The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean mo...The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering.展开更多
International social work is facing an exponentially growing number of critical challenges in developing nations in meeting essential social needs as well as the distribution of material goods, social and economic res...International social work is facing an exponentially growing number of critical challenges in developing nations in meeting essential social needs as well as the distribution of material goods, social and economic resources and the provision, and effective services delivery to populations of need. There is a need to respond rapidly to populations in need and adapt to quickly changing socio-economic conditions. However, the resources needed to evaluate data and make effective decisions to provide support are often limited by the availability of trained data analysts who can interpret statistical findings and make relevant service delivery decisions for such communities of need. Modern computing technology shows promise of providing a graphical evaluation approach to enriching understanding of the available information. Thereby, the need to make critical decisions is augmented for those decision-makers that are working most closely with marginalized groups. A recent research effort undertaken by the Adventist Development and Relief Agency (ADRA) sought to provide administrators and policy-makers with information regarding the associations between social capital, economic development, and food security in the mountain region of Perú. Survey data collection and descriptive statistical analysis were used to provide information on the relationship between socio-economic development services and the emergence of social capital needed to develop effective human self-support systems. This paper demonstrates how to provide a graphical interface between areas of economic need and development initiatives. A visual exploration of data patterns would serve to inform human service decision-makers as they develop policies and programs. Visualization of data helps to enrich meaning and understanding of core areas of population need. This enhanced information format can identify needs for social policy development and improve the delivery of critical services and resources. This paper will provide a conceptual argument for considering data visualization methods in the evaluation of significant needs for, at risk, international populations. The paper will also present a number of examples for improving data evaluation and the understanding of need through data visualization methods through widely available computing technology. Procedures for graphically representing data will be demonstrated using the Perú survey, and applications for social policy formulation will be discussed.展开更多
With the rapid advancement of artificial intelligence,research on enabling computers to assist humans in achieving intelligent augmentation-thereby enhancing the accuracy and efficiency of information perception and p...With the rapid advancement of artificial intelligence,research on enabling computers to assist humans in achieving intelligent augmentation-thereby enhancing the accuracy and efficiency of information perception and processing-has been steadily evolving.Among these developments,innovations in human motion capture technology have been emerging rapidly,leading to an increasing diversity in motion capture data types.This diversity necessitates the establishment of a unified standard for multi-source data to facilitate effective analysis and comparison of their capability to represent human motion.Additionally,motion capture data often suffer from significant noise,acquisition delays,and asynchrony,making their effective processing and visualization a critical challenge.In this paper,we utilized data collected from a prototype of flexible fabric-based motion capture clothing and optical motion capture devices as inputs.Time synchronization and error analysis between the two data types were conducted,individual actions from continuous motion sequences were segmented,and the processed results were presented through a concise and intuitive visualization interface.Finally,we evaluated various system metrics,including the accuracy of time synchronization,data fitting error from fabric resistance to joint angles,precision of motion segmentation,and user feedback.展开更多
Data flow diagram(DFD),as a special kind of data,is a design artifact in both requirement analysis and structured analysis in software development.However,rigorous analysis of DFD requires a formal semantics.Formal re...Data flow diagram(DFD),as a special kind of data,is a design artifact in both requirement analysis and structured analysis in software development.However,rigorous analysis of DFD requires a formal semantics.Formal representation of DFD and its formal semantics will help to reduce inconsistencies and confusion.The logical structure of DFD can be described using formalism of Calculus of Communicating System(CCS).With a finite number of states based on CCS,state space methods will help a lot in analysis and verification of the behavior of the systems.But the number of states of even a relatively small system is often very great that is called state explosion.In this paper,we present a visual system which combines Formal methods and visualization techniques so as to help the researchers to understand and analyze the system described by the DFD regardless of the problem of state explosion.展开更多
Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data cl...Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering results.Design/methodology/approach-Let“N”be the number of tweets documents for the topics extraction.Unwanted texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing step.Bag-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics extraction.Approximation of topics features are extracted for every tweet document.These set of topics features of N documents are treated as multi-viewpoints.The key idea of the proposed work is to use multi-viewpoints in the similarity features computation.The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and v5.For example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine metric.Findings-Healthcare problems with tweets data.Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled tweets.Originality/value-Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.展开更多
Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challen...Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.展开更多
基金supported by the Natural Science Foundation of China under Project 41076115the Global Change Research Program of China under project 2012CB955603the Public Science and Technology Research Funds of the Ocean under project 201005019
文摘The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering.
文摘International social work is facing an exponentially growing number of critical challenges in developing nations in meeting essential social needs as well as the distribution of material goods, social and economic resources and the provision, and effective services delivery to populations of need. There is a need to respond rapidly to populations in need and adapt to quickly changing socio-economic conditions. However, the resources needed to evaluate data and make effective decisions to provide support are often limited by the availability of trained data analysts who can interpret statistical findings and make relevant service delivery decisions for such communities of need. Modern computing technology shows promise of providing a graphical evaluation approach to enriching understanding of the available information. Thereby, the need to make critical decisions is augmented for those decision-makers that are working most closely with marginalized groups. A recent research effort undertaken by the Adventist Development and Relief Agency (ADRA) sought to provide administrators and policy-makers with information regarding the associations between social capital, economic development, and food security in the mountain region of Perú. Survey data collection and descriptive statistical analysis were used to provide information on the relationship between socio-economic development services and the emergence of social capital needed to develop effective human self-support systems. This paper demonstrates how to provide a graphical interface between areas of economic need and development initiatives. A visual exploration of data patterns would serve to inform human service decision-makers as they develop policies and programs. Visualization of data helps to enrich meaning and understanding of core areas of population need. This enhanced information format can identify needs for social policy development and improve the delivery of critical services and resources. This paper will provide a conceptual argument for considering data visualization methods in the evaluation of significant needs for, at risk, international populations. The paper will also present a number of examples for improving data evaluation and the understanding of need through data visualization methods through widely available computing technology. Procedures for graphically representing data will be demonstrated using the Perú survey, and applications for social policy formulation will be discussed.
基金supported by National Natural Science Foun-dation of China(62072383,61702433)the Public Technology Service Platform Project of Xiamen City(No.3502Z20231043)+2 种基金Xiaomi Young Talents Program/Xiaomi Foundation,the Funda-mental Research Funds for the Central Universities,Chinasupported by National Natural Science Founda-tion of China(62077039)the Fundamental Research Funds for the Central Universities,China(20720230106).
文摘With the rapid advancement of artificial intelligence,research on enabling computers to assist humans in achieving intelligent augmentation-thereby enhancing the accuracy and efficiency of information perception and processing-has been steadily evolving.Among these developments,innovations in human motion capture technology have been emerging rapidly,leading to an increasing diversity in motion capture data types.This diversity necessitates the establishment of a unified standard for multi-source data to facilitate effective analysis and comparison of their capability to represent human motion.Additionally,motion capture data often suffer from significant noise,acquisition delays,and asynchrony,making their effective processing and visualization a critical challenge.In this paper,we utilized data collected from a prototype of flexible fabric-based motion capture clothing and optical motion capture devices as inputs.Time synchronization and error analysis between the two data types were conducted,individual actions from continuous motion sequences were segmented,and the processed results were presented through a concise and intuitive visualization interface.Finally,we evaluated various system metrics,including the accuracy of time synchronization,data fitting error from fabric resistance to joint angles,precision of motion segmentation,and user feedback.
文摘Data flow diagram(DFD),as a special kind of data,is a design artifact in both requirement analysis and structured analysis in software development.However,rigorous analysis of DFD requires a formal semantics.Formal representation of DFD and its formal semantics will help to reduce inconsistencies and confusion.The logical structure of DFD can be described using formalism of Calculus of Communicating System(CCS).With a finite number of states based on CCS,state space methods will help a lot in analysis and verification of the behavior of the systems.But the number of states of even a relatively small system is often very great that is called state explosion.In this paper,we present a visual system which combines Formal methods and visualization techniques so as to help the researchers to understand and analyze the system described by the DFD regardless of the problem of state explosion.
文摘Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering results.Design/methodology/approach-Let“N”be the number of tweets documents for the topics extraction.Unwanted texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing step.Bag-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics extraction.Approximation of topics features are extracted for every tweet document.These set of topics features of N documents are treated as multi-viewpoints.The key idea of the proposed work is to use multi-viewpoints in the similarity features computation.The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and v5.For example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine metric.Findings-Healthcare problems with tweets data.Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled tweets.Originality/value-Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.
基金support from the Cyber Technology Institute(CTI)at the School of Computer Science and Informatics,De Montfort University,United Kingdom,along with financial assistance from Universiti Tun Hussein Onn Malaysia and the UTHM Publisher’s office through publication fund E15216.
文摘Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.
