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Proceedings of the Winter Simulation Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Distributed Systems and Multimedia Processing (DSMP) Lab Department of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798350369663

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.

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The Parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named Parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Exploiting Open Data for Co2 Estimation Via Artificial Intelligence and Explainable Ai

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SSRN 2024年

作者： Bilotta, Stefano Palesi, Luciano Alessandro Ipsaro Nesi, Paolo University of Florence Department of Mathematics and Computer Science "U. Dini" Firenze50134 Italy University of Florence Distributed Systems and Internet Technology DISIT Lab Firenze50139 Italy

In present paper, a general CO2 emission estimation approach is developed by means of a large range of open data as predictions. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine (or deep) learning approaches and taking into account an innovative study based on open data sources including city services. The goal is to understand how specific predictors can contribute to or mitigate CO2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the key factors influencing emissions. Thus, a formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solution and model have been improved to be more flexible by using transfer learning techniques. This study and its related results have been produced and validated by exploiting the Snap4City framework for smart city, mobility and transport and data analytics. In particular, such results deal with the metropolitan cities of Florence and Bologna. © 2024, The Authors. All rights reserved.

关键词： Open Data

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Emotion Classification Through Speech Data Analysis

Emotion Classification Through Speech Data Analysis

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Simulation Winter Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Ontario Canada Department of Computer Science Distributed Systems and Multimedia Processing (DSMP) Lab Toronto Metropolitan University Ontario Canada

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Alain Hennebelle Qifan Dieng Leila Ismail Rajkumar Buyya Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology Emirates Center for Mobility Research National Water and Energy Center UAE University Al-Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

关键词： Cloud computing Accuracy Smart cities Computational modeling Transportation industry Surveillance Machine learning Predictive models Diabetes Time factors

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on Software Engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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Securing Digital Ownership Using Non-Fungible Tokens(NFTs), an Application of BlockChain Technology 7th

Securing Digital Ownership Using Non-Fungible Tokens(NFTs), ...

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Harbola, Suhas Yadav, Jyotsna Johari, Rahul Verma, Ekta Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Sector-16C Delhi Dwarka India Guru Gobind Singh Indraprastha University Sector-16C Delhi Dwarka India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India National Informatics Centre New Delhi India

ISBN: (纸本)9789811997181

Non-Fungible Token(NFT) is the latest tech-jargon buzzing in IT Industry. The continuous work in the area of securing contents with BlockChain has given rise to Non-Fungible Tokens(NFTs). One of the major problems of digital assets is the claim of ownership, as to how to verify ownership of digital assets (like audios, music videos, digital art, streaming contents on the web, etc.) which is accessed/copied by billions. NFTs are unique and distinct from each other thus securing the ownership. These tokens are used to represent a digital asset and contain a proof of ownership. As the BlockChain technology grows, its applications have been designed, developed and deployed in a variety of industries. In this research, we intend to give an idea of NFT and related technology. Not only this, an effort has been made to explore Indian NFT marketplaces, and the process of selling and buying the NFTs has also been discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Measurement and analysis of handover latencies in IEEE 802.11i secured networks 13

Measurement and analysis of handover latencies in IEEE 802.1...

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13th European Wireless Conference: Enabling Technologies for Wireless Multimedia Communications, EW 2007

作者： Martinovic, Ivan Zdarsky, Frank A. Bachorek, Adam Schmitt, Jens B. Disco Distributed Computer Systems Lab University of Kaiserslautern Germany

The purpose of the IEEE 802.11i standard is to endue wireless networks with advanced security by leveraging mature and proven security technologies. The concept of a Robust Secure Network (RSN) as a long-term security architecture was defined in order to provide confidentiality of data being transferred over the wireless medium as well as to provide mutual authentication between mobile stations and the network infrastructure. Nonetheless, security provisioning is indubitable time and resource consuming, which poses a problem as far as meeting quality of service demands of forthcoming delay-critical applications (e.g. Voice over WLAN) is concerned. The main objective of this research is to measure and analyze how currently deployed mobile devices perform when joining the RSN as defined by the IEEE 802.11i security amendment. Furthermore, we investigate various state-of-the-art implementations of IEEE 802.11i among different mobile devices providing the answer to what to expect on performance sacrifice by utilizing link-layer protection of IEEE 802.11i. As a result of this work, we find that the price to pay for the IEEE 802.11 security greatly varies among different devices, starting from low latencies such as ≈19 ms up to ≈330 ms and interestingly, computational stronger clients are not a priori the winners. © EW 2007 - 13th European Wireless Conference: Enabling Technologies for Wireless Multimedia Communications. All rights reserved.

关键词： Quality of service

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