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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Jangada, Atharva Dadlani, Nimish Raina, Sanchit Sooraj, V.S. Buchade, A.R. Pune Institute of Computer Technology Savitribai Phule Pune University Dept. of Computer Engineering Pune India Mit School of Engineering Mit Art Design and Technology University Dept. of Comp Science and Engineering Pune India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

The revolutionary concept of blockchain, the technology behind the popular cryptocurrency Bitcoin and its successors, has ushered in a new era in the Internet and online services. While most people focus only on cryptocurrencies when they think about Blockchain, in fact many administrative activities and day-to-day services are managed offline and/or privately are now being securely transferred to the Internet as online services. Electronic Voting is one of the trending and important aspect of online services. Traditional voting systems in our country include the use of ballots at polling stations or EVM's (Electronic Voting Machine). The ballot system is prone to tampering, whereas EVM's being a centralised system, are prone to hacking. We aim to offer a more secure, transparent, safer, cheaper and easier to use alternative to the above methods of voting, by making use of the revolutionary Blockchain technology. Smart contracts are meaningful pieces of code that are incorporated into the Blockchain and implemented on schedule at each stage of the Blockchain update. Ethereum and its network is one of the most convenient due to its stability, wide usability and smart contract logic. In this work, we will be implementing and testing a prototype of an E-Voting application using Ethereum Wallet and Solidity language as a smart contract for the Ethereum network. © 2022 IEEE.

关键词： Smart contract

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Intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Gloria Mollah Majumder Haider Danda B. Rawat Imtiaz Ahmed Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

关键词： Navigation Simulation Wireless networks Artificial neural networks Massive MIMO Reconfigurable intelligent surfaces Real-time systems Resource management Optimization Intelligent control

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Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography

Comparative Study of Face Tracking Algorithms for Remote Pho...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Jayasinghe, J.A.S.Y. Katsigiannis, Stamos Malasinghe, Lakmini Sri Lanka Institute of Information Technology Dept. of Electrical and Electronic Engineering Malabe Sri Lanka Durham University Dept. of Computer Science Durham United Kingdom

ISBN: (纸本)9798350327816

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the main steps of this technique is facial tracking and Region of Interest (ROI) extraction. This research paper investigates four widely used face tracking algorithms, namely MediaPipe Face Mesh (MPFM), Haar Cascade, Multi-task Cascaded Convolutional Network (MTCNN), and Dlib, concerning their ROI extraction capabilities for rPPG HR measurements. Using evaluation metrics such as accuracy, processing time, and ease of extracting ROIs, this work also recommends the most suitable face tracking algorithm from those mentioned above for rPPG measurements, and presents a compilation of a prioritization list of ROIs based on their sensitivities for rPPG measurements. Experimental results showed that the MPFM algorithm and cheek ROIs provided the best HR measurements. © 2023 IEEE.

关键词： Face Tracking Machine Learning Remote Photoplethysmography ROI Extraction

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks

Sentiment Analysis using Speech Recognition for Improved Cus...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： D.Deva Hema N Sujith P J Rithika S Bhavesh Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces “Humanization through Automation,” a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization.

关键词： Sentiment analysis Emotion recognition Analytical models Automation Noise reduction Speech recognition computer architecture Feature extraction Real-time systems Convolutional neural networks

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Improved Sampling Methods for Evaluation of Classification Performance

Improved Sampling Methods for Evaluation of Classification P...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yue-Shi Lee Show-Jane Yen Yi-Jie Tang Dept. of Computer Science & Information Engineering Ming Chuan University Taoyuan Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

To evaluate the classification accuracy of a classification algorithm on a certain dataset, it is necessary to sample training and test data from the original dataset. After building the classification model from the training data, the test data can be used to evaluate the classification accuracy. However, in order to evaluate the classification accuracy of a classification algorithm on an original dataset, it is usually necessary to sample the training/test data many times, build the classification model many times, and then average the accuracies of all the evaluation, which will consume a lot of time and resources. To address this, we propose a sampling method so that the sampled training/test data can replace the results of building multiple models and averaging multiple classification accuracy, that is close to the evaluation results of the original data. Our approach introduces different methods for calculating the similarity of data distributions and incorporates feature weights in the similarity calculation process to select training/test sets that are close to the distribution of the original dataset.

关键词： Training Earth Accuracy Buildings Training data Sampling methods Data models Classification algorithms Artificial intelligence Testing

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Seungwoo Han Dept. of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification.

关键词： Correlation coefficient Correlation Arrhythmia Biological system modeling Frequency-domain analysis Electrocardiography Feature extraction Graph neural networks Bioinformatics

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Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0

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International Journal of Intelligent Networks 2023年第1期4卷 162-170页

作者： Patil, Suvarna Kaur, Mandeep Rogulj, Katarina Dr. D Y Patil Institute of Engineering Management and Research Pune India Dept. of Computer Science Savitribai Phule Pune University Pune India Permtech Research Solutions Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities. Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in he

关键词： Internet of things

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EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain 23

EMS-BERT: A Pre-Trained Language Representation Model for th...

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Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies

作者： M Arif Rahman Sarah Masud Preum Ronald D. Williams Homa Alemzadeh John Stankovic Dept. of Computer Science University of Virginia Charlottesville VA USA Dept. of Computer Science Dartmouth College Hanover NH USA Dept. of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

ISBN: (纸本)9798400701023

Emergency Medical Services (EMS) is an important domain of healthcare. First responders save millions of lives per year. Machine learning and sensing technologies are actively being developed to support first responders in their EMS activities. However, there are significant challenges to overcome in developing these new solutions. One of the main challenges is the limitations of existing methods for EMS text mining, and developing a highly accurate language model for the EMS domain. Several important Bidirectional Encoder Representations from Transformer (BERT) models for medical domains, i.e., BioBERT and ClinicalBERT, have significantly influenced biomedical text mining tasks. But extracting information from the EMS domain is a separate challenge due to the uniqueness of the EMS domain, and the significant scarcity of a high-quality EMS corpus. In this research, we propose EMS-BERT - a BERT model specifically developed for EMS text-mining tasks. For data augmentation on our small, classified EMS corpus which consists of nearly 2.4M words, we use a simultaneous pre-training method for transfer-learning relevant information from medical, bio-medical, and clinical domains; and train a high-performance BERT model. Our thorough evaluation shows at least 2% to as much as 11% improvement of F-1 scores for EMS-BERT on different classification tasks, i.e., entity recognition, relation extraction, and inferring missing information when compared both with existing state-of-the-art clinical entity recognition tools, and with various medical BERT models.

关键词： emergency medical services (EMS) data processing and analysis

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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IEEE International Conference on Big Data

作者： Yuuri Tanaka Yasuhiro Hashimoto Dept. Computer Science & Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Human beings have long been regarded as "homo narrans," employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the depth and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling" ability in humans, who adjust readability based on the original story’s readability. These insights advance our understanding of the "AI narrans" concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities.

关键词： Measurement Vocabulary Correlation Large language models Big Data Data models Complexity theory Global communication Cultural differences Context modeling

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Qinglong Hu Jing Bi Wei Zhang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science City University of Hong Kong Hong Kong China College of Computer Beijing University of Technology Beijing China Dept. of Computer Science and Engineering University of Connecticut Storrs CT USA Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

关键词： Training Accuracy Costs Computational modeling Memory management Organizations Quality of service Predictive models Market research Internet

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