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Emoji Prediction from Twitter Data using Deep Learning Approach

Emoji Prediction from Twitter Data using Deep Learning Appro...

引用

2021 Asian Conference on Innovation in Technology, ASIANCON 2021

作者： Durga Pavithra Kollipara, V.N. Hemanth Kollipara, V.N. Prakash, M Durga V R Siddhartha Engineering College Dept. of Electronics and Communication Vijayawada India Vellore Institute of Technology Dept. of Computer Science Vellore India

ISBN: (纸本)9781728185835

Emojis are a small visual representation of emotions or objects that are usually used in text messages to enhance the communication experience between individuals. With the rise in the widespread use of social media platforms like Twitter and instant messaging, many users are using these emojis in their text messages to convey broad feelings efficiently, which sometimes cannot be expressed using just words. This combination of text and emojis to improve emotion has become an essential part of how people communicate in the 21st century. Thus, giving rise to a problem statement that is to identify the relationship between these text messages and the emojis used in them. In this paper, we propose an approach to predict multiple emojis for a given text-based tweet message. Our proposal contains three modules, where the first module preprocesses the given text data, the second module is the model on which the data is trained, and a multi-class classifier to predict the emojis evoked by the given text. The objective of this model is to understand the underlying semantics of the text sentence using natural language processing techniques to predict reasonable emojis. © 2021 IEEE.

关键词： Semantics

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Poster: Clipped Quantization and Huffman Coding for Efficient Secure Transfer in Federated Learning

Poster: Clipped Quantization and Huffman Coding for Efficien...

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Seung-Ho Lim Min Choi Ki-Woong Park Division of Computer Engineering Hankuk University of Foreign Studies Yongin Korea Sch. of Information and Communication Engineering Chungbuk National University Cheongju Korea Dept. of Computer and Information Security Sejong University Seoul Korea

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Federated Learning(FL) has become an emerging method that trains private data by distributed learning of shared parameter, however, it has high communication overhead and is still exposed to attack on the model parameters. To minimize the communication overhead of federated learning while preserving its accuracy and security, we considered a combination of techniques with gradient quantization, clipping, and Huffman coding. Our system produces reduced parameters through quantization and clipping, then encodes the parameters through Huffman coding, which further increases the compression ratio as well as security of the parameters to be transmitted. Preliminary results identify that the scheme can significantly reduce the amount of transferring while preserving accuracy and security.

关键词： Quantization (signal) Accuracy Federated learning Distance learning Distributed databases Encoding Data models Security Huffman coding Edge computing

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Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Media

Unconventional Computing based on Four Wave Mixing in Highly...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Kostas Sozos Stavros Deligiannidis Charis Mesaritakis Adonis Bogris Dept. of Informatics and Computer Engineering. Aghiou Spiridonos University of West Attica Egaleo Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Karlovassi Samos Greece

ISBN: (纸本)9798350345995

Nowadays, photonic analog computing in the form of neuromorphic photonics or photonic neural networks has resulted in powerful hardware platforms that accelerate difficult tasks with marginal power consumption. Although linear transformations are efficiently carried out in the optical domain, nonlinear transformations are also of paramount importance. Here, we propose four wave mixing (FWM) process as an elegant solution for the generation of diverse nonlinear higher order products of an original signal. On one hand, these products can act as activation functions in different types of neural networks, either recurrent neural networks (RNNs) and reservoir computers or in any other type of brain-inspired frameworks. On the other hand, the same products can form the basis for polynomial regression which is a very powerful technique used in machine learning in different tasks i.e. for the prediction of COVID-19 transmission [1].

关键词： Four wave mixing Machine learning Neural networks Nonlinear wave mixing Optical computing Quantum computation

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Medical Data Security Using Blockchain

Medical Data Security Using Blockchain

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： Indrani Yella Venkata Sumanth Koppakula Sai Bhargav Jonnalagadda Mayur Teja Gudemupati SVL Sowjanya Nukala Sibendu Samanta Department of Computer Science and Engineering SRM University - AP Andhra Pradesh India Dept. of Electronics and Communication Engineering SRM University - AP Andhra Pradesh India Dept. of ECE & CSE SRM University - AP Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

In general, hospital procedures require a large number of medical reports, which are an essential component of the process. Hospitals are now increasing their business by integrating pathology and other test laboratories into the hospital for efficient and speedy reporting, as well as increased income. Hospital operations cover a wide range of functions, from patient admission and management to hospital expenditure management. This, together with additional services such as pathology and pharmacy management, adds operational complexity and makes it harder to track. To address this issue, we employ blockchain technology to keep track of every single transaction with 100% authenticity using the hyperledger idea. All transactions are encrypted and saved as blocks to enable authentication over a network of computers rather than a centralized server. Further-more, we employ the hyperledger idea to associate and preserve all of the medical papers linked with each transaction, including the date stamp. This enables for the authentication of each report, which will be identified if edited by anybody.

关键词： computers Pathology Hospitals Distributed ledger computer network reliability Authentication Signal processing Blockchains Complexity theory Reliability

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SCHEMA III: Dynamic & Scalable VNE Framework Based on Multi-Agent RL for 5G/6G Networks

SCHEMA III: Dynamic & Scalable VNE Framework Based on Multi-...

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IEEE Conference on Global communications (GLOBECOM)

作者： Anestis Dalgkitsis Luis A. Garrido Kostas Ramantas John S. Vardakas George Kormetzas Christos Verikoukis Iquadrat Informatica S.L. Barcelona Spain Information and Communication Systems Engineering Dept. University of the Aegean Samos Greece Computer Engineering & Informatics Dept. University of Patras Greece

Network Virtualization (NV) has proved a promising technology that allows multiple heterogeneous Virtual Networks (VNs) to operate simultaneously on the same infrastructure. Dynamic Virtual Network Embedding (NVE) has emerged as an enabler of elasticity and scalability in the VN deployment and resource allocation of the physical infrastructure. However, the key challenge in realizing NV in a sustainable way is how to dynamically embed VNs efficiently into physical network, which is defined as the VNE problem. To address this challenge, this paper proposes an approach that leverages Multi-Agent Reinforcement Learning (MARL) to solve the dynamic VNE of VNs for 5G/6G communication systems. The proposed framework consists of multiple horizontally distributed RL agents that co-operate to devise temporally dynamic VNE placements. The key contributions of this work are introducing a novel dynamic VNE orchestration framework for multi-domain networks based on Distributed RL, providing a scalable VNE framework targeted to Ultra-Reliable Low-Latency communication (URLLC) services, evaluating and comparing the proposed algorithm with existing solutions in the state of the art. The paper concludes that there is a significant improvement in latency of 144.191% when compared to the baselines.

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A Transfer Learning-Based Intelligent Judiciary system for Public Safety

A Transfer Learning-Based Intelligent Judiciary System for P...

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2024 IEEE International Conference on communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Pathak, Lakshin Virani, Mili Obaidat, Mohammad S. Patel, Prachita Hathaliya, Jigna J. Jadav, Nilesh Kumar Tanwar, Sudeep Yamsani, Nagendar Nirma University Dept.of CSE Ahmedabad India School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence TN 603203 India School of Engineering The Amity University UP Noida201301 India Marwadi University Dept. of CE-AI Rajkot India SR University Department of CS & AI Telangana India

ISBN: (纸本)9798350349832

With the increasing prevalence of criminal activities, there is a growing need for effective crime detection methods leveraging modern technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. In this paper, we propose a transfer learning (TL)-based solution for criminal face detection using convolutional neural networks (CNNs) to improve public safety. Leveraging pre-Trained CNN models, we fine-Tune them on a dataset comprising images of known criminals and non-criminals. The proposed framework aims to mitigate the challenges associated with training CNNs from scratch, such as data scarcity and computational inefficien-cies. Experimental results demonstrate the effectiveness of the proposed framework in accurately identifying criminal faces, showcasing its potential for enhancing law enforcement efforts. We achieved the highest accuracy of 97% with the TL scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

Mask Generation with Meta-Learning Classifier Weight Transfo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Fong-Ci Jhou Kai-Wen Liang Chung-Hsun Lo Chien-Yao Wang Yung-Fang Chen Jia-Ching Wang Pao-Chi Chang Dept. Communication Engineering National Central University Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Dept. Computer Science and Information Engineering National Central University Taiwan

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system.

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Traffic Light Control Using Reinforcement Learning

Traffic Light Control Using Reinforcement Learning

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Integrated Circuits and communication systems (ICICACS), IEEE International Conference on

作者： S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Ramachandra A C Md Babul Islam Rezaul Haque Mohammad Mominur Rahman Dept of CSE Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh College of Science and Engineering Hamad Bin Khalifa University Qatar

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people’s general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability.

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Control of Deterministic Chaotic Modes in Power systems

Control of Deterministic Chaotic Modes in Power Systems

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Mamyrbek Beisenbi Ainur Satpayeva Shugla Mussabayeva Gulnara Mussina Meruert Tlegen Dept. of System Analysis and Control L.N. Gumilyov Eurasian National University Astana Kazakhstan Dept. of Computer Engineering Astana IT University Astana Kazakhstan

The paper examines a new approach to controlling deterministic dynamical chaotic mode of power system with two electric generate-sources. A control law is synthesized in the class of one-parameter structurally stable mappings from catastrophe theory. Analytical calculations and a numerical experiment in MatLab showed that the proposed system excludes the generation of a deterministic chaotic mode for any changes in the uncertain system parameters. The study of the control system is carried out by the gradient-velocity method Lyapunov vector functions

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Enhancing Online Learning Monitoring with Novel Image Recognition Method Using Dlib for Eye Feature Detection

Enhancing Online Learning Monitoring with Novel Image Recogn...

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International Conference on Awareness Science and Technology (iCAST)

作者： ChunHsiu Yeh Wei-Cheng Shen Hung-Yu Chi Chin-En Lin Jong-Shin Chen Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan ROC College of Computer Science Jimei University Xiamen China Dept. of Information Communication and Engineering ChaoYang University of Technology Taichung Taiwan ROC

In the post-pandemic era, online learning has become a prevalent mode of education. To ensure effective monitoring of learners' engagement in online courses, this study proposes a novel method for real-time monitoring of learners' learning focus. It is divided into three main components: The first component is eye detection. We utilize Dlib technology and leverage pre-trained facial landmark detection to accurately identify the positions of the eyes. Through image processing and the application of appropriate thresholds (thresholds set above 190), this enables precise outlining of eye contours and recognition of eye movement characteristics, even during rapid eye movements or from varying angles. The second component is gaze estimation. By using adaptive thresholds for pupil segmentation and applying erosion and dilation to reduce image noise and enhance essential features, we can effectively determine pupil positions within images taken under varying photographic resolutions and lighting conditions. Angle calculation is subsequently employed to determine whether learners are concentrating on online learning courses. The third component involves an anti-cheating mechanism. By utilizing pupil-centered corneal reflex (PCCR) technology to address potential cheating behaviors of learners, an anti-cheating mechanism is proposed to detect and counter fraudulent tactics such as photos and makeup. Experimental results demonstrate that this study is capable of effectively monitoring learners' learning status and accurately determining whether learners exhibit appropriate learning attitudes and behaviors. This empowers a comprehensive assessment of learners' learning outcomes during the period of online learning.

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