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8th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2023

作者： Bouras, Christos Diasakos, Damianos Katsigiannis, Chrysostomos Kokkinos, Vasileios Gkamas, Apostolos Karacapilidis, Nikos Charalabidis, Yannis Lachana, Zoi Alexopoulos, Charalampos Papadopoulos, Theodoros Karamanolis, Georgios Psalidas, Michail University of Patras Computer Engineering & Informatics Department Patras Greece University Ecclesiastical Academy of Athens Athens Greece University of Patras Department of Mechanical Engineering Patras Greece University of Aegean Department of Information & Communication Systems Engineering Samos Greece Crowd Policy Athens Greece

ISBN: (纸本)9798350341652

Chatbots are witnessing remarkable growth in various fields. In modern society, chatbots have evolved into innovative digital entities that have revolutionized the way people interact with technology. These conversational AI agents have gone beyond their initial uses to become an integral part of various industries. One of the most important roles of chatbots is customer service, providing 24/7 support, quick problem resolution, and personalized interactions. Chatbots free up human agents by handling routine queries and tasks, allowing them to focus on complex and specific issues, and optimizing overall efficiency and customer satisfaction. To this end, this paper aims to present the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services. In addition, the paper presents the results of two pilot chatbot assessments by users with experience in artificial intelligence and chatbot technology. © 2023 IEEE.

关键词： Machine learning

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On Utilizing Unused Slots In Topology-Transparent TDMA MAC Policies for Ad Hoc Networks

On Utilizing Unused Slots In Topology-Transparent TDMA MAC P...

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2022 Global Information Infrastructure and Networking Symposium, GIIS 2022

作者： Dragonas, Vasileios Kallergis, Dimitrios Tsoumanis, Georgios Oikonomou, Konstantinos Ionian University Department of Informatics Corfu Greece University of West Attica Dept. of Informatics & Computer Engineering Aegaleo Greece University of Ioannina Dept. of Informatics & Telecommunications Arta Greece

ISBN: (数字)9781665490955

ISBN: (纸本)9781665490955

In the age of the Internet of Things, numerous devices of diverse characteristics are communicating with each other. The underlying networks supporting those communications can either use some sort of infrastructure or implement an ad hoc network. For the latter type of networks and depending on the application requirements, Topology-Transparent TDMA MAC policies can be considered as they are inherently suited for dynamic and unstructured topologies. However, such TDMA-based schemes may suffer from low throughput performance, especially in sparse networks, where a significant portion of available time slots remains unused. To address this challenge, a Topology-Transparent TDMA MAC policy is revisited in this paper, which utilizes unused slots by allowing nodes to transmit in them under a common and fixed probability p. Additionally, an improvement is proposed, where every node can calculate its own individual access probability based on information gathered from its local neighborhood. Simulation results demonstrate that the use of the individual access probability leads to improved performance in terms of system throughput. Moreover, the requirement for assigning a value to probability p during the network initialization is relaxed, as this value is now calculated dynamically and distributively during the network operation. © 2022 IEEE.

关键词： Ad hoc networks

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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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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Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio 48

Dynamic Independent Component Extraction with Blending Mixin...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cmejla, Jaroslav Koldovsky, Zbynek Kautsky, Vaclav Adali, Tulay Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Czech Republic University of Maryland Department of Electrical and Computer Engineering BaltimoreMD21250 United States

ISBN: (纸本)9781728163277

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian. © 2023 IEEE.

关键词： Blind source separation

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Editorial

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Computational Geometry: Theory and Applications 2024年 122卷

作者： Bae, Sang Won Okamato, Yoshio Division of Artificial Intelligence and Computer Science Kyonggi University Korea Republic of Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Japan

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Progressive Motion Boosting for Video Frame Interpolation

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IEEE Transactions on Multimedia 2023年 25卷 8076-8090页

作者： Xiao, Jing Xu, Kangmin Hu, Mengshun Liao, Liang Wang, Zheng Lin, Chia-Wen Wang, Mi Satoh, Shin'Ichi The School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, i.e., gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework’s flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios. © 2023 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Motion estimation

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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