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2022 IEEE International Conference on Big data, Big data 2022

作者： Paisley, John Rowland, Sebastian Zhe Liu, Jeremiah Coull, Brent Kioumourtzoglou, Marianthi-Anna Columbia University Data Science Institute Dept. of Electrical Engineering New YorkNY United States Columbia University Environmental Health Sciences New YorkNY United States Google Research San FranciscoCA United States Harvard University Department of Biostatistics BostonMA United States

ISBN: (纸本)9781665480451

Bayesian nonparametric methods provide a convenient and well-founded framework for constructing spatio-temporally evolving ensemble models. They not only provide a flexible way to weight different underlying models in an ensemble according to the strengths of each model on different regions of the input space, but also allow for quantification of the ensemble's uncertainty. However, computational costs can pose a challenge to kernel-based ensemble methods when spatio-temporal resolution is high, such as environmental models. We propose a Bayesian Nonparametric Ensemble (BNE) method for spatio-temporal ensemble learning based on the Gaussian process. Our ensemble relies on theoretically well-founded linearized approximations to the Gaussian process to adaptively weight the underlying models in a way that is scalable and amenable to stochastic learning methods. We investigate both the random Fourier feature and Nystrom approaches in this setting. We demonstrate the practicality and usefulness of the approximate model on the problem of air pollution prediction across the contiguous USA over a 6 year period. © 2022 IEEE.

关键词： Learning systems

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Assessing the Impact of Emerging Technologies on Cybersecurity with a Special Emphasis on Artificial Intelligence, the Internet of Things, and Blockchain Innovations

Assessing the Impact of Emerging Technologies on Cybersecuri...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Eliph Mazher Mankhi Akula Rajitha V Revathi H Pal Thethi Dinesh Kumar Yadav K. Surya Kanthi College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh AP Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (TII), Dynamic Risk Assessment (DRA), Blockchain Integrity Verification (BIV), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The TII enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity Verification (BIV) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIV tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions.

关键词： Technological innovation Heuristic algorithms Computational modeling Prediction algorithms Robustness Blockchains Internet of Things

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The Future of Wire Arc Additive Manufacturing Comprehensive Strategies for Improved Close Loop Monitoring and Control

The Future of Wire Arc Additive Manufacturing Comprehensive ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ginni Nijhawan G Lalitha V Asha V J Suresh Praveen Zahraa H. Abdulzahraa Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a series of approaches that improve wire are additive manufacturing (WAAM) for manufacturing many metal items. ATM, MCA 2, ALTC 2, and RSM are the five techniques in the framework. Ablation research examined each approach and how they work together to improve WAAM accuracy, distortion, and material compatibility. The thorough performance comparison, including photographs, indicates that WAAM is better in deposition rate, surface quality, clarity, material diversity, cost-effectiveness, and scalability. The integrated framework enables dynamic modifications, tracking, and iterative improvements. This makes WAAM the optimal additive manufacturing process for many applications. The data indicates how significant each strategy is and how they work together to improve WAAM. This study illuminates sophisticated additive manufacturing technologies and indicates that WAAM is a feasible and flexible alternative to large metal component manufacturing.

关键词： Accuracy Scalability Heuristic algorithms Metals Three-dimensional printing Distortion Wire

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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A Study on the Utilization of Green Hydrogen Energy - An Overview

A Study on the Utilization of Green Hydrogen Energy - An Ove...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Omolayo M. Ikumapayi Opeyeolu T. Laseinde Tin T. Ting Sunday A. Afolalu Adedotun O. Adetunla Temitayo S. Ogedengbe Department of Mechanical and Industrial Engineering Technology University of Johannesburg South Africa Faculty of Data Science and Information Technology INTI International University Nilai Negeri Sembilan Malaysia Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Dept. of Mechanical Engineering Science University of Johannesburg South Africa Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Mechanical Engineering Nile University of Nigeria FCT Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This review focuses on the use of hydrogen generated from renewable sources for energy generation and storage systems. Different strategies for producing, storing, and transporting environmentally friendly hydrogen will be covered in the first section of the examination. A critical analysis and choice of production method was studied. The study focused on the problems with transporting and storing hydrogen with a view to suggest solutions. The use of hydrogen in reducing carbon emissions was reviewed and an analysis of selected case studies was done with an emphasis on how hydrogen's carbon footprint varies depending on the gas source.

关键词： Temperature Wind energy Reviews Oils Production Green hydrogen Air pollution

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Prediction of New Associations between ncRNAs and Diseases Exploiting Multi-Type Hierarchical Clustering 28

Prediction of New Associations between ncRNAs and Diseases E...

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28th Italian Symposium on Advanced database Systems, SEBD 2020

作者： Barracchia, Emanuele Pio Pio, Gianvito D'Elia, Domenica Ceci, Michelangelo Dept. of Computer Science University of Bari Aldo Moro Bari Italy Dept. of Computer Science University of Bari Aldo Moro Bari Italy Big Data Laboratory Rome Italy Dept. of Knowledge Technologies Jouef Stefan Institute Ljubljana Slovenia

The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks or can exploit only a limited set of the features of biological entities. To overcome the limitations of existing approaches, we proposed the system LP-HCLUS, which analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts hierarchically organized, possibly overlapping, multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. Our experimental evaluation shows that, according to both quantitative (i.e., TPR@k, ROC and PR curves) and qualitative criteria, LP-HCLUS produces better results. Copyright © 2020 for this paper by its authors.

关键词： RNA

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Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC

arXiv

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arXiv 2023年

作者： Xue, Ye Liu, An Li, Yang Shi, Qingjiang Lau, Vincent Shenzhen Research Institute of Big Data Shenzhen518172 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Tongji University Shanghai201804 China Dept. of ECE The Hong Kong University of Science and Technology Hong Kong

In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorizationminimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Channel estimation

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Measures of Organizational Process Performance in the Capability Maturity Model Integration

Measures of Organizational Process Performance in the Capabi...

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Information Technology (ACIT)

作者： Abdel-Rahman Al-Ghuwairi Dimah Al-Fraihat Yousef Sharrab Ahmed Kittaneh Ahmed Ali Ayoub Alsarhan Muhsen Alkhalidy Dept. of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Jordan Dept. of Software Engineering Faculty of Information Technology Isra University Jordan Dept. of Artificial Intelligence and Data Science Faculty of Information Technology Isra University Jordan Dept. of Cyber Security Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Jordan Dept. of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Jordan

The Capability Maturity Model Integration (CMMI) is widely utilized by organizations to facilitate process improvement. Among its 22 process areas, this paper focuses on the Organizational Process Performance (OPP) area, which assesses the effectiveness of a process. Specifically, this study examines one goal (establishing performance baselines and models) and two practices (establishing quality and process performance objectives and selecting processes within OPP. To measure these practices, the Goal Questions Metrics (GQM) approach is employed. data was collected using a questionnaire, and the defined measures are validated quantitatively. 250 responses were used for analysis. The results obtained indicate that implementing the defined measures in companies would be beneficial for achieving high-quality processes in OPP and advancing the maturity level of CMMI.

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Comparative Study on the Results of Student Assessment data Developed by SURE Model 15

Comparative Study on the Results of Student Assessment Data ...

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Senden, Delgermaa Shambaljamts, Tsooj Tudevdagva, Uranchimeg Reva, Ivan L. Mongolian University of Science and Technology Department of Mechanical Science Ulaanbaatar Mongolia Chemnitz University of Technology Computer Sceince Faculty Chemnitz Germany Novosibirsk State Technical University Faculty of Automation and Computer Engineering Dept. of Data Security Novosibirsk Russia

ISBN: (纸本)9781665434089

The Mongolian University of science and Technology collects information (data) related to teaching methods and learning environment using a brief questionnaire from each student each semester. The data collected via questionnaires should be used for analysis of education activities, as well as development of policy level measures directed to improve education quality and teaching methods. However, as was observed by teachers, there is still an insufficiency in development of collected data and actions taken based on the results. In order to conduct comparative study within the framework of this issue, one module was taught by the same teacher (Theoretical mechanics 2) and other three different subjects (Mathematics 1, Information Technology 1, Theoretical Mechanics 1) were chosen, which are selected by most of the students, and ultimately processed the results with the use of the SURE model of structure-based evaluation. In this article, a total of 66681 evaluation data were provided by students who completed general education courses, professional foundation courses and other specialized courses at three schools (School of Industrial Technology, School of Mechanical engineering and Transportation, School of Information and Telecommunication Technology) under the MUST, and these data were processed with the use of evaluation model mentioned above and the results were compared with the outcomes of previous study. © 2021 IEEE.

关键词： Students

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Measuring robustness in deep learning based Compressive sensing

arXiv

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arXiv 2021年

作者： Darestani, Mohammad Zalbagi Chaudhari, Akshay S. Heckel, Reinhard Dept. of Electrical and Computer Engineering Rice University Dept. of Radiology Dept. of Biomedical Data Science Stanford University Dept. of Electrical and Computer Engineering Technical University of Munich

Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that deep-learning-based image reconstruction methods are sensitive to perturbations and are less robust than traditional methods: Neural networks (i) may be sensitive to small, yet adversarially-selected perturbations, (ii) may perform poorly under distribution shifts, and (iii) may fail to recover small but important features in an image. In order to understand the sensitivity to such perturbations, in this work, we measure the robustness of different approaches for image reconstruction including trained and un-trained neural networks as well as traditional sparsity-based methods. We find, contrary to prior works, that both trained and un-trained methods are vulnerable to adversarial perturbations. Moreover, both trained and un-trained methods tuned for a particular dataset suffer very similarly from distribution shifts. Finally, we demonstrate that an image reconstruction method that achieves higher reconstruction quality, also performs better in terms of accurately recovering fine details. Our results indicate that the state-of-the-art deep-learning-based image reconstruction methods provide improved performance than traditional methods without compromising robustness. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

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