Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00079
Ahmad Traboulsi, M. Barbeau
Drones are becoming a major element in defense applications, geographic surveillance, delivery of packages and their uses are expanding. Drone activity detection and identification have become an important research subject. An even more challenging problem is recognizing the intentions of a group of drones. Their intention may not be obvious, which might impose a security threat in several instances. Recognizing the targeted plan of a group of drones is the subject of study in this paper. We focus on identifying the formation a group of drones is trying to achieve. We predict the formation during the transition phase from one formation to another using softmax regression. We test several feature vector designs and present our results
{"title":"Recognition of Drone Formation Intentions Using Supervised Machine Learning","authors":"Ahmad Traboulsi, M. Barbeau","doi":"10.1109/CSCI49370.2019.00079","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00079","url":null,"abstract":"Drones are becoming a major element in defense applications, geographic surveillance, delivery of packages and their uses are expanding. Drone activity detection and identification have become an important research subject. An even more challenging problem is recognizing the intentions of a group of drones. Their intention may not be obvious, which might impose a security threat in several instances. Recognizing the targeted plan of a group of drones is the subject of study in this paper. We focus on identifying the formation a group of drones is trying to achieve. We predict the formation during the transition phase from one formation to another using softmax regression. We test several feature vector designs and present our results","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321996","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00127
N. Mijatovic, Rana Haber, G. Anagnostopoulos, Anthony O. Smith, A. Peter
Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds many regression methods. The present work details a novel method for estimating the Difference of Densities (DoD) between two distributions. This new method directly calculates the DD, in the form of a wavelet expansion, without the need for explicitly reconstructing individual distributions. Furthermore, the method applies a regularization technique that utilizes both l2 and l1 norm penalties to robustly estimate the coefficients of the wavelet expansion. Optimizing the regularized objective is accomplished via a Proximal Gradient Descent (PGD) approach. Thus, we term our method Regularized Wavelet-based Density-Difference (RWDD) with PGD. On extensive simulated datasets, from complex multimodal to skewed distributions, our method demonstrated superior performance in comparison to other contemporary techniques.
{"title":"A Proximal Algorithm for Estimating the Regularized Wavelet-Based Density-Difference","authors":"N. Mijatovic, Rana Haber, G. Anagnostopoulos, Anthony O. Smith, A. Peter","doi":"10.1109/CSCI49370.2019.00127","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00127","url":null,"abstract":"Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds many regression methods. The present work details a novel method for estimating the Difference of Densities (DoD) between two distributions. This new method directly calculates the DD, in the form of a wavelet expansion, without the need for explicitly reconstructing individual distributions. Furthermore, the method applies a regularization technique that utilizes both l2 and l1 norm penalties to robustly estimate the coefficients of the wavelet expansion. Optimizing the regularized objective is accomplished via a Proximal Gradient Descent (PGD) approach. Thus, we term our method Regularized Wavelet-based Density-Difference (RWDD) with PGD. On extensive simulated datasets, from complex multimodal to skewed distributions, our method demonstrated superior performance in comparison to other contemporary techniques.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"428 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131716392","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00145
Eamon P. Doherty, Paulette Laubsch, Elly Goei
This paper starts with a discussion of forensic imaging and its importance to robotics, computer forensics, and computer security. This is followed by a discussion of a computer science based robotics class that received favorable student opinion reports as well as accolades by the press. The class also inspired young women to pursue careers in STEM fields. Lastly we will discuss the need for project based lessons in STEM education and the need to increase the number of women in STEM fields in order to advance government, academia, and industry.
{"title":"An Example of Project Based Learning to Advance Women's Interest in STEM Education and Robotics","authors":"Eamon P. Doherty, Paulette Laubsch, Elly Goei","doi":"10.1109/CSCI49370.2019.00145","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00145","url":null,"abstract":"This paper starts with a discussion of forensic imaging and its importance to robotics, computer forensics, and computer security. This is followed by a discussion of a computer science based robotics class that received favorable student opinion reports as well as accolades by the press. The class also inspired young women to pursue careers in STEM fields. Lastly we will discuss the need for project based lessons in STEM education and the need to increase the number of women in STEM fields in order to advance government, academia, and industry.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282574","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00196
Masaya Hori, Hiroaki Inoue, Yu Kikuchi, Mayu Maeda, Yusuke Kobayashi, Takuya Kiryu, T. Tsubota, S. Shimizu
Visually impaired persons recognize their surrounding with a white cane or a guide dog while walking. This skill called "Orientation and Mobility" is difficult to learn. The training of the "Orientation and Mobility Skills" is performed at the school for visually impaired person. However, the evaluation of this skill is limited to subjective evaluation by teacher. We have proposed that quantitative evaluation of the "Orientation and Mobility Skills" is required. In this paper, we tried to execute the quantitative evaluation of the "Orientation and Mobility Skills" using brain activity measurements. In this experiment, brain activity was measured when subjects are walking in the corridor alone or with guide helper. Experimental subjects were sighted person who was blocked visual information during walking. The blood flow of prefrontal cortex was increased as the movement distance of the subject increased when subjects walk alone. From this result, it can be considered that the feeling of fear and the attention relayed to "Orientation and Mobility Skills" could be measured quantitatively by measuring human brain activities.
{"title":"Basic Study on Evaluation Method of Orientation and Mobility Skills Consideration for Visually Impaired Persons Based on Brain Activity","authors":"Masaya Hori, Hiroaki Inoue, Yu Kikuchi, Mayu Maeda, Yusuke Kobayashi, Takuya Kiryu, T. Tsubota, S. Shimizu","doi":"10.1109/CSCI49370.2019.00196","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00196","url":null,"abstract":"Visually impaired persons recognize their surrounding with a white cane or a guide dog while walking. This skill called \"Orientation and Mobility\" is difficult to learn. The training of the \"Orientation and Mobility Skills\" is performed at the school for visually impaired person. However, the evaluation of this skill is limited to subjective evaluation by teacher. We have proposed that quantitative evaluation of the \"Orientation and Mobility Skills\" is required. In this paper, we tried to execute the quantitative evaluation of the \"Orientation and Mobility Skills\" using brain activity measurements. In this experiment, brain activity was measured when subjects are walking in the corridor alone or with guide helper. Experimental subjects were sighted person who was blocked visual information during walking. The blood flow of prefrontal cortex was increased as the movement distance of the subject increased when subjects walk alone. From this result, it can be considered that the feeling of fear and the attention relayed to \"Orientation and Mobility Skills\" could be measured quantitatively by measuring human brain activities.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128838841","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00204
Naoshi Yakura, H. Higaki
In wireless multihop transmissions of data messages in wireless adhoc networks, it is surely assumed that both of a routing protocol and a data message transmission protocol work correctly in each intermediate wireless node. Until now, for flooding based ad-hoc routing protocols such as AODV in which routing and data message transmission functions are separately implemented, various methods for detection of malfunctioning intermediate nodes have been proposed. However, for most of location-based ad-hoc routing protocols such as GEDIR and GPSR in which routing and data message transmission functions are tightly combined, it is impossible for the proposed methods to be applied. This paper proposes a novel method for detection of malfunctioning intermediate nodes. Here, verification of location information advertised by 1-hop neighbor nodes and verification of data message transmissions by 1-hop neighbor nodes by cooperation among neighbor nodes sharing location information of their 2-hop neighbor wireless nodes are introduced.
{"title":"Extended Location-Based Ad-Hoc Routing with Erroneous/Malicious Intermediate Node Detection","authors":"Naoshi Yakura, H. Higaki","doi":"10.1109/CSCI49370.2019.00204","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00204","url":null,"abstract":"In wireless multihop transmissions of data messages in wireless adhoc networks, it is surely assumed that both of a routing protocol and a data message transmission protocol work correctly in each intermediate wireless node. Until now, for flooding based ad-hoc routing protocols such as AODV in which routing and data message transmission functions are separately implemented, various methods for detection of malfunctioning intermediate nodes have been proposed. However, for most of location-based ad-hoc routing protocols such as GEDIR and GPSR in which routing and data message transmission functions are tightly combined, it is impossible for the proposed methods to be applied. This paper proposes a novel method for detection of malfunctioning intermediate nodes. Here, verification of location information advertised by 1-hop neighbor nodes and verification of data message transmissions by 1-hop neighbor nodes by cooperation among neighbor nodes sharing location information of their 2-hop neighbor wireless nodes are introduced.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128858667","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00199
Jonathan Meredith, J. Straub, Ben Bernard
Anti-drone technologies that attack drone clusters or swarms autonomous command technologies may need to identify the type of command system being utilized and the various roles of particular UAVs within the system. This paper presents a set of algorithms to identify what swarm command method is being used and the role of particular drones within a swarm or cluster of UAVs utilizing only passive sensing techniques (which cannot be detected). A testing configuration for validating the algorithms is also discussed.
{"title":"Identifying UAV Swarm Command Methods and Individual Craft Roles Using Only Passive Sensing","authors":"Jonathan Meredith, J. Straub, Ben Bernard","doi":"10.1109/CSCI49370.2019.00199","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00199","url":null,"abstract":"Anti-drone technologies that attack drone clusters or swarms autonomous command technologies may need to identify the type of command system being utilized and the various roles of particular UAVs within the system. This paper presents a set of algorithms to identify what swarm command method is being used and the role of particular drones within a swarm or cluster of UAVs utilizing only passive sensing techniques (which cannot be detected). A testing configuration for validating the algorithms is also discussed.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404239","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00176
Y. Moriyama, Chonho Lee, S. Date, Y. Kashiwagi, Yuki Narukawa, K. Nozaki, Shinya Murakami
By exploring the feasibility of medical imaging applicable to periodontal disease, we have designed a MapReduce-like deep learning model for the severity assessment by estimating the pocket depth from oral images. However, deep learning typically relies on supervised training with a large annotated dataset, and medical data often faces an insufficiency in quantity and variety. Furthermore, obtaining patient data and annotating such data by experts still remain a challenge. To overcome the insufficiency in the data, we propose random cropping and GAN-based augmentation methods on tooth pocket region images extracted from oral images. We verify that the proposed methods successfully increase the number of training data and its variety, and these synthetic data contribute to improving the estimation accuracy from 78.3% to 84.5%, and sensitivity from 50.4% to 74.0%, with specificity of around 90%, compared to the MapReduce-like model without the augmentation.
{"title":"Evaluation of Dental Image Augmentation for the Severity Assessment of Periodontal Disease","authors":"Y. Moriyama, Chonho Lee, S. Date, Y. Kashiwagi, Yuki Narukawa, K. Nozaki, Shinya Murakami","doi":"10.1109/CSCI49370.2019.00176","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00176","url":null,"abstract":"By exploring the feasibility of medical imaging applicable to periodontal disease, we have designed a MapReduce-like deep learning model for the severity assessment by estimating the pocket depth from oral images. However, deep learning typically relies on supervised training with a large annotated dataset, and medical data often faces an insufficiency in quantity and variety. Furthermore, obtaining patient data and annotating such data by experts still remain a challenge. To overcome the insufficiency in the data, we propose random cropping and GAN-based augmentation methods on tooth pocket region images extracted from oral images. We verify that the proposed methods successfully increase the number of training data and its variety, and these synthetic data contribute to improving the estimation accuracy from 78.3% to 84.5%, and sensitivity from 50.4% to 74.0%, with specificity of around 90%, compared to the MapReduce-like model without the augmentation.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121111532","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00205
F. Tinetti, Oscar C. Valderrama Riveros, Fernando L. Romero
We are working on a (re) configurable general-purpose drone development. In this context, we are setting the basis for replacing the proprietary and traditional RC (Radio Control) receivers by other wireless communication devices. Thus, the communication device could be specifically defined for each application. We expect to be able to interact with standard drone flight controls (FC) as well as our own one/s. We show in this paper the general drone receiver proposal, the signals it handles, and a proof-of-concept Wi-Fi implementation. Furthermore, interacting with standard FC lets us to demonstrate that our proposal is not "tailored" to our own proprietary or specific FC.
{"title":"Unmanned Vehicles: Real Time Problems in Drone Receivers","authors":"F. Tinetti, Oscar C. Valderrama Riveros, Fernando L. Romero","doi":"10.1109/CSCI49370.2019.00205","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00205","url":null,"abstract":"We are working on a (re) configurable general-purpose drone development. In this context, we are setting the basis for replacing the proprietary and traditional RC (Radio Control) receivers by other wireless communication devices. Thus, the communication device could be specifically defined for each application. We expect to be able to interact with standard drone flight controls (FC) as well as our own one/s. We show in this paper the general drone receiver proposal, the signals it handles, and a proof-of-concept Wi-Fi implementation. Furthermore, interacting with standard FC lets us to demonstrate that our proposal is not \"tailored\" to our own proprietary or specific FC.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115274624","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00275
Leonardo Henrique Pereira, Carlos Nascimento Silla Junior, J. C. Nievola
With the rapid advancement of researches in the genomics and proteomic areas, the growth of bases with biological data was inevitable, making the analysis of these data a Herculean task for the human beings. Thus, it was indispensable the intervention of informatics to fulfill this need. Bioinformatics is used to analyze information in the field of biology using computer techniques. One of the problems of this area is the prediction of the protein functions, which is not so common because the analysis is very laborious and complex to treat, especially when there are classes with hierarchy, that is, their classes organized in super classes that inherit Protein functions of subclasses, forming structures of trees or directed acyclic graphs. The method presented here is based on the hierarchical classification of the protein function using machine learning algorithms, thus performing the prediction of protein functions. The novelty of this work lies in the study of feature selection approaches applied to different local-model hierarchical classification approaches. The results were obtained by conducting the experiments using the hierarchical mean and standard deviation, calculated through the correct rates that the hierarchical classification algorithms obtained. From the results found, comparisons were made between the hierarchical classification methods with and without the selection of attributes, thus proving that in the prediction scenario of the protein function, which have their classes in the hierarchical format, become much more favorable with the local hierarchical ranking approach per layer and not using attribute selection.
{"title":"Local Hierarchical Classification Techniques Analysis Using Attribute Selection for Protein Function Prediction","authors":"Leonardo Henrique Pereira, Carlos Nascimento Silla Junior, J. C. Nievola","doi":"10.1109/CSCI49370.2019.00275","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00275","url":null,"abstract":"With the rapid advancement of researches in the genomics and proteomic areas, the growth of bases with biological data was inevitable, making the analysis of these data a Herculean task for the human beings. Thus, it was indispensable the intervention of informatics to fulfill this need. Bioinformatics is used to analyze information in the field of biology using computer techniques. One of the problems of this area is the prediction of the protein functions, which is not so common because the analysis is very laborious and complex to treat, especially when there are classes with hierarchy, that is, their classes organized in super classes that inherit Protein functions of subclasses, forming structures of trees or directed acyclic graphs. The method presented here is based on the hierarchical classification of the protein function using machine learning algorithms, thus performing the prediction of protein functions. The novelty of this work lies in the study of feature selection approaches applied to different local-model hierarchical classification approaches. The results were obtained by conducting the experiments using the hierarchical mean and standard deviation, calculated through the correct rates that the hierarchical classification algorithms obtained. From the results found, comparisons were made between the hierarchical classification methods with and without the selection of attributes, thus proving that in the prediction scenario of the protein function, which have their classes in the hierarchical format, become much more favorable with the local hierarchical ranking approach per layer and not using attribute selection.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115310056","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00277
Kishwar Ahmed, Kazutomo Yoshii, S. Tasnim
High-performance computing (HPC) systems are large computing infrastructures, which consume massive amount of power during their operation. Power capping is a feature introduced in modern processor architecture to control application performance running on compute nodes. In this paper, we exploit power capping capability in the processors to develop a thermal-aware energy-efficient model for HPC systems. Our model optimizes energy consumption of HPC applications, while ensures processor temperature remains within a limit. We execute various HPC applications and measure different characteristics of execution (e.g., power, performance, temperature). Based on real-life measurements, we demonstrate that our proposed model is effective on achieving thermal-aware energy-efficiency for HPC systems.
{"title":"Thermal-Aware Power Capping Allocation Model for High Performance Computing Systems","authors":"Kishwar Ahmed, Kazutomo Yoshii, S. Tasnim","doi":"10.1109/CSCI49370.2019.00277","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00277","url":null,"abstract":"High-performance computing (HPC) systems are large computing infrastructures, which consume massive amount of power during their operation. Power capping is a feature introduced in modern processor architecture to control application performance running on compute nodes. In this paper, we exploit power capping capability in the processors to develop a thermal-aware energy-efficient model for HPC systems. Our model optimizes energy consumption of HPC applications, while ensures processor temperature remains within a limit. We execute various HPC applications and measure different characteristics of execution (e.g., power, performance, temperature). Based on real-life measurements, we demonstrate that our proposed model is effective on achieving thermal-aware energy-efficiency for HPC systems.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355133","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}
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