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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Badieah Sam Farisa Chaerul Haviana Sri Mulyono Channa Abdullah Salim Muhammad Fatih Adli Moh Taufik Febriansah Wiktasari Noor Suhana Sulaiman Azliza Yaqob Informatics Engineering Department Universitas Islam Sultan Agung Semarang Indonesia Computer Engineering Technology Politeknik Negeri Semarang Semarang Indonesia Faculty of Computer Media and Technology Management University College of TATI Terengganu Malaysia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model.

关键词： computer science Biological system modeling Computational modeling Semantics computer architecture Transformers Data models Encoding Recommender systems Context modeling

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Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback

arXiv

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arXiv 2024年

作者： Makridis, Evagoras Grammenos, Andreas Oliva, Gabriele Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Department of Electrical and Computer Engineering School of Engineering University of Cyprus Aglantzia Nicosia2109 Cyprus Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo Roma21–00128 Italy

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology. © 2024, CC BY.

关键词： Consensus algorithm

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Decentralized Federated Deep Reinforcement Learning Framework for Energy-Efficient Base Station Switching Control

Decentralized Federated Deep Reinforcement Learning Framewor...

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International Symposium on Telecommunications, IST

作者： Narges Movahedkor Reza Shahbazian Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service ($\mathbf{Q o S}$) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods.

关键词： Training Base stations Energy consumption Privacy 5G mobile communication Federated learning Switches Quality of service Deep reinforcement learning Telecommunications

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Implementation of Convolutional Neural Network with EfficientNet-B0 Architecture for Brain Tumor Classification

Implementation of Convolutional Neural Network with Efficien...

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International Conference on informatics and Computing (ICIC)

作者： Vinsensius Haryo Bhaskoro Hadi Achmad Benny Mutiara Rina Refianti Informatics Engineering Gunadarma University Depok Indonesia Computer Science and Information Technology Gunadarma University Depok Indonesia

Brain tumor is a type of cancer that is difficult to detect in the early stages and can be a fatal disease. Early detection of brain tumors is crucial to increase the chances of successful treatment. The use of imaging technologies such as CT-Scan and MRI has helped in detecting brain tumors, but accurate and fast diagnosis remains a challenge. In this study, the Convolutional Neural Network (CNN) method was used as an alternative method to detect brain tumors faster and more accurately. Some CNN architectures used include VGGNet, InceptionResNet, and the latest architecture called EfficientNet-B0. This research aims to compare the performance of EfficientNet-B0 with other CNN architectures in classifying brain tumors on MRI images. The results of this study are expected to provide an understanding of the effectiveness of using EfficientNet-B0 compared to other architectures in classifying brain tumors on MRI images. The dataset used consists of 3264 images with glioma, normal, pituitary, and meningioma classes. Testing was conducted with several scenarios for epochs and testing against optimizers such as Adam and RMSProp. The results from testing the model using confusion matrix showed that in experiments with an epoch scenario of 25 and Optimizer obtained an average precision value reaching 98%, average recall reaching 98%, and average F1-Score reaching 98%. The implementation of the best-designed model in the form of a system successfully predicted types of glioma, normal, pituitary, and meningioma tumors. Additionally, the classification model will be implemented into a website using Python programming language along with Flask framework.

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Gain Enhancement of a 5G mmWave 2×2 Antenna Array Using a 3D Printed Hemispherical Lens

Gain Enhancement of a 5G mmWave 2×2 Antenna Array Using a 3...

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Antennas and Propagation Society International Symposium

作者： Adnan Nadeem David Chatzichristodoulou Nosherwan Shoaib Photos Vryonides Symeon Nikolaou School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus Electrical Engineering Department Frederick University Nicosia Cyprus

The manuscript presents a 5G mm-Wave 2×2 patch antennas array with high gain that is further enhanced using a 3D printed hemispherical homogenous lens. The array operates at 27 GHz, and it is fed by a microstrip lines network, using proximity coupling. The gain on the compact 2×2 patch antennas array is 13.8 dBi and the addition of a hemispherical 3D printed lens enhances the gain by more than 9 dB. For the lens implementation and integration with the antenna array, additive manufacturing in used. The support structure and the lens itself can be 3D printed using the low permittivity (2.2) Zetamix filament. Using mm wave frequencies allows the use of relatively small size lens. For the presented case the radius of the hemispherical lens is 33 mm, and the lens' flat surface is suspended 26 mm over the patch antennas' layer. The combination of the lens with the 2×2 patch antennas array results in total realized gain of 23.2 dBi.

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SparkReact: A Novel and User-friendly Graphical Interface for the Apache Spark MLlib Library 22

SparkReact: A Novel and User-friendly Graphical Interface fo...

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Proceedings of the 26th Pan-Hellenic Conference on informatics

作者： Aristeidis Karras Christos Karras Agorakis Bompotas Panagiotis Bouras Leonidas Theodorakopoulos Spyros Sioutas Department of Computer Engineering and Informatics University of Patras Greece Department of Management Science and Technology University of Patras Greece

ISBN: (纸本)9781450398541

Visualization is a critical component across every software as it enables users to familiarize themselves with the environment and perform certain tasks with ease. Therefore, straightforward yet interactive and easy-to-understand tools let users’ complex demands be satisfied within minutes. The objective of this work is to give an optimized graphical user interface for the Apache Spark MLlib library to apply machine learning algorithms quickly, conveniently, and effectively. We introduce SparkReact, a responsive graphical user interface that allows users to apply clustering, classification, and regression techniques within just a few mouse clicks by implementing and evaluating a certain algorithm and pre-building the code ready for import to Spark. To evaluate the usefulness of our tool we performed crowdsourcing to two categories, computer experts and ordinary users. The results indicate that both populations were satisfied with the tool at a surprising 98 percent. As per the time required to construct and evaluate a machine learning model, it took approximately 4 minutes using SparkReact while with ordinary methods it took almost 4 times longer. Ultimately, future extensions will seek to provide more algorithmic choices.

关键词： Regression Classification Graphical User Interface (GUI) Apache Spark Clustering ReactJS Visualization Machine Learning

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Digital Twin for Grey Box Modeling of Multistory Residential Building Thermal Dynamics

Digital Twin for Grey Box Modeling of Multistory Residential...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Lina Morkunaite Justas Kardoka Darius Pupeikis Paris Fokaides Vangelis Angelakis Faculty of Civil Engineering and Architecture Kaunas University of Technology Kaunas Lithuania Faculty of Informatics Kaunas University of Technology Kaunas Lithuania School of Engineering Frederick University Nicosia Cyprus Department of Science and Technology Linkoping University Norrkoping Sweden

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Buildings' energy efficiency is a widely researched topic, which is rapidly gaining popularity due to rising environmental concerns and the need for energy independence. In Northern Europe heating energy alone accounts for up to 70% of the total building energy consumption. Industry 4.0 technologies such as IoT, big data, cloud computing and machine learning, along with the creation of predictive and proactive digital twins, can help to reduce this number. However, buildings' thermal dynamics is a very complex process that depends on many variables. As a result, commonly used physics-based white box models are time-consuming and require vast expertise. On the contrary, black box forecasting models, which rely primarily on building energy consumption data, lack fundamental insights and hinder re-use. In this study we propose an architecture to facilitate grey box modeling of building's thermal dynamics while integrating real time IoT data with 3D representation of buildings. The architecture is validated in a case study creating a digital twin platform that enables users to define the thermal dynamics of buildings based on physical laws and real data, thus facilitating informed decision making for the best heating energy optimization strategy. Also, the created user interface enables stakeholders such as facility managers, energy providers or governing bodies to analyze, compare and evaluate buildings' thermal dynamics without extensive expertise or time resources.

关键词： Solid modeling Analytical models Energy consumption Three-dimensional displays Atmospheric modeling Computational modeling Architecture

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Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

arXiv

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arXiv 2022年

作者： Cheng, Lei Yin, Feng Theodoridis, Sergios Chatzis, Sotirios Chang, Tsung-Hui College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electronics Systems Aalborg University Denmark Department of Electrical & Computer Engineering and Informatics Cyprus University of Technology Cyprus Cyprus

Sparse modeling for signal processing and machine learning, in general, has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning comprises two major paths paved by: a) discriminative methods that establish direct input-output mapping based on a regularized cost function optimization, and b) generative methods that learn the underlying distributions. The latter, more widely known as Bayesian methods, enable uncertainty evaluation with respect to the performed predictions. Furthermore, they can better exploit related prior information and also, in principle, can naturally introduce robustness into the model, due to their unique capacity to marginalize out uncertainties related to the parameter estimates. Moreover, hyper-parameters (tuning parameters) associated with the adopted priors, which correspond to cost function regularizers, can be learnt via the training data and not via costly cross-validation techniques, which is, in general, the case with the discriminative methods. To implement sparsity-aware learning, the crucial point lies in the choice of the function regularizer for discriminative methods and the choice of the prior distribution for Bayesian learning. Over the last decade or so, due to the intense research on deep learning, emphasis has been put on discriminative techniques. However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models, such as Gaussian processes, and, also, inspire new paths for unsupervised learning, such as Bayesian tensor decomposition. The goal of this article is two-fold. First, to review, in a unified way, some recent advances in incorporating sparsity-promoting priors into three highly popular data modeling/analysis tools, namely deep neural networks, Gaussian processes, and tensor decomposition. Second, to review their associated inference techniques from differ

关键词： Bayesian networks

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziru Chen Zhou Ni Peiyuan Guan Lu Wang Lin X. Cai Morteza Hashemi Zongzhi Li Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA Department of Electrical Engineering and Computer Science University of Kansas Lawrence USA Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms—Federated Vehicular Network, NOMA

关键词： Wireless communication NOMA Data privacy Federated learning Simulation Power control Road safety Real-time systems Resource management Vehicles

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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