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IEEE Conference on Decision and Control

作者： Jingxuan Zhu Alvaro Velasquez Ji Liu Department of Applied Mathematics and Statistics at Stony Brook University Department of Computer Science at University of Colorado Boulder Department of Electrical and Computer Engineering at Stony Brook University

This paper presents a resilient distributed algorithm for solving a system of linear algebraic equations over a multi-agent network in the presence of Byzantine agents capable of arbitrarily introducing untrustworthy information in communication. It is shown that the algorithm causes all non-Byzantine agents' states to converge to the same least squares solution exponentially fast, provided appropriate levels of graph redundancy and objective redundancy are established. An explicit convergence rate is also provided.

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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International Conference on Wireless and Telematics (ICWT)

作者： Eki Ahmad Zaki Hamidi Arga Valen Lazuardi Muhammad Litfan Rahmansyah Salman Alfarizi Novel Bajri Rohmat Tulloh Ridha Muldina Negara Syaiful Ahdan Jupriyadi Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Telecommunication Technology School of Applied Science Telkom University Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions.

关键词： Performance evaluation 5G mobile communication Network topology Emulation computer architecture Quality of service Aerodynamics

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Near-Field Mobile Tracking: A Framework of Using XL-RIS Information

arXiv

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arXiv 2024年

作者： Wu, Tuo Pan, Cunhua Zhi, Kangda Yao, Junteng Ren, Hong Elkashlan, Maged Yuen, Chau The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

This paper introduces a novel mobile tracking framework leveraging the high-dimensional signal received from extremely large-scale (XL) reconfigurable intelligent surfaces (RIS). This received signal, named XL-RIS information, has a much larger data dimension and therefore offers a richer feature set compared to the traditional base station (BS) received signal, i.e., BS information, enabling more accurate tracking of mobile users (MUs). As the first step, we present an XL-RIS information reconstruction (XL-RIS-IR) algorithm to reconstruct the high-dimensional XL-RIS information from the low-dimensional BS information. Building on this, this paper proposes a comprehensive framework for mobile tracking, consisting of a Feature Extraction Module and a Mobile Tracking Module. The Feature Extraction Module incorporates a convolutional neural network (CNN) extractor for spatial features, a time and frequency (T&F) extractor for domain features, and a near-field angles of arrival (AoAs) extractor for capturing AoA features within the XL-RIS. These features are combined into a comprehensive feature vector, forming a time-varying sequence fed into the Mobile Tracking Module, which employs an Auto-encoder (AE) with a stacked bidirectional long short-term memory (Bi-LSTM) encoder and a standard LSTM decoder to predict MUs’ positions in the upcoming time slot. Simulation results confirm that the tracking accuracy of our proposed framework is significantly enhanced by using reconstructed XL-RIS information and exhibits substantial robustness to signal-to-noise ratio (SNR) variations. © 2024, CC BY.

关键词： Convolutional neural networks

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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A Design of an Effective Methodology to Detect Glaucoma Disease using Intelligent Learning Strategy 9

A Design of an Effective Methodology to Detect Glaucoma Dise...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Shobana, D. Akilarasu, G. Sangeetha, J. Priya, V. Banu Karkuzhali, S. Malathi, P. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Lovely Professional University Department of Computer Science and Engineering Punjab India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Globally, glaucoma is now among the leading causes of blindness. It is a chronic, irreversible illness. Screening for glaucoma and detecting it early are crucial in the fight against this illness. Here, deep learning shows great promise. Two deep learning architectures, the Convolutional Neural Network (CNN) model and the Improved Learning Strategy to Predict Glaucoma (ILSPG) model, have been evaluated in this article. In order to diagnose glaucoma, the DCNN method is utilized. Ultimately, this area of study aims to help doctors diagnose glaucoma by developing methods to spot the disease in retinal fundus pictures. Because glaucoma can have both beneficial and bad effects on the model, the result of the model might be good or bad. Several measures are utilized in the process of evaluating models, including accuracy, precision, specificity, and reliability. In order to determine the accuracy of the proposed model, an additional comparison study is conducted. The results are contrasted with a deep learning-based classification system that uses convolutional neural networks. The results demonstrate, without a doubt, that the proposed system is effective. © 2023 IEEE.

关键词： Convolutional neural networks

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MEMEGRAPHS: LINKING MEMES TO KNOWLEDGE GRAPHS

arXiv

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arXiv 2023年

作者： Kougia, Vasiliki Fetzel, Simon Kirchmair, Thomas Çano, Erion Baharlou, Sina Moayed Sharifzadeh, Sahand Roth, Benjamin University of Vienna Faculty of computer science Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Boston University Department of Electrical and Computer Engineering BostonMA United States Ludwig Maximilians University of Munich Faculty of Computer Science Munich Germany

Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes. © 2023, CC BY.

关键词： Knowledge graph

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Enhancing the detection capabilities of nano-avalanches via data-driven classification of acoustic emission signals

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Physical Review E 2023年第4期108卷 045001-045001页

作者： Emil Bronstein Jonathan Zimmerman Eugen Rabkin Eilon Faran Ronen Talmon Doron Shilo Faculty of Mechanical Engineering Technion–Israel Institute of Technology 3200003 Haifa Israel Department of Materials Science and Engineering Technion–Israel Institute of Technology 3200003 Haifa Israel Viterbi Faculty of Electrical and Computer Engineering Technion–Israel Institute of Technology 3200003 Haifa Israel

Acoustic emission (AE) is a powerful experimental method for studying discrete and impulsive events termed avalanches that occur in a wide variety of materials and physical phenomena. A particular challenge is the detection of small-scale avalanches, whose associated acoustic signals are at the noise level of the experimental setup. The conventional detection approach is based on setting a threshold significantly larger than this level, ignoring “false” events with low AE amplitudes that originate from noise. At the same time, this approach overlooks small-scale events that might be true and impedes the investigation of avalanches occurring at the nanoscale, constituting the natural response of many nanoparticles and nanostructured materials. In this work, we develop a data-driven method that allows the detection of small-scale AE events, which is based on two propositions. The first includes a modification of the experimental conditions by setting a lower threshold compared to the conventional threshold, such that an abundance of small-scale events with low amplitudes are considered. Second, instead of analyzing several conventional scalar features (e.g., amplitude, duration, energy), we consider the entire waveform of each AE event and obtain an informative representation using dynamic mode decomposition. We apply the developed method to AE signals measured during the compression of platinum nanoparticles and demonstrate a significant enhancement of the detection range toward small-scale events that are below the conventional threshold.

关键词： Acoustic measurements Acoustic signal processing & noise Disclinations & dislocations Plasticity Nanoparticles Acoustic techniques

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm with Particle Swarm Optimization K-Means for Banking Data

Performance Comparison of the Invasive Weed Optimization K-M...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ni Luh Gede Pivin Suwirmayanti I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Emy Setyaningsih Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset.

关键词： Accuracy Consumer behavior Metaheuristics Clustering algorithms Banking Computational efficiency Particle swarm optimization Intelligent systems Cybernetics Convergence

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DiCTI: Diffusion-based Clothing Designer via Text-guided Input

arXiv

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arXiv 2024年

作者： Lampe, Ajda Stopar, Julija Jain, Deepak K. Omachi, Shinichiro Peer, Peter Štruc, Vitomir University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Dalian University of Technology China Tohoku University Graduate School of Engineering Sendai Japan

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study. The source code of DiCTI will be made publicly available. © 2024, CC BY-NC-SA.

关键词： Semantics

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Deeper or wider: a perspective from optimal generalization error with Sobolev loss 24

Deeper or wider: a perspective from optimal generalization e...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yahong Yang Juncai He Department of Mathematics The Pennsylvania State University University Park State College PA Computer Electrical and Mathematical Science and Engineering Division The King Abdullah University of Science and Technology Thuwal Saudi Arabia

Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deeper or wider remains a persistent question. This paper explores a comparison between deeper neural networks (DeNNs) with a flexible number of layers and wider neural networks (WeNNs) with limited hidden layers, focusing on their optimal generalization error in Sobolev losses. Analytical investigations reveal that the architecture of a neural network can be significantly influenced by various factors, including the number of sample points, parameters within the neural networks, and the regularity of the loss function. Specifically, a higher number of parameters tends to favor WeNNs, while an increased number of sample points and greater regularity in the loss function lean towards the adoption of DeNNs. We ultimately apply this theory to address partial differential equations using deep Ritz and physics-informed neural network (PINN) methods, guiding the design of neural networks.

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