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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Modeling of Extended Flooding of the Volga-Akhtuba Floodplain Under Various Conditions

Modeling of Extended Flooding of the Volga-Akhtuba Floodplai...

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Applied Mathematics, Computational Science and Mechanics: Current Problems (AMCSM)

作者： Anna Yu. Klikunova Tatyana A. Dyakonova Ekaterina O. Agafonnikova Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

ISBN: (数字)9798350348576

ISBN: (纸本)9798350348583

A computer model of a extended flood due to an accident at a hydraulic structure is considered. The model is based on the two-dimensional Saint-Venant equations, taking into account the friction and the structure of the terrain on the right side. The integration of the equations is carried out using the Lagrangian-Eulerian CSPH-TVD method (Combined Smooth Particle Hydrodynamics - Total Variation Diminishing). Modeling of various scenarios of the destruction of the Volga hydroelectric dam on the dry bottom during the fall and the wet bottom during the seasonal flood was carried out. For calculations, a regular digital terrain model is used, taking into account the channels of the Volga, Akhtuba and the main floodplain peaks. The main parameters of the breakout wave are calculated. Integral characteristics of flooding for the northern part of the Volga-Akhtuba floodplain are obtained. It is shown that hydraulic friction influences the propagation velocity of the breakthrough wave. The simulation results for a given digital model confirm the hypothesis that Volgograd is located in a zone that is not subject to flooding.

关键词： Water Dams Computational modeling Friction Hydraulic systems Predictive models Hydrodynamics

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computer Modeling of the Distribution of Pollutants in the Bed of the Volga River and the Akhtuba River

Computer Modeling of the Distribution of Pollutants in the B...

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Eugenii Baskakov Egor Savin Mariya Kornaukhova Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

The process of describing the distribution of impurities is described using two examples: phenomena in the bed of the Volga River in the northern section of the Volga-Akhtuba floodplain without a source under the influence of velocity fields and the flow of impurities from two sources. computer simulation is implemented on the basis of software developed using supercomputer technologies written using the CUDA software and hardware architecture. A mathematical model is proposed that describes the process of transfer of impurities in the aquatic environment. The process of developing and testing a software package is presented. The functionality of the created program allows you to predict the speed and range of propagation of various impurities in a continuous medium with a visual representation. The software package consists of two modules: calculation and visualization. The calculation module is implemented by transferring the computational load to the video card. The graphics module is implemented in Python. The results of numerical modeling of the distribution of pollutants in the Volga River bed are presented. The obtained result shows agreement with the real situation and the results of other researches.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, Engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Overcoming Imbalanced Datasets and Feature Complexity in Fraud Transaction Detection Through Down-Sampling and Dimension Reduction 17

Overcoming Imbalanced Datasets and Feature Complexity in Fra...

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17th International Conference on Open Source systems and Technologies, ICOSST 2023

作者： Shan, Chan Pei Leng, Too Ai Ahmad, Muneer Malik, Yasir Jaffar, Fehmi Universiti Malaya Dept. of Information Systems Malaysia Digby Stuart College University of Roehampton United Kingdom Bishops University Dept. of Computer Science Canada Université du Québec à Chicoutimi Département d'Informatique et de Mathématique Canada

ISBN: (纸本)9798350381320

Detecting fraudulent transactions presents two primary obstacles, the presence of imbalanced datasets and the sheer volume of feature categories. The scarcity of fraudulent instances within the dataset often results in these cases being erroneously classified as noise, consequently introducing bias towards non-fraudulent cases in the detection outcomes. Simultaneously, the extensive array of feature categories poses a formidable challenge in identifying crucial variables, thereby limiting the efficacy of detection algorithms. This research contributes to the field of fraud transaction detection by proposing a multifaceted approach that addresses the challenges of imbalanced data and feature complexity. Through down-sampling, dimension reduction, model evaluation, and precision enhancement, the study aims to improve the accuracy and efficiency of fraud detection models, with the goal of reducing false alarms and financial losses in real-world scenarios. © 2023 IEEE.

关键词： Principal component analysis

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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SES: Smart Education System

SES: Smart Education System

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International Conference on computer and Applications (ICCA)

作者： Wadha Alyammahi Saed Alrabaee Dept. of Computer and Network Engineering Dept. of Information Systems and Security College of Information Technology United Arab Emirates University AlAin UAE

Responding to inefficiencies in traditional classroom management methods, this paper proposes an innovative, technology-driven approach to enhance educational processes. Central to our proposal is the development of a facial recognition system designed for efficient student attendance tracking, thus eliminating time-consuming roll calls. Additionally, we plan to introduce interactive whiteboard features to enrich classroom dynamics between students and faculty. However, our approach extends beyond mere attendance tracking and interactive learning. We aim to launch a Program Learning Outcomes (PLO) and Course Learning Outcomes (CLO) mapper. Leveraging Natural Language Processing (NLP) techniques, this tool will auto-align CLOs with PLOs, facilitating a more efficient curriculum development process. We also suggest implementing a feature powered by YOLOv5 to monitor and assess student attention in the classroom. Our comprehensive suite of tools is designed to equip educators with resources to refine their teaching strategies and boost student learning outcomes. By integrating facial recognition for attendance, interactive whiteboard features, NLP-based CLO/PLO mapping, and attention monitoring, we aspire to provide a robust solution enabling educators to adapt their teaching methods to students’ unique needs.

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An Overview of Cybersecurity Knowledge Graphs Mapped to the MITRE ATT&CK Framework Domains

An Overview of Cybersecurity Knowledge Graphs Mapped to the ...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Joshua Bolton Lavanya Elluri Karuna Pande Joshi Dept. of Information Systems University of Maryland Baltimore County Baltimore USA Dept. of Computer Information Systems Texas A&M University - Central Texas Killeen USA

A large volume of cybersecurity-related data sets are generated daily from systems following disparate protocols and standards. It is humanly impossible for cybersecurity experts to manually sieve through these large data sets, with different schema and metadata, to determine potential attacks or issues. A myriad of applications and tool sets are offered to automate the analysis of large cyber data sets. Semantic Web's community has been studying the field of cybersecurity for over a decade and produced numerous knowledge graphs and frameworks. The Unified Cybersecurity Ontology (UCO) connected many of the leading knowledge representation frameworks, providing a holistic mapping of cyber data, beginning in 2016. MITRE ATT&CK is used by a wide variety of practitioners to understand how their current data and tooling prepare them to defend against both Advanced Persistent Threats (APTs) and less formal threat actors. The UCO and MITRE ATT&CK have provided researchers and practitioners, respectively, with tools to standardize data collection, correlation, and analysis. However, it is not apparent how current knowledge graphs and their applications in the cybersecurity domain utilize ATT&CK. In this paper, we present the results of our study on whether current cybersecurity knowledge graphs have mapped the main MITRE ATT&CK matrices.

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Blockchain Anchored Federated Learning and Tokenized Traceability for Sustainable Food Supply Chains 4

Blockchain Anchored Federated Learning and Tokenized Traceab...

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4th International Conference on Ubiquitous Computing and Intelligent information systems, ICUIS 2024

作者： Chaganti, Koushik Reddy Kumar, B. Naveen Gutta, Pruthvi Krishna Elicherla, Sivananda Lahari Reddy Nagesh, C. Raghavendar, K. University of the Cumberlands Dept. of Computer and Information Systems KY United States Vignan Institute of Technology and Science Department of Information Technology Hyderabad India Tesla Dept of It AustinTX United States Dayananda Sagar University Dept of Cse Bangalore India Srinivasa Ramanujan Institute of Technology Dept of Cse Anantapur India Teegala Krishna Reddy Engineering College Dept of Cse Hyderabad India

ISBN: (纸本)9798331529635

The management of food supply chains relates to just about the most imperative issues concerning sustainability, quality, and traceability, where existing methodologies usually fail to meet the demands caused by data privacy, lack of transparency, and inefficiencies concerning collaboration among stakeholders. This compromises data security, and thus there arises single points of failure. Conversely, traditional traceability frameworks fail to incite wide-scale participation and lack real-time accuracy. Having these limitations in mind, we introduce a hybrid framework consisting of three advanced methodologies: Decentralized Federated Learning with Blockchain Anchoring (DFLBA), Dynamic Tokenized Traceability Framework (DTTF), and Adaptive Supply Chain Graph Neural Networks (ASCGNN) process. DFLBA would support privacy-preserving machine learning as such that collaborative training of yield prediction and crop monitoring models by stakeholders without revealing raw data will be feasible. Blockchain anchoring ensures logs of training updates are tamper-proof, thus increasing trust and transparency. DTTF introduces tokenized incentives that enhance traceability in real-time: every product has a digital twin represented by blockchain-anchored tokens updated by stakeholders. Smart contracts enforce the compliance that will improve participation and lifecycle data accuracy. ASCGNN models the food supply chain as a dynamic graph, leveraging graph neural networks to capture stakeholder interdependencies, optimize operations, and detect fraud. Blockchain integration ensures reliability and transparency by securing edge weights. The suggested framework has demonstrated good impact, like 95% model accuracy for yield prediction, 99% traceability coverage, and 92% prediction accuracy for bottlenecks of the supply chain. It provides a scalable, privacy-preserving, and transparent solution that enhances the efficiency of food supply chains, and positively impacts stakeholders in a

关键词： Federated learning

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Exploring Key Factors Driving QR Payment Adoption in Digital Banking in Indonesia

Exploring Key Factors Driving QR Payment Adoption in Digital...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Marchesa Ruisli Marviola Hardini Yulia Putri Ayu Sanjaya Padeli Harry Agustian Dept. of Information Technology University of Raharja Tangerang Indonesia Dept. of Retail Management University of Raharja Tangerang Indonesia Dept. of Computer Systems University of Raharja Tangerang Indonesia

ISBN: (数字)9798331528799

ISBN: (纸本)9798331528805

Technological progress within the banking industry has facilitated the extensive adoption of digital payment solutions, particularly those utilizing QR codes. Although these methods offer convenience and efficiency, significant hurdles remain in securing widespread consumer endorsement. This study analyzes factors influencing the intention to adopt QR payments, focusing on Perceived Ease of Use, Perceived Usefulness, Technology Readiness, Trust in Technology, and Behavioral Intention. Using Structural Equation Modeling (SEM) with SmartPLS, the study surveyed 400 digital banking users in Indonesia. Results reveal that perceived ease of use and usefulness significantly impact behavioral intention to adopt QR payments. Additionally, tech-nology readiness and trust in technology are critical drivers of adoption. The findings emphasize that enhancing QR payment adoption requires not only technological advancements but also ensuring user-friendliness and trust, which are statistically signif-icant factors in driving adoption, as demonstrated by the p-value <0.01 in our analysis.

关键词： Industries Numerical analysis Green products Banking QR codes Software systems Mathematical models User experience Security Sustainable development

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