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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Hana Patricia Mawarnie Widodo Murien Nugraheni Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia Dept. of Information System and Technology Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%.

关键词： Support vector machines Sentiment analysis Accuracy Social networking (online) Blogs Machine learning Polynomials Internet of Things Kernel Robots

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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Sustainable AI & Agricultural Technologies 4

Sustainable AI & Agricultural Technologies

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4th IEEE International Conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Sarkadi, Stefan Moraru, Ionut Manning, Louise King's College London Dept. of Informatics London United Kingdom School of Computer Science University of Lincoln Lincoln United Kingdom Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798350337464

Climate change, increase in world population and the war in Ukraine have led nations to put a larger focus on food security, while simultaneously trying to halt declines in biodiversity and reduce risks to human health posed by chemically-reliant farming practices. Achieving these goals simultaneously will require the adoption of novel approaches. One potential solution is accelerating the uptake of AI & Robotic Technologies in the agricultural sector. However, we currently do not have a scientific model for predicting the effects of adopting AI-based agri-tech in complex systems such as the food sector. Self-organising systems offer a holistic and intuitive perspective on complex ecosystems and their resilience to socio-economic pressures at various scales. We propose the application of evolutionary agent-based modelling to explore the emergence of strategies in the food sector considering various levels of abstraction. © 2023 IEEE.

关键词： Biodiversity

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Yonghwan Kim Yoshiaki Katayama Koichi Wada Dept. of Computer Science Nagoya Institute of Technology Nagoya Japan Dept. of Applied Informatics Hosei University Tokyo Japan

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot’s capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors.

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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General Concepts in Swarm of Drones Control: Analysis and Implementation

General Concepts in Swarm of Drones Control: Analysis and Im...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Marek, Dariusz Paszkuta, Marcin Szygula, Jakub Biernacki, Piotr Domanski, Adam Szczygiel, Marta Krol, Marcel Wojciechowski, Konrad Silesian University of Technology Dept. of Distributed Systems and Informatic Devices Warsaw Poland Polish-Japanese Academy of Information Technology ul. Koszykowa 86 Warsaw02-008 Poland Silesian University of Technology Dept. of Computer Graphics Vision and Digital Systems Warsaw Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Warsaw Poland Silesian University of Technology Warsaw Poland

ISBN: (纸本)9798350324457

In the presented paper, detailed schematics and descriptions concerning communication in the context of swarm drone control are introduced. Methods and technologies behind them are discussed. The implementation of the presented concept was verified through a series of tests. Simulation results which confirm the effectiveness and efficiency of the proposed solutions are presented. The obtained results prove the correctness implementation of the presented methods and also demonstrate the benefits derived from the proposed approach. The presented concept of controlling a swarm of drones represents the current state of knowledge and technology in the field of drones and their control. By utilizing advanced communication technologies, positioning, and analysis of communication structures, this work makes a significant contribution to the UAV (Unmanned Aerial Vehicle) field. © 2023 IEEE.

关键词： Control Concepts Industrial Environment Industry 4.0 simulation environments Swarm of Drones UAV

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Development of a teaching model based on lifelong learning through active methodological strategies 12

Development of a teaching model based on lifelong learning t...

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12th International Conference on Virtual Campus, JICV 2022

作者： Trujillo-Aguilera, F D. Blazquez-Parra, E.B. Almendros, J. Calderon Marti-Campoy, A. Plaza-Garcia, I. University of Malaga Dept. Electronic Technology Malaga Spain University of Malaga Dept. Graphic Engineering Design and Projects Malaga Spain Polytechnic University of Valencia Dept. of Computer and Systems Informatics Valencia Spain University of Zaragoza Dept. of Electronic Engineering and Communications Zaragoza Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in which a set of active teaching activities have been implemented for the acquisition of key competences in the challenge of achieving one of the most important goals that any educational system must provide: lifelong learning, thereby facilitating the incorporation of new university graduates into the world of work. This project has been implemented on the subject of Power Electronics. However, to facilitate the transfer to other subjects, the catalogue of active teaching activities is sufficiently varied to make it easy to extrapolate and use in other subjects, thanks to the fact that teachers have a wide variety of activities to choose from the set. Finally, a survey has been drawn to evaluate this new active methodology's satisfaction, impact and improvement to know the students' perspectives once they have worked with this catalogue of activities. © 2022 IEEE.

关键词： E-learning

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Augmented Reality-Based Navigation: Bridging Indoor and Outdoor Environments through Immersal Integration

Augmented Reality-Based Navigation: Bridging Indoor and Outd...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Zacky Maulana Achmad Sritrusta Sukaridhoto Muhammad Agus Zainuddin Aurent Savila Ardian Firmansyah Septa Mega Saputra Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Multimedia Creative Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

A navigation system is an essential tool designed to assist users in determining and following a route from one location to another. Navigation systems are typically categorized into two types: outdoor navigation systems designed for open areas and indoor navigation systems used within enclosed spaces. However, transitioning between indoor and outdoor environments has limitations, often requiring users to switch applications, such as using GPS for outdoor navigation and a different application for indoor navigation. Therefore, we propose the integration of indoor and outdoor navigation based on Augmented Reality. The development of AR navigation system begins with the creation of 3D assets of the Politeknik Elektronika Negeri Surabaya (PENS) campus, which is the site of our research including three buildings for indoor navigation and the connecting roads between the buildings for outdoor navigation. The development of this navigation system uses the Immersal SDK as a library for Spatial Mapping, Localization and System Integration. Several features are included, such as indoor-outdoor navigation, multilevel floor navigation, and zero additional devices. The system testing results are based on user testing, integration testing, and multilevel floor testing. From User Testing with PIECES Framework, 32 respondents expressed satisfaction with the proposed system. Integration Testing showed that the system could navigate between indoor and outdoor environments. And Multilevel Floor Testing demonstrated that the system could navigate within buildings with multiple floors.

关键词： Location awareness Three-dimensional displays Navigation Indoor navigation Buildings Switches Stairs Floors Augmented reality Testing

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network

Emotion Recognition from Video Frame Sequence using Face Mes...

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International Seminar on Intelligent technology and Its Applications (ISITIA)

作者： Derry Pramono Adi Eko Mulyanto Yuniarno Diah Puspito Wulandari Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe’s face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of “Anger,” “Fearful,” “Happy,” and “Sad.” For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of $\sim2.3$ seconds (achieved using the SqueezeNet model).

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