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uSAP: An Ultra-Fast Stochastic Graph Partitioner

uSAP: An Ultra-Fast Stochastic Graph Partitioner

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Chih-Chun Chang Tsung-Wei Huang Dept. of Electrical and Computer Engineering University of Wisconsin-Madison

Stochastic graph partitioning (SGP) plays a crucial role in many real-world applications, such as social network analysis and recommendation systems. Unlike the typical combinatorial graph partitioning problem, SGP presents unique computational difficulties due to time-consuming sampling processes. To address this challenge, the recent HPEC launched the Stochastic Graph Partitioning Challenge (SGPC) to seek novel solutions from the high-performance computing community. Despite many SGP algorithms over the last few years, their speed-ups are not remarkable because of various algorithm limitations. Consequently, we propose uSAP, an ultra-fast stochastic graph partitioner to largely enhance SGP performance. uSAP introduces a novel strongly connected component-based initial block merging strategy to reduce the number of partitioning iterations significantly. To further improve the runtime and memory performance, uSAP adopts a dynamic batch parallel nodal block assignment algorithm and a dynamic matrix representation. We have evaluated uSAP on the 2022 official HPEC SGPC benchmarks. The results demonstrate the promising performance of uSAP on graphs of different sizes and complexities. For example, uSAP achieves 129.4x speed-up over the latest champion on a graph of 50K nodes.

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Deep learning detection approach for speech impairment children in Parkinson's disease 6

Deep learning detection approach for speech impairment child...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Najm, Ihab Ahmed Abdulateef, Omar Gheni Ali, Ahmed Hussein Ahmed, Saadaldeen Rashid Ahmed, Mohsin A. Algburi, Sameer College of Education for Women Tikrit University Salahaldin Iraq College Of Literature University Of Samarra Salahaldin Iraq Dept. computer engineering technique Al Hikma University College Baghdad Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Computer Techniques Engineering Al-Ma’moon University College Al-Washash / Baghdad Iraq AL-KITAB UNIVERSITY KIRKUK36015 Iraq

ISBN: (纸本)9798350394634

Long-term neurological conditions that affect patients on a large scale, for instance, Parkinsonian disease (PD) represent a global strain in the life of patients and healthcare. Early detection and monitoring are the gates of successful treatment, these are the best ways that can be used to fight this disease and to stop its progress. The paper will be on the usefulness of voice pitch of tone of voice [rather than directly Wallace clear sounding as a marker of PD disease. The proposed paper looks forward to implementing a deep learning model to classify a varied arrangement of PD symptoms into a single class. As the two public datasets available for the data, the model is built with 61 fusing categories which there are 22 voice measures and 39 Mel Frequency Cepstral Coefficients (MFCC). The CNN-LSTM architecture that achieves a high accuracy rate of 97% was used. The brought forth model thus presents a viewing point for remote PD detection and monitoring which could be transformative to the clinical practice. © 2024 IEEE.

关键词： Long short-term memory

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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A Thyristor Controlled Bidirectional DC Solid State Circuit Breaker for Residential Microgrids

A Thyristor Controlled Bidirectional DC Solid State Circuit ...

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IEEE General Meeting Power& Energy Society

作者： Aditya Pogulaguntla Griddaluru Venkata Yagna Satish Naik Banavath Rajendra Singh Dept. of Electrical Engineering Indian Institute of Technology Dharwad Karnataka India Dept. of Electrical and Computer Engineering Clemson University Clemson USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Buildings consume roughly 40% of total global energy out of which, residential buildings account for three-quarters of total energy consumption in the building sector, and there is significant room for improvement in energy efficiency. Residential dc microgrid is a promising technology that aims to ease the transition to energy-efficient homes and provide a simple, highly flexible integration of renewable energy sources, storage, and loads. However, the dc systems have no natural current zero crossing points and lack proper standards to safeguard residential dc systems against faults. Furthermore, because of the low inertia, the fault current may surge to greater magnitudes quickly, necessitating the adoption of solid-state circuit breakers (SSCBs), which are fast and reliable protection devices. This paper introduces a thyristor-based bidirectional SSCB wherein the fault current interruption is achieved by adopting coupled inductors. A detailed operation and analysis of the proposed SSCB is presented. The paper also presents an experimental validation of the proposed SSCB at a dc system rating of 250 V and 10 A (20 A fault current) and 350 V at 20 A (25 A fault current).

关键词： Circuit breakers Buildings Microgrids Energy efficiency Solid state circuits Topology Circuit faults Protection Integrated circuit reliability Fault currents

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Stacking Ensemble Framework for Hate Speech Detection in Bangla Videos

Stacking Ensemble Framework for Hate Speech Detection in Ban...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Shamrin Jahan Mim Tanjim Mahmud Md Hasan Ali Mohammad Tarek Aziz Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The proliferation of internet usage and social media platforms has significantly enhanced the ability of individuals to express their opinions on various topics. However, this freedom of expression sometimes morphs into a vehicle for disseminating hate speech, leading to increased incidents of cyberbullying, violations, and conflicts. Particularly on video-sharing websites, which have become a prominent stage for such activities due to their widespread use and the dynamic nature of video content. This study aims to address the issue of hate speech in video content by developing a robust method for detecting hate speech in Bangla language videos. The focus is on the spoken content within these videos, which is a primary vector for the transmission of harmful messages. We constructed a comprehensive dataset by extracting and converting audio from a collection of videos into text. Utilizing this dataset, we applied machine learning techniques and deep learning models to analyze and classify the content. Specifically, our approach involves a stacking ensemble model that combines the strengths of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) with the analytical capabilities of customized feature extraction applied to Multinomial Naive Bayes and Random Forest classifiers serving as a meta-model. The proposed stacking ensemble model demonstrates a high level of efficacy, achieving an accuracy rate of 96% in detecting hate speech within the tested video content. This performance indicates a significant advancement over existing methods, underlining the effectiveness of our hybrid, multi-model approach.

关键词： Analytical models Computational modeling Hate speech Stacking Logic gates Vectors Bayes methods Convolutional neural networks Vehicle dynamics Random forests

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Gaussian Process Dynamical Modeling for Adaptive Inference Over Graphs

Gaussian Process Dynamical Modeling for Adaptive Inference O...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qin Lu Konstantinos D. Polyzos Dept. of Electrical and Computer Engineering University of Minnesota USA

Graph-based inference arises in a gamut of network science-related applications, including smart transportation, climate forecasting, and neuroscience. Given observations over a subset of the nodes due to sampling costs or privacy considerations, extrapolation of time-varying signals over the unobserved nodes can be realized by leveraging their spatio-temporal correlations across the graph. Building on a recently proposed Gaussian process (GP) auto-regressive model to capture spatio-temporal dynamics across slots, the present work further pursues an adaptive framework by ensembling a candidate set of such dynamical models, each representing a unique dynamic pattern of the sought process. With nodal observation arriving on-the-fly, the proposed method simultaneously estimates the missing nodal values and selects the fitted dynamical model via data-adaptive weights. Tests with real data showcase the merits of the proposed method.

关键词： Adaptation models Privacy Neuroscience Buildings Gaussian processes Signal processing Smart transportation

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Augmented Reality Based Machine Monitoring for Legacy Machines: A retrofitting use case 30

Augmented Reality Based Machine Monitoring for Legacy Machin...

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30th International Scientific Conference Electronics, ET 2021

作者： Kancharla, Chandrakanth R. Bekaert, Lukas Lannoo, Jonas Vankeirsbilck, Jens Vanoost, Dries Boydens, Jeroen Hallez, Hans Dept. of Computer Science Dept. of Electrical Engineering Brugge8200 Belgium

ISBN: (纸本)9781665445184

Monitoring a machine and the insight it provides for appropriate maintenance is of prime importance for the modern industry. While this is fully supported in Industry 4.0, many manufacturing units today are unable to use its technologies. One of the major reasons behind that is the lack of onboard sensors to capture and communicate appropriate data from a legacy machine. Retrofitting is considered an efficient solution to include legacy machines in an industry 4.0 ecosystem by adding external sensing and connectivity. As a proof of concept, in this paper we consider an induction motor for retrofitting, a major component of any manufacturing unit. We further show how retrofitting can help in making legacy machines compliant to the new technologies from Industry 4.0. Finally, visualization of the collected data is performed over a web based Augmented Reality application. The choice for a web-based Augmented Reality application was made to enable monitoring of the sensor data over a multitude of devices. © 2021 IEEE.

关键词： Data visualization

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On the Involvement of IEEE 802.11ah Enabled Unmanned Aerial Vehicles (UAVs) in Emergency Networks

On the Involvement of IEEE 802.11ah Enabled Unmanned Aerial ...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Kostas Chounos Manos Maroulis Thanasis Korakis Dept. of Electrical and Computer Engineering University of Thessaly Greece

In this work, a practical evaluation for IEEE 802.11ah enabled Unmanned Aerial Vehicles (UAVs) is carried out. The proposed aerial devices can be involved and help significantly in dealing with natural disasters, by providing extended network capabilities in long distances. The major advantages of those developed emergency networks are indicatively the easy deployment, the low power consumption and the dynamic wide extension. In contrast with prior approaches which solely based on simulations, in this work the experiments are conducted with the use of real IEEE 802.11ah network adapters and drones. Finally, the examined experimental scenarios reveal the realistic limits of the aforementioned wireless protocol, which constitutes the main criterion for whether this can be used in emergency scenarios.

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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First-principles density functional calculations for non-lead perovskites 12

First-principles density functional calculations for non-lea...

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Physics, Simulation, and Photonic engineering of Photovoltaic Devices XII 2023

作者： Esther, Emily Zhu, Peifen Henshaw, John Dept. of Mechanical Engineering Univ. of Tulsa 800 South Tucker Drive TulsaOK74104 United States Dept. of Electrical Engineering and Computer Science Univ. of Missouri 230 Jesse Hall ColumbiaMO65211 United States

ISBN: (纸本)9781510659377

The possibility of replacing lead in perovskites with other elements in 2D and 3D perovskites is explored. The most highly efficient perovskites are made with an inorganic-organic compound with lead. The influence on band structure, the density of states, and absorption coefficient are explored using first-principals density functional calculations. The advantages and disadvantages of 2D vs. 3D perovskites and their efficiency for energy conversion are also discussed. Ab initio DFT methods were used and implemented using a VASP software package. This research is important because perovskites solar cells are the most efficient solar cells currently studied by researchers, but they are prevented from being introduced to the market due to their instability. Perovskites are unstable and break down when they encounter things that they would commonly encounter during their application including moisture, high UV light, and excessive heat. When the perovskite structure starts to break down, lead is released into the environment. © 2023 SPIE.

关键词： Perovskite

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