Vikram Nagapudi, ArchBishop Mitty, Ameeta Agrawal, N. Bulusu
{"title":"Extracting Physical Events from Digital Chatter for Covid-19","authors":"Vikram Nagapudi, ArchBishop Mitty, Ameeta Agrawal, N. Bulusu","doi":"10.1109/SMARTCOMP52413.2021.00082","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00082","url":null,"abstract":"By June 3, 2021, the US experienced over 33 million total cases of Covid-19, surpassing 592,000 deaths. In response, the Centers for Disease Control and Prevention (CDC) advised masking, social distancing and avoiding mass gatherings. In this work, we seek to automatically identify physical mass gathering events including dates and locations from digital chatter, i.e., social media data. We also study spread and sentiment associated with such large gathering events, finding a moderate negative correlation between large public gatherings, overall sentiment, and reported Covid-19 case numbers post event.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116064690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On Realizing Stateful FaaS in Serverless Edge Networks: State Propagation","authors":"C. Cicconetti, M. Conti, A. Passarella","doi":"10.1109/SMARTCOMP52413.2021.00033","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00033","url":null,"abstract":"In this paper, we address the problem of supporting chains of stateful function invocations following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios, such as wide-area Internet of Things (IoT) networks, and we propose three different solutions: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We show via simulation that StateLocal, by applying the data locality principle, can significantly enhance the performance by reducing the application delay due to data transfer and keeping a lower traffic volume in the network. This study sheds light on some aspects within the unexplored area of stateful FaaS, which is very promising among the edge computing technologies and has several open research directions associated.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mutual Reinforcement Learning with Heterogenous Agents","authors":"Cameron Reid, S. Mukhopadhyay","doi":"10.1109/SMARTCOMP52413.2021.00081","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00081","url":null,"abstract":"Mutual learning is an emerging technique for allowing intelligent systems to learn from each other, giving rise to improved performance. In this paper, we explore mutual reinforcement learning between systems which use very different learning algorithms. In particular, we present an algorithm which allows two agents, one using Q-learning and another using adaptive dynamic programming, to share learned knowledge. We discuss how these agents negotiate the relative importance of knowledge they receive from other agents, and we present results that show how this affects the learning process.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"18 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134034940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Bria, L. Ferrigno, L. Gerevini, C. Marrocco, M. Molinara, P. Bruschi, M. Cicalini, G. Manfredini, Andrea Ria, G. Cerro, R. Simmarano, Giovanni Teolis, M. Vitelli
{"title":"A False Positive Reduction System For Continuous Water Quality Monitoring","authors":"A. Bria, L. Ferrigno, L. Gerevini, C. Marrocco, M. Molinara, P. Bruschi, M. Cicalini, G. Manfredini, Andrea Ria, G. Cerro, R. Simmarano, Giovanni Teolis, M. Vitelli","doi":"10.1109/SMARTCOMP52413.2021.00065","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00065","url":null,"abstract":"Water monitoring systems continuously working ensure real–time pollutant detection capabilities according to their sensitivity and specificity. It is necessary to balance such features because, although being able to sense several substances is a desired feature, the reduction of false positives is a primary goal a classification system should have. High false positive makes the system unusable. The current solution enables a 24/7 service with a sampling rate equal to 0.6 Hz. Our goal is to limit false positives to 1 per day, thus achieving 99.99% accuracy at least. In this paper, we add a false positive reduction module to our pre-existent system, aiming to manage false positive boosters as sensor drift and signal oscillations. Obtained results, using a Multi Layer Perceptron classifier, confirm the false positive reduction while keeping high true positive rates.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":" 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120832070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Efficient Edge Deep Learning Computer Vision System to Prevent Sudden Infant Death Syndrome","authors":"Vivek Bharati","doi":"10.1109/SMARTCOMP52413.2021.00061","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00061","url":null,"abstract":"Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors, also called an \"outside stressor,\" that is responsible for Sudden Infant Death Syndrome (SIDS), is the sleeping position of the baby. According to past research, the risk of SIDS increases when the baby sleeps facedown on the stomach. We propose a Convolutional Neural Network (CNN) based computer-vision system that estimates the sleeping pose of the baby and alerts caregivers on their mobile phones within a few seconds of the baby moving to the hazardous facedown sleeping position. The system has a low computational load and a low memory footprint. This characteristic allows the system to be embedded in low power edge devices such as certain baby monitors. Processing at the edge also alleviates privacy concerns with regards to sending images into the network. We experimented with various numbers of convolutional processing units and dense layers as well as the number of convolutional kernels to arrive at the optimal production configuration. We observed a consistently high accuracy of detection of infant sleeping position changes from turning to facedown positions with a potential towards even higher accuracies with caregiver feedback for model retraining. Therefore, this system is a viable candidate for consideration as a non-intrusive solution to assist in preventing SIDS.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117050166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"PhD Forum Abstract: I/Ocloud: Adopting the IaaS Paradigm in the Internet of Things","authors":"Zakaria Benomar","doi":"10.1109/SMARTCOMP52413.2021.00087","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00087","url":null,"abstract":"In most of the actual Internet of Things (IoT) deployments, data processing/management tasks are often delegated to Cloud-based platforms. In such scenarios, the IoT applications’ developers see the IoT infrastructure as a mere data provider. To challenge the mainstream consensus on the relationship between the Cloud and IoT and provide flexible, shareable, and reconfigurable IoT deployments, we aim to expand the benefits of the Cloud Infrastructure-as-a-Service (IaaS) paradigm to the IoT world. In particular, our view is to adapt the Cloud metaphors to IoT by merging the two ecosystems at the broadest level. The IoT infrastructure can be seen then as a natural extension of a datacenter by abstracting their hosted I/O resources (e.g., sensors and actuators) through file system virtualization.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129509233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}