo Egypt Computer Science Department Benha University Benha Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt

ISBN: (纸本)9781665493253

Display Serial Interface (DSI) is a high-speed serial interface standard. It supports display and touch screens in mobile devices such as smartphones, laptops, tablets, and other platforms. DSI describes several layers that define the detailed interconnect between a host processor and a peripheral device in a mobile system. The targeted DSI protocol layer is the low-level layer in the standard which is responsible for bytes organization, error-checking information addition, and packet formulation. In this paper, we propose a constrained random Coverage-Driven Verification approach (CDV) for the DSI low-level protocol layer using Universal Verification Methodology (UVM). This approach could be the basis of a standardized Verification Intellectual Property (VIP) to test and verify the standardized DSI layer. We provide a well-established base of a reusable and scalable UVM environment that can verify the DSI protocol layer using various techniques such as error injection mechanism, System Verilog Assertions (SVA), and direct UVM sequences aim to cover the different real-life scenarios between the host processor and the peripheral device. Our results show that we can detect all inserted errors to assure the functionality of the DSI low-level protocol layer. Different real-life scenarios between the host processor and the peripheral device are covered with 100% functional coverage and 93% code coverage.

关键词： Protocols Codes Portable computers Standards organizations Intellectual property Organizations Touch sensitive screens

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Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning 17th

Improving Autonomous Vehicles Safety in Snow Weather Using F...

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17th International Conference on Mobile Web and Intelligent Information systems, MobiWIS 2021

作者： Rjoub, Gaith Wahab, Omar Abdel Bentahar, Jamal Bataineh, Ahmed Saleh Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science and Engineering Université du Québec en Outaouais Quebec Canada

ISBN: (纸本)9783030831639

Accurate object detection (e.g., buildings, vehicles, road signs and pedestrians) is essential to the success of the idea of autonomous and self-driving cars. Various object detection techniques have been proposed to enable Autonomous Vehicles (AVs) to achieve reliable safe driving. Most of these techniques are adequate for normal weather conditions, such as sunny or overcast days, but their effectiveness drops when they are exposed to inclement weather conditions, such as days with heavy snowfall or foggy days. In this paper, we propose an object detection system over AVs that capitalizes on the You Only Look Once (YOLO) emerging convolutional neural network (CNN) approach, together with a Federated Learning (FL) framework with the aim of improving the detection accuracy in adverse weather circumstances in real-time. We validate our system on the Canadian Adverse Driving Conditions (CADC) dataset. Experiments show that our solution achieves better performance than traditional solutions (i.e. Gossip decentralized model, and Centralized model). © 2021, Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

arXiv

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arXiv 2025年

作者： Jeon, Dong Hyun Sun, Wenbo Song, Houbing Herbert Liu, Dongfang Alvaro, Velasquez Xie, Yixin Chloe Niu, Shuteng Department of Computer Science Bowling Green State University United States Transportation Research Institute University of Michigan Ann Arbor United States Department of Information Systems University of Maryland Baltimore County United States Department of Computer Engineering Rochester Institute of Technology United States College of Engineering & Applied Science University of Colorado Boulder United States Department of Information Technology Kennesaw State University United States

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized. This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1. © 2025, CC BY.

关键词： Knowledge graph

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