Pub Date : 2022-01-01DOI: 10.1109/MSMC.2021.3103498
Syed Moshfeq Salaken, Imali T. Hettiarachchi, Afsana Ahmed Munia, M. Hasan, A. Khosravi, Shady M. K. Mohamed, Ashikur Rahman
Understanding cognitive load is important due to its inherent implications across many different disciplines. This is, in general, a difficult task due to personal nature of data normally used to infer cognitive load. In addition, an individual changes over time and his/her pattern of data changes as well, which implies past data from an individual may not reliably predict the future cognitive load of the same individual. In this article, we show that utilization of data from other people (a.k.a. crowdsourcing) offers a significant improvement in classifier performance when predicting cognitive load. We reveal that the improvement is substantial compared to an individualistic model and is statistically significant.
{"title":"Predicting Cognitive Load of an Individual With Knowledge Gained From Others: Improvements in Performance Using Crowdsourcing","authors":"Syed Moshfeq Salaken, Imali T. Hettiarachchi, Afsana Ahmed Munia, M. Hasan, A. Khosravi, Shady M. K. Mohamed, Ashikur Rahman","doi":"10.1109/MSMC.2021.3103498","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3103498","url":null,"abstract":"Understanding cognitive load is important due to its inherent implications across many different disciplines. This is, in general, a difficult task due to personal nature of data normally used to infer cognitive load. In addition, an individual changes over time and his/her pattern of data changes as well, which implies past data from an individual may not reliably predict the future cognitive load of the same individual. In this article, we show that utilization of data from other people (a.k.a. crowdsourcing) offers a significant improvement in classifier performance when predicting cognitive load. We reveal that the improvement is substantial compared to an individualistic model and is statistically significant.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"12 1","pages":"4-15"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78684829","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 : 2022-01-01DOI: 10.1109/MSMC.2021.3097982
Arif Jahangir, Kavyan Tirdad, Alex Dela Cruz, Alireza Sadeghian, Michael Cusimano
The objective of the work presented in this article is to investigate the applicability of lightweight machine learning (ML) algorithms capable of detecting and forecasting hypertensive (HT) episodes from historical intracranial pressure (ICP) signals. Specifically, we aim at identifying noncomputationally dependent algorithms, which can be supported by lightweight hardware such as medical monitoring devices. We also propose applicable algorithms, which can be trained with a limited number of labeled samples due to the unfeasibility of manually labeling large volumes of ICP signals in most instances.
{"title":"An Application of Machine Learning to Forecast Hypertension Signals in Intracranial Pressure: A Comparison of Various Algorithms","authors":"Arif Jahangir, Kavyan Tirdad, Alex Dela Cruz, Alireza Sadeghian, Michael Cusimano","doi":"10.1109/MSMC.2021.3097982","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3097982","url":null,"abstract":"The objective of the work presented in this article is to investigate the applicability of lightweight machine learning (ML) algorithms capable of detecting and forecasting hypertensive (HT) episodes from historical intracranial pressure (ICP) signals. Specifically, we aim at identifying noncomputationally dependent algorithms, which can be supported by lightweight hardware such as medical monitoring devices. We also propose applicable algorithms, which can be trained with a limited number of labeled samples due to the unfeasibility of manually labeling large volumes of ICP signals in most instances.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"76 1","pages":"29-38"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83859060","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 : 2022-01-01DOI: 10.1109/MSMC.2021.3100702
Anushri Rajendran, P. Kebria, N. Mohajer, A. Khosravi
Human spaceflight requires a perfectly balanced system of personality traits, coined “the right stuff,” in the space environment, which is so “wrong” for life that human physiology begins to disintegrate. Several factors, including personal experiences, upbringing, and training, influence the motivation, coping, and other unique personality traits of astronauts, which differentiate them from the civilian population and make them perfectly suited to high functionality in extreme conditions. To evaluate the creative potential of astronauts in coping with such a hostile environment, we need to study their psychology as part of a perfectly balanced system that places mindsets in codependence with space and altered physiology.
{"title":"The Right Stuff in the Right Place: Introducing the Four Pillars of Optimization That Support the Creative Potential of Astronauts During Human Spaceflight","authors":"Anushri Rajendran, P. Kebria, N. Mohajer, A. Khosravi","doi":"10.1109/MSMC.2021.3100702","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3100702","url":null,"abstract":"Human spaceflight requires a perfectly balanced system of personality traits, coined “the right stuff,” in the space environment, which is so “wrong” for life that human physiology begins to disintegrate. Several factors, including personal experiences, upbringing, and training, influence the motivation, coping, and other unique personality traits of astronauts, which differentiate them from the civilian population and make them perfectly suited to high functionality in extreme conditions. To evaluate the creative potential of astronauts in coping with such a hostile environment, we need to study their psychology as part of a perfectly balanced system that places mindsets in codependence with space and altered physiology.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"113 1","pages":"39-44"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83307665","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 : 2022-01-01DOI: 10.1109/msmc.2021.3116286
S. Nahavandi
{"title":"Examining Artificial Intelligence Applications [Editorial]","authors":"S. Nahavandi","doi":"10.1109/msmc.2021.3116286","DOIUrl":"https://doi.org/10.1109/msmc.2021.3116286","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"36 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85272483","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 : 2022-01-01DOI: 10.1109/MSMC.2021.3069145
T. Reddy, L. Behera
Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating RTs and drowsiness from electroencephalogram (EEG) signals was feasible only with moderate accuracy, which led to unreliability for neuro-engineering applications. However, recent developments in machine learning algorithms, notably stationarity-based approaches and deep convolutional neural networks (CNNs), have demonstrated promising results for a class of BCI systems, e.g., motor imagery BCIs, and affective state classification. These methods have not been systematically analyzed for EEG-based driver drowsiness detection and RT prediction.
{"title":"Driver Drowsiness Detection: An Approach Based on Intelligent Brain–Computer Interfaces","authors":"T. Reddy, L. Behera","doi":"10.1109/MSMC.2021.3069145","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3069145","url":null,"abstract":"Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating RTs and drowsiness from electroencephalogram (EEG) signals was feasible only with moderate accuracy, which led to unreliability for neuro-engineering applications. However, recent developments in machine learning algorithms, notably stationarity-based approaches and deep convolutional neural networks (CNNs), have demonstrated promising results for a class of BCI systems, e.g., motor imagery BCIs, and affective state classification. These methods have not been systematically analyzed for EEG-based driver drowsiness detection and RT prediction.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"2 1","pages":"16-28"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77852927","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 : 2021-10-01DOI: 10.1109/msmc.2021.3103218
P. Shi
{"title":"The Impact of IEEE Systems, Man, and Cybernetics Society Publications Is Increasing [Society News]","authors":"P. Shi","doi":"10.1109/msmc.2021.3103218","DOIUrl":"https://doi.org/10.1109/msmc.2021.3103218","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"40 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79053257","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 : 2021-10-01DOI: 10.1109/MSMC.2021.3098981
H. Gabbar, Sk Sami Al Jabar, Hassan A. Hassan, Jing Ren
This article presents an intelligent experience retention system (IERS), which is designed to overcome challenges and limitations of capturing human experience related to operating procedures for plant operation and maintenance in nuclear power plants. It is time-consuming to find specific information from thousands of input documents. Less experienced employees cannot operate complex tasks due to having less knowledge and training about the documents and their operation. Research gaps in current knowledge structuring and retrieval methods are discussed and used to identify essential features to achieve effective methods to manage instructive text (iText) related to learning and answering queries connected to operating procedures. Knowledge structure is proposed to represent inputs from documents, data, text, and voice related to operation and maintenance instructions in nuclear power plants. Human experience is captured and integrated within the structured knowledge in an integrated scheme, called the human experience semantic network (HESN), which includes deterministic, qualitative, and probabilistic parameters and attributes that are captured and dynamically tuned throughout the execution of the system.
{"title":"An Intelligent Experience Retention System: Challenges and Limitations for Operation and Maintenance in Nuclear Power Plants","authors":"H. Gabbar, Sk Sami Al Jabar, Hassan A. Hassan, Jing Ren","doi":"10.1109/MSMC.2021.3098981","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3098981","url":null,"abstract":"This article presents an intelligent experience retention system (IERS), which is designed to overcome challenges and limitations of capturing human experience related to operating procedures for plant operation and maintenance in nuclear power plants. It is time-consuming to find specific information from thousands of input documents. Less experienced employees cannot operate complex tasks due to having less knowledge and training about the documents and their operation. Research gaps in current knowledge structuring and retrieval methods are discussed and used to identify essential features to achieve effective methods to manage instructive text (iText) related to learning and answering queries connected to operating procedures. Knowledge structure is proposed to represent inputs from documents, data, text, and voice related to operation and maintenance instructions in nuclear power plants. Human experience is captured and integrated within the structured knowledge in an integrated scheme, called the human experience semantic network (HESN), which includes deterministic, qualitative, and probabilistic parameters and attributes that are captured and dynamically tuned throughout the execution of the system.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"12 1","pages":"31-34"},"PeriodicalIF":3.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79139895","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 : 2021-10-01DOI: 10.1109/msmc.2021.3103217
S. Nahavandi
{"title":"Applying Artificial Intelligence to Real-World Problems [Editorial]","authors":"S. Nahavandi","doi":"10.1109/msmc.2021.3103217","DOIUrl":"https://doi.org/10.1109/msmc.2021.3103217","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"42 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84984998","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 : 2021-10-01DOI: 10.1109/MSMC.2021.3090569
Yurui Ming, Chin-Teng Lin
The automatic feature-extraction capability of deep neural networks (DNNs) endows them with the potential for analyzing complicated electroencephalogram (EEG) data captured from brain functionality research. This article investigates the potential coherent correspondence between the region of interest (ROI) for DNNs to explore, and the ROI for conventional neurophysiological-oriented methods to work with, as exemplified in the case of a working memory study. The attention mechanism induced by global average pooling (GAP) is applied to a public EEG data set of a working memory test to unveil these coherent ROIs via a classification problem. The results show the potential alignment of the ROIs from different discipline methods, and consequently asserts the confidence and promise of utilizing DNNs for EEG data analysis.
{"title":"The Coherence of the Working Memory Study Between Deep Neural Networks and Neurophysiology: Insights From Distinguishing Topographical Electroencephalogram Data Under Different Workloads","authors":"Yurui Ming, Chin-Teng Lin","doi":"10.1109/MSMC.2021.3090569","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3090569","url":null,"abstract":"The automatic feature-extraction capability of deep neural networks (DNNs) endows them with the potential for analyzing complicated electroencephalogram (EEG) data captured from brain functionality research. This article investigates the potential coherent correspondence between the region of interest (ROI) for DNNs to explore, and the ROI for conventional neurophysiological-oriented methods to work with, as exemplified in the case of a working memory study. The attention mechanism induced by global average pooling (GAP) is applied to a public EEG data set of a working memory test to unveil these coherent ROIs via a classification problem. The results show the potential alignment of the ROIs from different discipline methods, and consequently asserts the confidence and promise of utilizing DNNs for EEG data analysis.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"os-17 1","pages":"24-30"},"PeriodicalIF":3.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87199847","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 : 2021-09-21DOI: 10.1109/MSMC.2021.3114139
Hafiz Majid Hussain, A. Narayanan, Subham S. Sahoo, Yongheng Yang, P. Nardelli, F. Blaabjerg
Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs wirelessly collect information from hardware installed in the power system and homes with the objective of intelligently and efficiently optimizing electricity usage and minimizing costs.
{"title":"Home Energy Management Systems: Operation and Resilience of Heuristics Against Cyberattacks","authors":"Hafiz Majid Hussain, A. Narayanan, Subham S. Sahoo, Yongheng Yang, P. Nardelli, F. Blaabjerg","doi":"10.1109/MSMC.2021.3114139","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3114139","url":null,"abstract":"Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs wirelessly collect information from hardware installed in the power system and homes with the objective of intelligently and efficiently optimizing electricity usage and minimizing costs.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"1 1","pages":"21-30"},"PeriodicalIF":3.2,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76191025","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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