Pub Date : 2022-01-01DOI: 10.1109/UEMCON54665.2022.9965720
Kevin Matthe Caramancion
{"title":"Veteran Status as a Potent Determinant of Misinformation and Disinformation Cyber Risk","authors":"Kevin Matthe Caramancion","doi":"10.1109/UEMCON54665.2022.9965720","DOIUrl":"https://doi.org/10.1109/UEMCON54665.2022.9965720","url":null,"abstract":"","PeriodicalId":92155,"journal":{"name":"Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual","volume":"27 1","pages":"40-44"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76872424","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}
Pub Date : 2020-01-01DOI: 10.1109/UEMCON51285.2020.9298106
Edder Sebastian Mendoza Garibay, M. S. Ullah
{"title":"Analysis and Synthesis of Respiratory Rate for Male Patients","authors":"Edder Sebastian Mendoza Garibay, M. S. Ullah","doi":"10.1109/UEMCON51285.2020.9298106","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298106","url":null,"abstract":"","PeriodicalId":92155,"journal":{"name":"Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual","volume":"100 1","pages":"923-927"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80837806","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992920
Khalid H. Alnafisah
Researching and analyzing large and complex graphs is an important aspect of data visualization research, but completely new, scalable methods and graph visualization methodologies are required [49]. Overall, this can provide more insight into this fuzzy graph's structure and function. To clarify further, in the “Hair Balls” we need to find a technique to build a solution for presenting a clean graph with the minimum overlap between edges. Despite the growing importance of researching and thoroughly examining and interpreting very large data graphs, the traditional way of viewing graphs has trouble scaling up, and usually ends up representing such large graphs as “Hair Balls.” Nevertheless, this traditional approach has a profoundly intuitive foundation [75]: nodes are represented in a form such as a circle, triangle or square, which are then bound by lines or curves representing the edges [73]. In any way, while there are many different methods of applying this fundamental underlying concept, it needs to be reconsidered in the given current and developing needs to consider the increasingly complex convergence between the edges in the graphs [55]. The Hair Ball complex, appearing as an indecipherable diagram, originated from the edge-to-edge convergence. We found the major drawback in the Hair Balls graph from our preliminary research was that it confused observers [38]–[40]. Users might feel that there are some extra nodes; but they don't actually exist. Since there are many crossovers in the Hair Balls between the edges, the impression can also affect observers ‘ understanding of the graph's entire structure [38] [39]. Major problem-no effective reception of information from a Hair Balls graph-meaningless to observers [64].
{"title":"An algorithmic Solution in Data Visualization for the \"Hair Ball\" Problem","authors":"Khalid H. Alnafisah","doi":"10.1109/UEMCON47517.2019.8992920","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992920","url":null,"abstract":"Researching and analyzing large and complex graphs is an important aspect of data visualization research, but completely new, scalable methods and graph visualization methodologies are required [49]. Overall, this can provide more insight into this fuzzy graph's structure and function. To clarify further, in the “Hair Balls” we need to find a technique to build a solution for presenting a clean graph with the minimum overlap between edges. Despite the growing importance of researching and thoroughly examining and interpreting very large data graphs, the traditional way of viewing graphs has trouble scaling up, and usually ends up representing such large graphs as “Hair Balls.” Nevertheless, this traditional approach has a profoundly intuitive foundation [75]: nodes are represented in a form such as a circle, triangle or square, which are then bound by lines or curves representing the edges [73]. In any way, while there are many different methods of applying this fundamental underlying concept, it needs to be reconsidered in the given current and developing needs to consider the increasingly complex convergence between the edges in the graphs [55]. The Hair Ball complex, appearing as an indecipherable diagram, originated from the edge-to-edge convergence. We found the major drawback in the Hair Balls graph from our preliminary research was that it confused observers [38]–[40]. Users might feel that there are some extra nodes; but they don't actually exist. Since there are many crossovers in the Hair Balls between the edges, the impression can also affect observers ‘ understanding of the graph's entire structure [38] [39]. Major problem-no effective reception of information from a Hair Balls graph-meaningless to observers [64].","PeriodicalId":92155,"journal":{"name":"Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual","volume":"13 1","pages":"408-418"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73279084","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}
Pub Date : 2016-10-01Epub Date: 2016-12-12DOI: 10.1109/UEMCON.2016.7777912
Xinyu Li, Yanyi Zhang, Mengzhu Li, Shuhong Chen, Farneth R Austin, Ivan Marsic, Randall S Burd
We present a multimodal deep-learning structure that automatically predicts phases of the trauma resuscitation process in real-time. The system first pre-processes the audio and video streams captured by a Kinect's built-in microphone array and depth sensor. A multimodal deep learning structure then extracts video and audio features, which are later combined through a "slow fusion" model. The final decision is then made from the combined features through a modified softmax classification layer. The model was trained on 20 trauma resuscitation cases (>13 hours), and was tested on 5 other cases. Our results showed over 80% online detection accuracy with 0.7 F-Score, outperforming previous systems.
{"title":"Online Process Phase Detection Using Multimodal Deep Learning.","authors":"Xinyu Li, Yanyi Zhang, Mengzhu Li, Shuhong Chen, Farneth R Austin, Ivan Marsic, Randall S Burd","doi":"10.1109/UEMCON.2016.7777912","DOIUrl":"https://doi.org/10.1109/UEMCON.2016.7777912","url":null,"abstract":"<p><p>We present a multimodal deep-learning structure that automatically predicts phases of the trauma resuscitation process in real-time. The system first pre-processes the audio and video streams captured by a Kinect's built-in microphone array and depth sensor. A multimodal deep learning structure then extracts video and audio features, which are later combined through a \"slow fusion\" model. The final decision is then made from the combined features through a modified softmax classification layer. The model was trained on 20 trauma resuscitation cases (>13 hours), and was tested on 5 other cases. Our results showed over 80% online detection accuracy with 0.7 F-Score, outperforming previous systems.</p>","PeriodicalId":92155,"journal":{"name":"Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual","volume":"2016 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/UEMCON.2016.7777912","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36603961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01Epub Date: 2016-12-12DOI: 10.1109/UEMCON.2016.7777899
Mohammad Shekaramiz, Todd K Moon, Jacob H Gunther
Recently, we proposed an algorithm for the single measurement vector problem where the underlying sparse signal has an unknown clustered pattern. The algorithm is essentially a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. Treating the cluster pattern is controlled via a knob that accounts for the amount of clumpiness in the solution. The parameter corresponding to the knob is learned using expectation-maximization algorithm. In this paper, we provide further study by comparing the performance of our algorithm with other algorithms in terms of support recovery, mean-squared error, and an example in image reconstruction in a compressed sensing fashion.
{"title":"AMP-B-SBL: An algorithm for clustered sparse signals using approximate message passing.","authors":"Mohammad Shekaramiz, Todd K Moon, Jacob H Gunther","doi":"10.1109/UEMCON.2016.7777899","DOIUrl":"https://doi.org/10.1109/UEMCON.2016.7777899","url":null,"abstract":"<p><p>Recently, we proposed an algorithm for the single measurement vector problem where the underlying sparse signal has an unknown clustered pattern. The algorithm is essentially a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. Treating the cluster pattern is controlled via a knob that accounts for the amount of clumpiness in the solution. The parameter corresponding to the knob is learned using expectation-maximization algorithm. In this paper, we provide further study by comparing the performance of our algorithm with other algorithms in terms of support recovery, mean-squared error, and an example in image reconstruction in a compressed sensing fashion.</p>","PeriodicalId":92155,"journal":{"name":"Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual","volume":"2016 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/UEMCON.2016.7777899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35640985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：