The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex d...The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.展开更多
Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. On...Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.展开更多
Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML syste...Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML systems are black boxes that are presented to users,while the human cognition represents a second black box that can be difficult for the algorithm to interpret.These black boxes create cognitive gaps between the user and the interactive ML model.In this paper,we identify several cognitive gaps that exist in a previously-developed interactive visual analytics(VA)system,Andromeda,but are also representative of common problems in other VA systems.Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system.These include designing new visual features to help people better understand how Andromeda processes and interacts with data,as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process.We evaluate our designs through both qualitative and quantitative analysis,and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.展开更多
Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and education.Its decentralized,secure,and transparent nature ma...Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and education.Its decentralized,secure,and transparent nature makes it ideal for use in industries where trust,security,and efficiency are of paramount importance.The integration of blockchain technology into the education system has the potential to greatly improve the efficiency,security,and credibility of the educational process.By creating secure and transparent platforms for tracking and verifying students’academic achievements,blockchain technology can help to create a more accessible and trustworthy education system,making it easier for students to showcase their skills and knowledge to potential employers.While the potential benefits of blockchain in education are significant,there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational sector.Some of the major challenges include adoption,technical knowledge,interoperability,regulation,cost,data privacy and security,scalability,and accessibility.The necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative technology.Organizations should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain network.Blockchain technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and organizations.Organizations should consider incorporating blockchain technology into their sustainability education programs,in order to enhance the transparency,verifiability,and efficiency of their sustainability-related activities.While the use of blockchain technology in education is still in its early stages,the available data suggest that it has significant potential to transform the education sector and improve the efficiency and transparency of educational systems.展开更多
文摘The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.
文摘Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.
基金This work was supported in part by NSF grant CSSI-2003387 and NSF I/UCRC CNS-1822080 via the NSF Center for Space,Highperformance,and Resilient Computing(SHREC).
文摘Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML systems are black boxes that are presented to users,while the human cognition represents a second black box that can be difficult for the algorithm to interpret.These black boxes create cognitive gaps between the user and the interactive ML model.In this paper,we identify several cognitive gaps that exist in a previously-developed interactive visual analytics(VA)system,Andromeda,but are also representative of common problems in other VA systems.Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system.These include designing new visual features to help people better understand how Andromeda processes and interacts with data,as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process.We evaluate our designs through both qualitative and quantitative analysis,and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.
基金the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.:GRANT3,029].
文摘Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and education.Its decentralized,secure,and transparent nature makes it ideal for use in industries where trust,security,and efficiency are of paramount importance.The integration of blockchain technology into the education system has the potential to greatly improve the efficiency,security,and credibility of the educational process.By creating secure and transparent platforms for tracking and verifying students’academic achievements,blockchain technology can help to create a more accessible and trustworthy education system,making it easier for students to showcase their skills and knowledge to potential employers.While the potential benefits of blockchain in education are significant,there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational sector.Some of the major challenges include adoption,technical knowledge,interoperability,regulation,cost,data privacy and security,scalability,and accessibility.The necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative technology.Organizations should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain network.Blockchain technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and organizations.Organizations should consider incorporating blockchain technology into their sustainability education programs,in order to enhance the transparency,verifiability,and efficiency of their sustainability-related activities.While the use of blockchain technology in education is still in its early stages,the available data suggest that it has significant potential to transform the education sector and improve the efficiency and transparency of educational systems.
