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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Slovakia

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead.

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DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-Optimal Deontic Logic Agents 37

DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Deb, Tonmoay Dix, Jürgen Jeong, Mingi Molinaro, Cristian Pugliese, Andrea Li, Alberto Quattrini Santos, Eugene Subrahmanian, V.S. Yang, Shanchieh Zhang, Youzhi Department of Computer Science Northwestern University EvanstonIL United States Department of Computer Science Dartmouth College HanoverNH United States Department of Informatics Modeling Electronics and System Engineering University of Calabria Rende Italy Department of Informatics Technical University of Clausthal Clausthal Germany Department of Computer Engineering Rochester Institute of Technology RochesterNY United States CAIR Hong Kong Institute of Science and Innovation Hong Kong

ISBN: (纸本)9781577358800

Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multiagent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defenses against them. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Terrorism

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Naufal, Muhammad Rizky Sari, Christy Atika Rachmawanto, Eko Hari Handoko, Lekso Budi Isinkaye, Folasade Olubusola Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Ekiti State University Department of Computer Science Ekiti Nigeria Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR. © 2023 IEEE.

关键词： Pixels

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Application of Convolution Neural Network for Plastics Waste Management Using TensorFlow 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Saputro, Immanuela Puspasari Sanger, Junaidy Budi Adrian, Angelia Melani Mokorisa, Gilberth Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Faculty of Engineering Universitas Katolik De La Salle Manado Manado95000 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Nowadays, plastic waste has become a global problem. Plastic waste can be found in the land, oceans, rivers, and even soil sediments. This problem has motivated various countries to overcome environmental pollution, especially plastic waste primarily through recycling programs. Plastic waste that is still good can be recycled into handicrafts or sold at the waste bank according to the current selling price. However, the waste management officer encountered difficulties classifying plastic waste since there are many forms of waste. Recently, a deep learning-based method, namely The Convolutional Neural Network (CNN), has been widely used for image processing tasks. In this study, we developed a mobile-based application by employing the CNN Algorithm to identify and classify plastic waste. The application will recognize and classify recyclable plastic waste such as plastic bags, plastic bottles, shampoo bottles, etc. There were 156 images collected manually by a smartphone camera and Google Image. All images are converted into jpg format with the size of 300 × 300 pixels. The dataset was divided into two parts, 106 images for data training and 50 images for data testing. From the experimental result, the model obtained an accuracy of 86%. Moreover, there are features to view price lists and tutorials for handicrafts from plastic waste. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep learning

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DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14258-14271页

作者： Ma, Wanli Karakus, Oktay Rosin, Paul L. Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cambridge Department of Engineering Cambridge United Kingdom

Semi-supervised learning (SSL) aims to help reduce the cost of the manual labelling process by leveraging a substantial pool of unlabelled data alongside a limited set of labelled data during the training phase. Since pixel-level manual labelling in large-scale remote sensing imagery is expensive and time consuming, semi-supervised learning has become a widely used solution to deal with this. However, the majority of existing SSL frameworks, especially various teacher-student frameworks, are too bulky to run efficiently on a GPU with limited memory. There is still a lack of lightweight SSL frameworks and efficient perturbation methods to promote the diversity of training samples and enhance the precision of pseudo labels during training. In order to fill this gap, we proposed a simple, lightweight, and efficient SSL architecture named DiverseHead, which promotes the utilisation of multiple decision heads instead of multiple whole networks. Another limitation of most existing SSL frameworks is the insufficient diversity of pseudo labels, as they rely on the same network architecture and fail to explore different structures for generating pseudo labels. To solve this issue, we propose DiverseModel to explore and analyse different networks in parallel for SSL to increase the diversity of pseudo labels. The two proposed methods, namely DiverseHead and DiverseModel, both achieve competitive semantic segmentation performance in four widely used remote sensing imagery datasets compared to state-of-the-art semi-supervised learning methods. Meanwhile, the proposed lightweight DiverseHead architecture can be easily applied to various state-of-the-art SSL methods while further improving their performance. The code is available at Here. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Machine Learning Technique for Classification of Internet Firewall Data Using RapidMiner 6

Machine Learning Technique for Classification of Internet Fi...

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6th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2022

作者： Naryanto, Rizqi Fitri Delimayanti, Mera Kartika Unviversitas Negeri Semarang Department of Mechanical Engineering Semarang Indonesia Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350320718

A firewall system is a type of security system that controls how data packets enter and leave a network. It does this to improve cyber defence and decide what to do with harmful packets. Traffic packets are checked against criteria to stop possible cyber threats from getting into the network. This study demonstrates the classification of internet firewall data using a public dataset containing 65,532 records and 11 features, and a unique machine-learning technique. No use has been made of any personally identifying information in the filtered data. The action characteristic is selected from among these features as the class label. The action class now supports the options 'allow,' 'deny,' 'drop,' and 'reset-both.' The study proposes an intelligent classification model that firewall systems can use to determine what to do with each received packet using a machine learning algorithm and the RapidMiner tool to look at a packet's properties. We classified the data using the Decision Tree (DT) and K-Nearest Neighbor (K-NN) methods. The highest accuracy was achieved using the Decision Tree model. © 2022 IEEE.

关键词： Classification (of information)

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Web-Based Movie Recommendation System using Content-Based Filtering and KNN Algorithm 9

Web-Based Movie Recommendation System using Content-Based Fi...

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9th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2022

作者： Delimayanti, Mera Kartika Laya, Mauldy Warsuta, Bambang Faydhurrahman, Mas Brillianesa Khairuddin, Mohammad Azri Ghoyati, Hubbaka Mardiyono, Anggi Naryanto, Rizqi Fitri Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Unviversitas Negeri Semarang Department of Mechanical Engineering Semarang Indonesia

ISBN: (数字)9781665471503

ISBN: (纸本)9781665471480

Improving the effectiveness of approaches has been a significant obstacle for recommended systems. It has also been crucial to address the trade-off between accuracy and calculation time when promoting goods via recommendation systems, as they must deliver accurate and real-time recommendations. This article discusses the development of a web-based system for recommending movies based on content-based filtering techniques. The system can recommend movies based on any combination of these two or more characteristics. This study has created a recommendation system based on genre preferences. To achieve this, content-based filtering based on genre connection is employed using cosine similarity. In order to improve the accuracy of this system, the KNN algorithm and the cosine similarity concept were implemented. The system employed a Movie Lens dataset, and the accuracy reached more than 87%. Moreover, this research successfully developed a user-friendly movie recommendation system (RS) with Python data analytic tools and the Django web framework. Moreover, this system can be accessed using a web browser and has more than 87% accuracy for giving movie recommendations based on some features or characteristics of the movie. © 2022 IEEE.

关键词： Recommender systems

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Towards Personalized Fitness with Multi-modal Wearable Heart Rate Monitors: A Proposal 6

Towards Personalized Fitness with Multi-modal Wearable Heart...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Nugraha, Alfian Dwi Puspitaningayu, Pradini Hidayatullah, Rahmat Pramukantoro, Eko Sakti Widayaka, Parama Diptya Rochmawati, Naim State University of Surabaya Dept. of Informatics Engineering Surabaya Indonesia State University of Surabaya Dept. of Electrical Engineering Surabaya Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9798350326642

Nowadays, the trend for home workouts is getting more and more popular. The growth of wearable fitness trackers from basic accelerometers, pedometers, pulse detectors, and ECGs also supports the advancement of personal fitness monitoring. Everyone has a distinct level of fitness, in theory. The heart rate, which is correlated with physical activity, such as sports, demonstrates how this organ functions. As the muscles work harder at a high intensity, the heart rate will also rise. This paper presents the design and implementation of a multimodal wearable heart rate monitoring system. The software is Android-based and is designed to be able to receive heart rate data sent by two different wearable heart rate monitors simultaneously using Bluetooth low-energy protocol. One uses an optical heart monitor and the other uses an electrocardiogram or ECG. The implementation also includes IoT for real-Time data acquisition and a web-based application for wider accessibility. The system is evaluated by collecting packets from two devices for 60 minutes. The results underscore the feasibility of the system in collecting users' heart rate data either using different devices. Furthermore, it serves as a foundation for future studies aiming to expand the personalization capabilities of wearable devices in healthcare and fitness. © 2023 IEEE.

关键词： Electrocardiograms

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FLoW-Vision: Object Recognition and Pose Estimation System based on Three-Dimensional (3D) computer Vision 23

FLoW-Vision: Object Recognition and Pose Estimation System b...

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23rd International Electronics Symposium, IES 2021

作者： Putra, Vardyansyah Cahya Pratama Harsetya Apriandy, Kevin Ilham Pramadihanto, Dadet Barakbah, Ali Ridho Dept. of Informatics and Computer Engineering Electronic Engineering Indonesia

ISBN: (纸本)9781665443463

This paper presents a three-dimensional computer vision-based object recognition on FLoW-Vision in RoISC (formerly ER2C) has entered its second phase. Previously, the robot had a basic vision that was used to replicate 'human-like' visual skills using 2D computer vision. As a result of the above discussion, we proposed the design and implementation of an object recognition and pose estimation system based on three-dimensional computer vision to handle object recognition and pose estimation tasks in real-world environments simultaneously. In the object recognition process, a point-cloud segmentation method is used to obtain possible object clusters before starting the calculation of feature description. Then, a keypoints-based two-stage matching process is performed to speed up the computation of finding correspondences between the object clusters of the current scene and a colored point cloud model of an object. Next, a Hough voting algorithm is employed to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Last process process the pose estimation from clustered object using RANSAC to search the largest surface as Z surface. Experimental validate the object recognition can work correctly with percentage 100% and pose estimation accuracy of the proposed system can work correctly with percentage 60% in a complex real-world scene. © 2021 IEEE.

关键词： Object detection

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Genera: A Serious Game Leveraging Blockchain Technology for Renewable Energy Education 14

Genera: A Serious Game Leveraging Blockchain Technology for ...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Stamatakis, Dimitris Kogias, Dimitrios G. Leligou, Helen C. Karkazis, Panagiotis A. Penalvo, Elisa Department of Industrial Design and Production Engineering University of West Attica Petrou Ralli Athens12243 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Athens12210 Greece Institute of Energy Engineering U. Politecnica de Valencia Spain

ISBN: (纸本)9781837242689

The GENERA Card Game combines serious gaming and blockchain technology to raise awareness on renewable energy. As part of the GENERA project, this multiplayer game engages players by assigning them the role of a town's mayor tasked with expanding their town using sustainable energy sources. The game integrates educational content with interactive gameplay, utilizing NFTs for the in-game assets and ERC-20 tokens as rewards. Developed using web technologies, the game benefits from extensive accessibility while maintaining high-quality features. A survey of 45 players indicated high satisfaction with the game's educational impact and accessibility, though it highlighted areas for improvement in user experience and mechanics. The study underscores the potential of serious games in promoting environmental awareness and the benefits of blockchain technology in enhancing educational tools. Future updates will focus on adding new features, improving user interface and experience, and integrating realistic data on renewable energy sources to further enrich the educational content. The findings suggest that serious games like GENERA can effectively raise awareness and foster a deeper understanding of renewable energy, thereby supporting global sustainability efforts. © The Institution of engineering & Technology 2024.

关键词： Clean energy

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