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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Chu, Jielei Wang, Hongjun Liu, Jing Gong, Zhiguo Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Sichuan University School of Business Sichuan610065 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

ISBN: (纸本)9798350322279

Feature learning is a crucial phase machine learning [1] - [3]. How to obtain appropriate features distribution without any background is still a hard problem in machine learning. In this paper, we present a novel unsupervised feature learning architecture (see Fig. 1 ), which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we choose three clusterers to obtain three different global clustering partitions (CPs). Then, an unanimous voting strategy is used to generate the local clustering partition (LCP) of visible layer data. Hence, the LCP only has partial visible layer data. The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into the CD 1 learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. This work has three main contributions: 1) A novel unsupervised feature learning architecture is proposed, which consists of a multi-clustering integration module and an MIRBM model. 2) In the multi-clustering integration module of the proposed architecture, three unsupervised algorithms are employed to obtain three different global CPs without any background knowledge or label. 3) The MIRBM model in the proposed architecture uses the LCP as an unsupervised guidance to guide the distribution of the hidden layer features by integrating the LCP into the CD 1 learning. © 2023 IEEE.

关键词： Integration

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On the Effect of Design Parameters on Fringing Fields of a Loop-Based FSS

On the Effect of Design Parameters on Fringing Fields of a L...

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2021 IEEE International Symposium on Antennas and Propagation and North American Radio science Meeting, APS/URSI 2021

作者： Ramesh, Swathi Muthyala Mahmoodi, Mahboobeh Donnell, Kristen M. Laboratory Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO United States

ISBN: (纸本)9781728146706

Frequency selective surfaces (FSSs) are periodic arrays of conductive elements that act as spatial filters of electromagnetic energy. FSSs have found recent application as sensors, filters, reflectors and radomes, amongst others. Often miniaturization techniques are used to improve the stability of the FSS to incident angle (of the excitation). However, due to the type of coupling (internal/self vs. external/mutual with adjacent elements), some designs are more responsive to miniaturization techniques than others. Hence, this paper quantifies the effect of the ratios of inter-element spacing-to-substrate thickness (S/h) and side length-to-substrate thickness (SL/h) on fringing fields (and hence coupling and potential for miniaturization) of a loop-based FSS. To do this, the effective perimeter and electrical periodicity of the loop (resulting from fringing fields) is calculated based on the effective permittivity of the substrate and the resonant frequency for various substrate thicknesses. The results indicate that external coupling (with adjacent elements) dominates when S/h is less than SL/h and hence such designs are more receptive to miniaturization techniques. © 2021 IEEE.

关键词： Frequency selective surfaces

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures 4

FedMonitor: An Implementation Architecture for Monitoring of...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Nnaji, Uche Wejin, John S. Ojesanmi, Festus Akanle, Matthew B. Ajayi, Priscilla O. Sweetwiliams, Faith O. Thakur, Surendra C. Adebiyi, Ezekiel F. Department of Electrical and Information Engineering and Covenant Applied Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Department of Computer and Information Science & CApIC-ACE Ota Nigeria Covenant University Department of Electrical and Information Engineering & CApIC-ACE Jalingo Nigeria Tareba State University Department of Computer Science Jalingo Nigeria Ota Nigeria Covenant University & CApIC-ACE Covenant University Department of Electrical and Information Engineering Ota Nigeria Covenant University Center for Systems and Information Services & CApIC-ACE Ota Nigeria Durban University of Technology e-Skills Co-Laboratory Department of Information Technology and KZN Durban4001 South Africa Covenant University Ogun State Ota Nigeria Makerere University African Center of Excellence in Bioinformatics & Data Intensive Science Kampala10218 Uganda

ISBN: (纸本)9798350353266

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments. © 2024 IEEE.

关键词： Cloud platforms

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A New Mutimodel approach for modelling of fractional order interval transfer functions 20

A New Mutimodel approach for modelling of fractional order i...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Talmoudi, Samia Ksouri, Moufida Lahmari Applied College Department of Computer Science University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis1002 Tunisia

ISBN: (纸本)9798350332568

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models' library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library' models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples. © 2023 IEEE.

关键词： Transfer functions

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Nonparametric Sparse Online Learning of the Koopman Operator

arXiv

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arXiv 2025年

作者： Hou, Boya Sanjari, Sina Dahlin, Nathan Koppel, Alec Bose, Subhonmesh Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign United States Department of Mathematics and Computer Science Royal Military College of Canada Canada Department of Electrical and Computer Engineering University at Albany SUNY United States Artificial Intelligence Research JP Morgan Chase & Co Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign United States

The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and explore the mis-specified scenario where the dynamics may escape the chosen function space. We relate the Koopman operator to the conditional mean embeddings (CME) operator and then present an operator stochastic approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation. We provide both asymptotic and finite-time last-iterate guarantees of the online sparse learning algorithm with trajectory-based sampling with an analysis that is substantially more involved than that for finite-dimensional stochastic approximation. Numerical examples confirm the effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Dynamical systems

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P-192: Advancements in Photoaligned Azo-Dye Thin-Film Polarizer with Exceptional Dichroic Ratio

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SID Symposium Digest of Technical Papers 2024年第1期55卷 2119-2122页

作者： Yue-Chu Cheng Man-Chun Tseng Yeuk-Lung Ho Olena Vashchenko Valerii Vashchenko Shu-Tuen Tang Sze-Yan Yeung Hoi-Sing Kwok State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong S.A.R. China

This study presents the development of ultra-thin azo-dye polarizers, notable for their minimal thickness (100-200 nm) and superior optical performance, with polarization efficiency exceeding 99.9%, extinction ratios above 4000, transmittance over 45.3%, and dichoric ratio over 100. Utilizing cost-effective solution-processing methods, we fabricate these high-performance polarizers and determine optimal experimental conditions for peak efficacy. Our research includes comprehensive Scanning Electron Microscopy analyses to understand the microstructural properties of these azo-dye thin film polarizers, contributing significantly to the field of advanced display technologies.

关键词： Azo-dye polarizer photoalignment liquid crystal display

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Hypergraph Consistency Learning with Relational Distillation

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IEEE Transactions on Multimedia 2025年

作者： Yi, Siyu Mao, Zhengyang Wang, Yifan Gu, Yiyang Xiao, Zhiping Chen, Chong Hua, Xian-Sheng Zhang, Ming Ju, Wei Sichuan University College of Mathematics Chengdu610065 China Peking University-Anker Embodied AI Lab Peking University School of Computer Science National Key Laboratory for Multimedia Information Processing Beijing100871 China University of International Business and Economics School of Information Technology & Management Beijing100029 China University of Washington Department of Computer Science Seattle United States Terminus Group Beijing100027 China Sichuan University College of Computer Science Chengdu610065 China

This paper studies the problem of semi-supervised learning on graphs, which has recently aroused widespread interest in relational data mining. The focal point of exploration in this area has been the utilization of graph neural networks (GNNs), which stand out for excellent performance. Previous methods, however, typically rely on the limited labeled data while ignoring the abundant structural information in unlabeled nodes inherently on graphs, easily resulting in overfitting, especially in scenarios where only a few label nodes are available. Even worse, GNNs, despite their success, are constrained by their ability to solely capture local neighborhood information through message-passing mechanisms, thereby falling short in modeling higher-order dependencies among nodes. To circumvent the above drawbacks, we propose a simple yet effective framework called Hypergraph COnsistency LeArning (HOLA). Specifically, we employ a collaborative distillation framework consisting of a teacher network and a student network. To achieve effective interaction, we propose momentum distillation, a self-training method that enables the student network to learn from pseudo-targets generated by a momentum teacher network. Further, a novel hypergraph structure learning network is developed to model complex high-order relations among nodes with relational consistency learning, thereby transferring the knowledge to the student network. Extensive experiments conducted on a variety of benchmark datasets demonstrate the superior performance of the HOLA over various state-of-the-art methods. © 2025 IEEE.

关键词： Self-supervised learning

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Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

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IEEE Internet of Things Journal 2025年第12期12卷 20406-20419页

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

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"re-ID BUFF": An enhanced similarity measurement based on genetic programming for person re-identification 21

"re-ID BUFF": An enhanced similarity measurement based on ge...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Li, Yiming Shang, Lin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450383516

Person re-identification (re-ID) is a fundamental link to ensure successful cross-camera tracking. Its overall process is divided into two stages, feature extraction and matching. In the matching stage, the simple Euclid measurement has limitations in score due to the lack of consideration of the vector directions. The majority of recent contributions focus on the use of neighborhood information of the query image, which makes the calculation process more complex. In addition, these similarity measurements are predefined and therefore evaluated. In this paper, we propose a re-ID similarity measurement based on Genetic Programming (GP). The similarity measurement formula is automatically evolved and constructed through the optimization using specific function set, terminal set and fitness function targeting re-ID. For the training process of GP, we propose the feature triplet-dataset like the triplet-loss based on the existing re-ID datasets. We conduct sufficient experiments on three benchmark datasets, comparing with features of different quality and various measurements. The proposed method has a better effect than the Euclid measurement and achieves a general improvement on the mAP and rank-1 of combination with other metrics. Therefore, our method can be used as a gain "buff"to enhance the score of other methods on the original basis. © 2021 Owner/Author.

关键词： Genetic programming

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Summer School on Circular Economy for Sustainable Manufacturing: A Case Study and Lessons Learned 31

Summer School on Circular Economy for Sustainable Manufactur...

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31st CIRP Conference on Life Cycle Engineering, LCE 2024

作者： Rejeb, Helmi Ben Muxika, Eñaut Ghadimi, Pezhman Univ. Grenoble Alpes CNRS Grenoble INP G-SCOP Grenoble France Mondragon University Electronics & Computer Science Department - Signal Theory & Communications Arrasate Spain Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Belfield Dublin Ireland

The Circular Economy Action Plan is the main building block of the European Green Deal, Europe's new agenda for sustainable growth. Businesses need new technologies, new methods and new industrial organization either to create new businesses or to transform their products, processes and business models. They also need new talents to innovate, create businesses and new jobs, and transform society into a more sustainable one. Circular economy has grown significantly in recent years as a topic taught in engineering curricula. The purpose of this paper is to present the design, implementation and evaluation of a summer school program dedicated to the development, transformation and implementation of circular economy systems and new business models in manufacturing. The program was designed in collaboration with three university partners during a European project and relied on contributions from industrial companies, including start-ups and innovative companies. At the end, some lessons are drawn in order to show the main added value based on the evaluation of this summer school and to identify recommendations for the design and implementation of similar summer schools. © 2024 The Author(s).

关键词： Engineering education

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