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International Conference on informatics and Computing (ICIC)

作者： Rizky Adi Bassamtiano Renaufalgi Irnawan Jiyi Li Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia Department of Computer Science and Engineering University of Yamanashi Kofu Japan

Product reviews are an integral part of ecommerce. By Q2 2020 alone, the average of e-commerce visitors in Indonesia almost reached 400 million. This vast amount of traffic resulting a massive amount of product review information. Product reviews in Indonesia are usually paired with a rating system from one to five based on user satisfaction with the product. Mostly, the number of reviews in each rating is unbalanced. Ratings 5, 4, and 1 are usually more frequent than ratings 2 and 3. This imbalanced data makes classification challenging to categorize the review rating, leading to inaccurate predictions for the rating with fewer data than the others. Previous research used various approaches for classifying review ratings, such as machine learning and fine-tuning a state-of-the-art deep learning model. In this research, we propose a new approach that leverages multiple-word embedding combined with CNN to deal with the highly unbalanced dataset problem. By leveraging multiple-word embedding techniques, the model can extract more features from the review text. We also proposed an extended pipeline to enhance the model performance. The proposed model and pipeline perform better than the fine-tuned Indonesian BERT-based and RoBERTa-based pre-trained models baseline with 0.93 accuracy and 0.82 F1-Macro scores.

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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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Smart Routing System in Wireless Sensor Networks

Smart Routing System in Wireless Sensor Networks

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Joshi, Kapil Khurana, Virender Sivarama Krishna, Ch. Azam, Mohammed Singh, Yashpal Johari, Lalit Uttaranchal University UIT India Rohtak Department of Computer Science India Department of Electronics and Communication Engineering Andhra Pradesh Mylavaram521230 India ISL College of Engineering Electronic and Communication Engineering Chandrayangutta Telangana Hyderabad India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India IFTM University Department of Computer Applications Moradabad India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The continued increase in greenhouse gas levels causes glaciers to melt, sea levels to rise, and rain forests to die. We must minimise our usage of fossil fuels in order to reduce greenhouse gas emissions. Electric cars' energy storage systems play a crucial part in this scenario, allowing them to exchange energy with the grid or with the house, depending on the power need. The control framework of a bi-directional wireless charger for an electric car connected to the smart home grid is the subject of this research. Furthermore, it addresses an electric vehicle's charging and discharging control infrastructure under various loading scenarios. The control structure is based on phase synchronisation between active H-bridge primary and secondary converters, with phase shift technology doing the actual control. Finally, a method for achieving optimum power flow and efficiency while still satisfying the smart house criterion is explored. © 2022 IEEE.

关键词： Electric power transmission networks

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MiliPoint: a point cloud dataset for mmWave radar 23

MiliPoint: a point cloud dataset for mmWave radar

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Han Cui Shu Zhong Jiacheng Wu Zichao Shen Naim Dahnoun Yiren Zhao School of Computer Science Electrical and Electronic Engineering and Engineering Maths University of Bristol Department of Computer Science University College London Department of Electrical and Electronic Engineering Imperial College London

Millimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency (RF) based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors?To answer this question, our work, termed MiliPoint2, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development. Available at https://***/yizzfz/MiliPoint/

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Validation of a heat detection system in different dairy management conditions 10

Validation of a heat detection system in different dairy man...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Kosanovic, M. Stojanovic, D. Laporte-Uribe, J.A. Datamars SA Lamone Switzerland Computer Science Department University of Nis Faculty of Electronic Engineering Nis Serbia

ISBN: (纸本)9788396536006

The automated activity monitoring (AAM) system for heat detection is an example of how animal sensor technologies and machine learning can improve heat detection while reducing labour costs. Nevertheless, customizing a heat detection algorithm to fit different management conditions is challenging. A new machine learning technique based on neural networks developed by Datamars SA seems to solve this issue. To develop this algorithm, a training dataset and heat labels were extracted from cattle managed in indoor and grazing conditions. Milk pro gesterone was used as a gold standard for developing the heat detection algorithm. The trained algorithm achieved an F1 score of 83%. The evaluation and commercial validation of the trained algorithm were performed across different farms in Europe, South America, and Oceania using a combination of farmer visual observation and a commercial AMM system as references. Finally, the proposed system was compared to a known commercial system (HerdInsight). The results showed that the final Datamars system outperformed the existing AMM system in both indoor and grazing conditions and achieved F1 scores greater than 90%. The results confirmed that the developed system is well suited to automatically detect heat (oestrus) events. © ECPLF 2022. All rights reserved.

关键词： Dairies

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Impact of Inter-Operator Interference via Reconfigurable Intelligent Surfaces

arXiv

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arXiv 2024年

作者： Miridakis, Nikolaos I. Tsiftsis, Theodoros A. Karkazis, Panagiotis A. Leligou, Helen C. Popovski, Petar the Department of Informatics and Computer Engineering University of West Attica Aegaleo12243 Greece the Department of Informatics and Telecommunications University of Thessaly Lamia35100 Greece the Department of Industrial Design and Production Engineering University of West Attica Aegaleo12241 Greece the Department of Electronic Systems Aalborg University Aalborg9220 Denmark

A wireless communication system is studied that operates in the presence of multiple reconfigurable intelligent surfaces (RISs). In particular, a multi-operator environment is considered where each operator utilizes an RIS to enhance its communication quality. Although out-of-band interference does not exist (since each operator uses isolated spectrum resources), RISs controlled by different operators do affect the system performance of one another due to the inherently rapid phase shift adjustments that occur on an independent basis. The system performance of such a communication scenario is analytically studied for the practical case where discrete-only phase shifts occur at RIS. The proposed framework is quite general since it is valid under arbitrary channel fading conditions as well as the presence (or not) of the transceiver’s direct link. Finally, the derived analytical results are verified via numerical and simulation trial as well as some novel and useful engineering outcomes are manifested. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio transceivers

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A Comparative Study on Effect of Activation Function Placement in Neural Network Architecture for Regression Problems 7

A Comparative Study on Effect of Activation Function Placeme...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Srivastava, Amrita Panda, Sashmita Jose, Justin Reddy, Karapureddy Jaswanth Nadella, Sree Teja Bhatia, Vimal Pandey, Sharad K. Mits Department of Electronics Engineering Gwalior474005 India Northwestern University Department of Neurobiology Evanston United States Nit Department of Electronics and Communication Engineering Durgapur713209 India Nit Department of Computer Science and Engineering Durgapur713209 India Iit Department of Electrical Engineering Indore453552 India Iit Centre for Advanced Electronics Indore453552 India Soochow University School of Electronic and Information Engineering China Wineural Technologies Private Limited India

ISBN: (纸本)9798350305173

In recent studies, a modified artificial neural network architecture, where the activation function is placed before the weighted sum, has shown promising results for classification problems like XOR. However, it remains unclear whether the same modified architecture will give better results than conventional architecture for regression problems or not. In this paper, we study the same and compare the modified and the conventional architecture on the basis of mean square error (MSE), R2, and mean absolute error (MAE). From simulations, it is revealed that contrary to the XOR classification problem, there is no significant improvement in the MSE with the modified architecture compared to the conventional architecture. Thus, this study contributes to gain a better understanding of the modified neural network architecture, discussing its limitations and applications. © 2023 IEEE.

关键词： Mean square error

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Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach

arXiv

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arXiv 2024年

作者： Bian, Xinyu Mao, Yuyi Zhang, Jun Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong

Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures. While most endeavours assume perfect synchronization among users and the base station, this paper investigates asynchronous grantfree massive RA, and develop efficient algorithms for joint user activity detection, synchronization delay detection, and channel estimation. Considering the sparsity on user activity, we formulate a sparse signal recovery problem and propose to utilize the framework of orthogonal approximate message passing (OAMP) to deal with the non-independent and identically distributed (i.i.d.) Gaussian pilot matrices caused by the synchronization delays. In particular, an OAMP-based algorithm is developed to fully harness the common sparsity among received pilot signals from multiple base station antennas. To reduce the computational complexity, we further propose a free probability AMP (FPAMP)-based algorithm, which exploits the rectangular free cumulants to make the cost-effective AMP framework compatible to general pilot matrices. Simulation results demonstrate that the two proposed algorithms outperform various baselines, and the FPAMP-based algorithm reduces 40% of the computations while maintaining comparable detection/estimation accuracy with the OAMP-based algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

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Modeling Parking Occupancy Using Algorithm of 3D Visibility Network

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IEEE Access 2025年 13卷 84424-84434页

作者： Matej Babič Branko Šter Ladislav Hluchý Giuliana Baiamonte Michele Calì Faculty of Information Studies Novo Mesto Slovenia Faculty of Computer and Informatics University of Ljubljana Ljubljana Slovenia Slovak Academy of Sciences Institute of Informatics Bratislava Slovakia Electric Electronics and Computer Engineering Department University of Catania Catania Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones.

关键词： Sensors Magnetic sensors Three-dimensional displays Wireless sensor networks Meteorology Computational modeling Automata Path planning Urban areas Sensor phenomena and characterization

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