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"abstract": "The limitations of traditional Computer Aided Detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients lead to investigating Deep Learning methods (DL) for mammograms. Deep Learning, in particular, Convolutional Neural Networks (CNNs) have been recently used for object localization and detection, risk assessment, and classification tasks in mammogram images. CNNs help radiologists provide more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions in short time. This survey reviews the strengths and limitations of major CNNs applications in analyzing mammogram images. It summarizes 51 contributions on applying CNNs on various tasks in mammography. Moreover, it discusses the best practices done using CNN methods and suggesting directions for further improvement in medical images and in particular mammogram images.",
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"content": "The limitations of traditional Computer Aided Detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients lead to investigating Deep Learning methods (DL) for mammograms. Deep Learning, in particular, Convolutional Neural Networks (CNNs) have been recently used for object localization and detection, risk assessment, and classification tasks in mammogram images. CNNs help radiologists provide more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions in short time. This survey reviews the strengths and limitations of major CNNs applications in analyzing mammogram images. It summarizes 51 contributions on applying CNNs on various tasks in mammography. Moreover, it discusses the best practices done using CNN methods and suggesting directions for further improvement in medical images and in particular mammogram images.",
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"normalizedAbstract": "The limitations of traditional Computer Aided Detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients lead to investigating Deep Learning methods (DL) for mammograms. Deep Learning, in particular, Convolutional Neural Networks (CNNs) have been recently used for object localization and detection, risk assessment, and classification tasks in mammogram images. CNNs help radiologists provide more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions in short time. This survey reviews the strengths and limitations of major CNNs applications in analyzing mammogram images. It summarizes 51 contributions on applying CNNs on various tasks in mammography. Moreover, it discusses the best practices done using CNN methods and suggesting directions for further improvement in medical images and in particular mammogram images.",
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"affiliation": "Diagnostic Imaging, University of Connecticut Health Center, Farmington, CT, USA",
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"title": "Morphological Classification of Neurons Based Deep Residual Multiscale Convolutional Neural Network",
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"abstract": "The study of neuron morphological classification has important application value to improve the accuracy and efficiency of three-dimensional reconstruction of neurons. However, due to the complex structure of neurons and the existence of global and local self-similarity in morphological distribution, it brings great difficulties to the classification of neuron morphology. Therefore, a new neuronal morphological classification model based on deep residual multiscale convolutional neural network is proposed. Firstly, the overall architecture of the model is based on the fast connection idea of ResNet, which can effectively prevent network model degradation. Secondly, by using the residual connection module, the input information is directly transferred to the output layer through a shortcut, so as to simplify the goal and difficulty of feature learning. Finally, the multi-scale convolution module is combined for feature extraction, and the dilated convolution with different dilation rates is adopted to increase the receiving field to expand the diversity of features, so as to improve the classification accuracy. To verify the effectiveness of the model, experiments are carried out on the neuron morphology classification dataset. The experimental results show that the accuracy, precision, sensitivity and specificity of our method reach 90.11%, 89.63%, 90.77% and 93.27%, respectively. Compared with other classification models (VGG, ResNet, RNN), the proposed model has better classification effect.",
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"normalizedAbstract": "The study of neuron morphological classification has important application value to improve the accuracy and efficiency of three-dimensional reconstruction of neurons. However, due to the complex structure of neurons and the existence of global and local self-similarity in morphological distribution, it brings great difficulties to the classification of neuron morphology. Therefore, a new neuronal morphological classification model based on deep residual multiscale convolutional neural network is proposed. Firstly, the overall architecture of the model is based on the fast connection idea of ResNet, which can effectively prevent network model degradation. Secondly, by using the residual connection module, the input information is directly transferred to the output layer through a shortcut, so as to simplify the goal and difficulty of feature learning. Finally, the multi-scale convolution module is combined for feature extraction, and the dilated convolution with different dilation rates is adopted to increase the receiving field to expand the diversity of features, so as to improve the classification accuracy. To verify the effectiveness of the model, experiments are carried out on the neuron morphology classification dataset. The experimental results show that the accuracy, precision, sensitivity and specificity of our method reach 90.11%, 89.63%, 90.77% and 93.27%, respectively. Compared with other classification models (VGG, ResNet, RNN), the proposed model has better classification effect.",
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"abstract": "Analysis of electromyography (EMG) signals of normal physical actions have found to be important in order to detect certain abnormalities of the musculoskeletal system and diagnose abnormalities in patient behavior. This paper presents the results of the development of an Artificial Neural Network (ANN) for classification of EMG signals, according to the type of human behavior. The developed ANN is able to distinguish between 10 normal behaviors: bowing, clapping, handshaking, hugging, jumping, running, sitting, standing, walking, and waving. Feedforward neural network architecture was developed using dataset from UCI Machine Learning Repository database. QPC of each episode in EMG signal were obtained using bispectrum signal analysis. Training of ANN was performed using k-fold cross validation and impact of different number of neurons in hidden layer on system output was evaluated. Finally, the single-layer, feedforward neural network architecture with 17 neurons in hidden layer achieved the best performance and had sensitivity of 86.67% and of specificity 85.00%. The overall accuracy of developed structure is 86.25%.",
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"content": "Analysis of electromyography (EMG) signals of normal physical actions have found to be important in order to detect certain abnormalities of the musculoskeletal system and diagnose abnormalities in patient behavior. This paper presents the results of the development of an Artificial Neural Network (ANN) for classification of EMG signals, according to the type of human behavior. The developed ANN is able to distinguish between 10 normal behaviors: bowing, clapping, handshaking, hugging, jumping, running, sitting, standing, walking, and waving. Feedforward neural network architecture was developed using dataset from UCI Machine Learning Repository database. QPC of each episode in EMG signal were obtained using bispectrum signal analysis. Training of ANN was performed using k-fold cross validation and impact of different number of neurons in hidden layer on system output was evaluated. Finally, the single-layer, feedforward neural network architecture with 17 neurons in hidden layer achieved the best performance and had sensitivity of 86.67% and of specificity 85.00%. The overall accuracy of developed structure is 86.25%.",
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"Bispectrum Analysis",
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"affiliation": "International Burch University, Sarajevo, Bosnia and Herzegovina",
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"abstract": "In recent years the field of neuromorphic low-power systems gained significant momentum, spurring brain-inspired hardware systems which operate on principles that are fundamentally different from standard digital computers and thereby consume orders of magnitude less power. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While neuromorphic adaptations of representation learning algorithms are now emerging, the efficient processing of temporal sequences or variable length-inputs remains difficult, partly due to challenges in representing and configuring the dynamics of spiking neural networks. Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons, while being compatible with the capabilities of current and near-future neuromorphic systems. This “train-and-constrain” method consists of first training RNNs using backpropagation through time, then discretizing the weights and finally converting them to spiking RNNs by matching the responses of artificial neurons with those of the spiking neurons. We demonstrate our approach by mapping a natural language processing task (question classification), where we demonstrate the entire mapping process of the recurrent layer of the network on IBM's Neurosynaptic System TrueNorth, a spike-based digital neuromorphic hardware architecture. TrueNorth imposes specific constraints on connectivity, neural and synaptic parameters. To satisfy these constraints, it was necessary to discretize the synaptic weights to 16 levels, discretize the neural activities to 16 levels, and to limit fan-in to 64 inputs. Surprisingly, we find that short synaptic delays are sufficient to implement the dynamic (temporal) aspect of the RNN in the question classification task. Furthermore we observed that the discretization of the neural activities is beneficial to our train-and-constrain approach. The hardware-constrained model achieved 74% accuracy in question classification while using less than 0.025% of the cores on one TrueNorth chip, resulting in an estimated power consumption of ≈ 17µW.",
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"abstract": "This paper presents progress on a new and novel testing approach for detecting interconnection deletion faults in electronic implementations of artificial neural networks (ANNs). The testing approach is based on an unusual transient behavior manifested by faulted ANNs showing better apparent performance than fault-free ANNs, when neurons are operated with low activation function gains. The result presented in this paper improves on prior results by requiring fewer test patterns.",
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"abstract": "Artificial neural network is an abstract and simplified computing structure that simulates the neural system of human brain and reflects some characteristics of human brain. Artificial nerve is favored by researchers because of its high efficiency, memory function and fast calculation. Constructing artificial nerve and synapse to simulate biological nerve and synapse puts forward higher requirements for the structure and properties of the required materials. The development and application of synaptic devices made of traditional block materials are limited due to the defects of strong scattering and relatively large space. Due to the characteristics of high carrier mobility, dangling bonding free surface and nanocrystallization of emerging two-dimensional (2D) materials, heterojunctions are widely used in the functional realization experiments of artificial neurons and synapses. The successful application of 2D heterojunction in artificial neural network also has a far-reaching impact on the development of artificial intelligence and intelligent manufacturing. In this review, firstly, the types of 2D materials and the advantages of 2D materials as new materials for constructing artificial neurons and synapses are summarized. Then, the working principles of biological neurons and synapses, artificial neurons and synapses are briefly described, and the applications of heterojunctions based on 2D materials in artificial synapses (neurons) are summarized. Finally, the prospect of 2D material heterojunction in the field of artificial neural research and the possible challenges are analyzed.",
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"abstract": "Deep Neural Network (DNN) techniques have been prevalent in software engineering. They are employed to facilitate various software engineering tasks and embedded into many software applications. However, because DNNs are built upon a rich data-driven programming paradigm that employs plenty of labeled data to train a set of neurons to construct the internal system logic, analyzing and understanding their behaviors becomes a difficult task for software engineers. In this paper, we present an instance-based visualization tool for DNN, namely NeuralVis, to support software engineers in visualizing and interpreting deep learning models. NeuralVis is designed for: 1). visualizing the structure of DNN models, i.e., neurons, layers, as well as connections; 2). visualizing the data transformation process; 3). integrating existing adversarial attack algorithms for test input generation; 4). comparing intermediate layers' outputs of different inputs. To demonstrate the effectiveness of NeuralVis, we design a task-based user study involving ten participants on two classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can assist engineers in identifying critical features that determine the prediction results. Video: https://youtu.be/solkJri4Z44.",
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"content": "Deep Neural Network (DNN) techniques have been prevalent in software engineering. They are employed to facilitate various software engineering tasks and embedded into many software applications. However, because DNNs are built upon a rich data-driven programming paradigm that employs plenty of labeled data to train a set of neurons to construct the internal system logic, analyzing and understanding their behaviors becomes a difficult task for software engineers. In this paper, we present an instance-based visualization tool for DNN, namely NeuralVis, to support software engineers in visualizing and interpreting deep learning models. NeuralVis is designed for: 1). visualizing the structure of DNN models, i.e., neurons, layers, as well as connections; 2). visualizing the data transformation process; 3). integrating existing adversarial attack algorithms for test input generation; 4). comparing intermediate layers' outputs of different inputs. To demonstrate the effectiveness of NeuralVis, we design a task-based user study involving ten participants on two classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can assist engineers in identifying critical features that determine the prediction results. Video: https://youtu.be/solkJri4Z44.",
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"normalizedAbstract": "Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA frameworks rely on a monolithic architecture with features for the integration of specialized analysis methods, we consider a loose coupling of independent applications, where autonomous VA tools are used in predefined analysis sequences. To this end, we provide a characterization of the data exchange process among individual VA tools in the form of a taxonomy. This taxonomy can be used as a checklist to identify characteristics and improve the data flow of one's own multi-tool VA setup. For this purpose, we conducted a systematic investigation of the individual aspects of data exchange that are commonly found across different usage scenarios. We apply our taxonomy to three existing multi-tool frameworks, the open-source library ReVize, the toolchain editor AnyProc, and the visualization and monitoring framework Plant@Hand3D.",
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"title": "Extracting the Co-occurrences of DNA Maximal Repeats in both Human and Viruses",
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"abstract": "This paper aims to extract significant DNA sequences appearing in both the genomes of human and viruses. To extract the co-occurrences of DNA sequences as long as possible, this study adopts a scalable approach that is based on hadoop mapreduce programming model and can extract maximal repeats; meanwhile compute the class frequency distribution of these repeats. The genomes of human and all of 4,388 viruses available in NCBI are downloaded at 2017/1/14. To take the taxonomy of viruses into consideration for further observation, only the 2,712 viruses that had been named with genus are selected from those 4,388 viruses. In this study, the taxonomic level “genus” is as the units (classes) when comparing viruses and human for experiments. Experimental results show that the longest DNA sequence appearing in both human and viruses extracted in this study is 463 base pair (bp), and that sequence, consisting of tandem repeats as “(CTAACC)n”, appears in the 5th human chromosome and virus “Human herpesvirus 6B”. It may be attractive for virologists to have further research why there exists such a long DNA fragment existed in both human and that virus. Indeed, this study may provide a new direction for genomic sequences comparison across classesthat can provide clues to inspect the existence of the relationship between these DNA maximal repeats (genotypes) with biased class frequency distribution and the features of classes (phenotypes).",
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"content": "This paper aims to extract significant DNA sequences appearing in both the genomes of human and viruses. To extract the co-occurrences of DNA sequences as long as possible, this study adopts a scalable approach that is based on hadoop mapreduce programming model and can extract maximal repeats; meanwhile compute the class frequency distribution of these repeats. The genomes of human and all of 4,388 viruses available in NCBI are downloaded at 2017/1/14. To take the taxonomy of viruses into consideration for further observation, only the 2,712 viruses that had been named with genus are selected from those 4,388 viruses. In this study, the taxonomic level “genus” is as the units (classes) when comparing viruses and human for experiments. Experimental results show that the longest DNA sequence appearing in both human and viruses extracted in this study is 463 base pair (bp), and that sequence, consisting of tandem repeats as “(CTAACC)n”, appears in the 5th human chromosome and virus “Human herpesvirus 6B”. It may be attractive for virologists to have further research why there exists such a long DNA fragment existed in both human and that virus. Indeed, this study may provide a new direction for genomic sequences comparison across classesthat can provide clues to inspect the existence of the relationship between these DNA maximal repeats (genotypes) with biased class frequency distribution and the features of classes (phenotypes).",
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"abstract": "Differentiating people on the basis of their names has always been a complex issue and our desire for grouping people, in a particular domain, based on their attributes is growing day by day. Despite years of research and a bunch of proposed techniques, the name ambiguity problem remains largely unsolved and the so far proposed techniques have faced one problem or the other. In case of author name disambiguation in digital citations, additional attributes like e-mail ID and affiliation of author and co-authors, which are normally available in publications, can help a lot in disambiguation process. Vector space model has traditionally been used in information retrieval field with great degree of success and we explore its use in case of author name disambiguation here. In this paper we propose an enhanced vector space model for disambiguating authors and their publications. Experimental results show that additional attributes present in publications can help a lot in disambiguation and solve the name ambiguity problem with a great degree of confidence. From the study we conducted and the experimental results obtained we conclude that both mixed citation and split citations problem can be handled very efficiently. We obtained a great deal of improvement in evaluation metrics obtaining F1 score of 0.97.",
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"abstract": "People share same names in real world. When a digital library user searches for an author name, he may see a mixture of publications by different authors who have the same name. Making distinctions between them is an important prerequisite to improve the quality of services and contents in digital libraries. The general task of author disambiguation is to associate publications which belong to an identical name or names with highly similar spellings to different people entities. In recent years, many researches have been conducted to solve this challenging task. However, some works rely heavily on external knowledge bases and manually annotated data. Some unsupervised learning based works require complex feature engineering. In this paper, we propose a novel and efficient author disambiguation framework which needs no labeled data. We first construct a publication heterogeneous network for each ambiguous name. Then, we use our proposed heterogeneous graph convolutional network embedding method that encodes both graph structure and node attribute information to learn publication representations. After that, we propose a graph enhanced clustering method for name disambiguation that can greatly accelerate the clustering process and need not require the number of distinct persons. Our framework can be continually retrained and applied on incremental disambiguation task when new publications are put in. Experimental results on two datasets show that our framework clearly performs better than several state-of-the-art methods for author disambiguation.",
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"abstract": "Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",
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"abstract": "In order to shorten the visualization fusion time of unstructured data and improve the fusion effect, a visualization fusion method of unstructured data based on improved clustering is proposed. The basic situation of unstructured data of power grid is analyzed, and the data acquisition architecture is designed. Based on the collected data, multiple sensors are used to measure the same object. The influence factors are obtained by calculating historical data, historical data fusion results and observation values. The visual fusion objective function is designed, and the influence factor is used to modify the objective function, and the modified unstructured data fusion results are output. The experimental results show that the fusion effect of this method is good, the fusion time is short, and the practical application effect is good.",
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"affiliation": "State Grid Xinjiang Economic Research Institute,Urumqi,Xinjiang,China,830011",
"fullName": "Honglian Zhou",
"givenName": "Honglian",
"surname": "Zhou",
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"affiliation": "State Grid Xinjiang Economic Research Institute,Urumqi,Xinjiang,China,830011",
"fullName": "Lin Fu",
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"fullName": "Jianli Zhu",
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"affiliation": "School of Computer Science and Engineering, Xi'an University of Technology,Xi'an,China,710048",
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"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
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"year": "2014",
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"article": {
"id": "12OmNBPtJET",
"doi": "10.1109/BIBM.2014.6999353",
"title": "The clinical cohort study on prospective efficacy of CHD Therapeutic Regimen of “the regulation of Pi(Spleen) and protection of Xin(Heart)” method treating CHD patients",
"normalizedTitle": "The clinical cohort study on prospective efficacy of CHD Therapeutic Regimen of “the regulation of Pi(Spleen) and protection of Xin(Heart)” method treating CHD patients",
"abstract": "Objective: Evaluate the prospective efficacy of CHD Therapeutic Regimen of “regulation of Pi(Spleen)” Method. Methods: With the form of prospective cohort study, 572 patients were observed from December, 2007 to August, 2010, in which 35 cases of disqualification were rejected, and 537 cases were accepted for research and statistical analysis. The 537 patients were randomly divided into Exposed Group and Unexposed Group. Exposed Group was treated by CHD Therapeutic Regimen of “The regulation of Pi(Spleen) and Protection of Xin(Heart) (RPRS) Method. Unexposed Group was treated was treated by other therapeutic regimens of TCM except the RPRS Method. Both Exposed Group and Unexposed Group were treated with standard Western Medicine therapies while TCM treatments. The angina pectoris scores, SAQ, death rate, revascularization because of myocardial ischemia events, and end event such as emergency treatments and readmission etc. because of cardiovascular events at the 360th day after treatments were observed. Results: After treatments, there were significant differences on angina pectoris scores, SAQ, revascularization because of myocardial ischemia events, emergency treatments and admission rate between 2 groups (P < 0.05), and Exposed Group was better than Unexposed Group. Conclusion: CHD Therapeutic Regimen of RPRS Method is reliable on improving prospective efficacy of treating CHD.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Objective: Evaluate the prospective efficacy of CHD Therapeutic Regimen of “regulation of Pi(Spleen)” Method. Methods: With the form of prospective cohort study, 572 patients were observed from December, 2007 to August, 2010, in which 35 cases of disqualification were rejected, and 537 cases were accepted for research and statistical analysis. The 537 patients were randomly divided into Exposed Group and Unexposed Group. Exposed Group was treated by CHD Therapeutic Regimen of “The regulation of Pi(Spleen) and Protection of Xin(Heart) (RPRS) Method. Unexposed Group was treated was treated by other therapeutic regimens of TCM except the RPRS Method. Both Exposed Group and Unexposed Group were treated with standard Western Medicine therapies while TCM treatments. The angina pectoris scores, SAQ, death rate, revascularization because of myocardial ischemia events, and end event such as emergency treatments and readmission etc. because of cardiovascular events at the 360th day after treatments were observed. Results: After treatments, there were significant differences on angina pectoris scores, SAQ, revascularization because of myocardial ischemia events, emergency treatments and admission rate between 2 groups (P < 0.05), and Exposed Group was better than Unexposed Group. Conclusion: CHD Therapeutic Regimen of RPRS Method is reliable on improving prospective efficacy of treating CHD.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Objective: Evaluate the prospective efficacy of CHD Therapeutic Regimen of “regulation of Pi(Spleen)” Method. Methods: With the form of prospective cohort study, 572 patients were observed from December, 2007 to August, 2010, in which 35 cases of disqualification were rejected, and 537 cases were accepted for research and statistical analysis. The 537 patients were randomly divided into Exposed Group and Unexposed Group. Exposed Group was treated by CHD Therapeutic Regimen of “The regulation of Pi(Spleen) and Protection of Xin(Heart) (RPRS) Method. Unexposed Group was treated was treated by other therapeutic regimens of TCM except the RPRS Method. Both Exposed Group and Unexposed Group were treated with standard Western Medicine therapies while TCM treatments. The angina pectoris scores, SAQ, death rate, revascularization because of myocardial ischemia events, and end event such as emergency treatments and readmission etc. because of cardiovascular events at the 360th day after treatments were observed. Results: After treatments, there were significant differences on angina pectoris scores, SAQ, revascularization because of myocardial ischemia events, emergency treatments and admission rate between 2 groups (P < 0.05), and Exposed Group was better than Unexposed Group. Conclusion: CHD Therapeutic Regimen of RPRS Method is reliable on improving prospective efficacy of treating CHD.",
"fno": "06999353",
"keywords": [
"Hospitals",
"Myocardium",
"Medical Diagnostic Imaging",
"Training",
"Diseases",
"Standards",
"End Events",
"CHD",
"Prospective Efficacy",
"Angina Pectoris Scores",
"Quality Of Life"
],
"authors": [
{
"affiliation": "Cardiac Disease Treatment Center, Guangdong Hospital of Traditional Chinese Medicine, Guangdong Provincial ACHDemy of Chinese Medical Sciences, Guangzhou 510006",
"fullName": "Danping Xu",
"givenName": "Danping",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Cardiac Disease Treatment Center, Guangdong Hospital of Traditional Chinese Medicine, Guangdong Provincial ACHDemy of Chinese Medical Sciences, Guangzhou 510006",
"fullName": "Xia Wang",
"givenName": "Xia",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Cardiac Disease Treatment Center, Guangdong Hospital of Traditional Chinese Medicine, Guangdong Provincial ACHDemy of Chinese Medical Sciences, Guangzhou 510006",
"fullName": "Wenjie Luo",
"givenName": "Wenjie",
"surname": "Luo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Cardiac Disease Treatment Center, Guangdong Hospital of Traditional Chinese Medicine, Guangdong Provincial ACHDemy of Chinese Medical Sciences, Guangzhou 510006",
"fullName": "Xiaoqing Li",
"givenName": "Xiaoqing",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Cardiac Disease Treatment Center, Guangdong Hospital of Traditional Chinese Medicine, Guangdong Provincial ACHDemy of Chinese Medical Sciences, Guangzhou 510006",
"fullName": "Wu Huanlin",
"givenName": "Wu",
"surname": "Huanlin",
"__typename": "ArticleAuthorType"
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"pubDate": "2014-11-01T00:00:00",
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"isbn": "978-1-4799-5669-2",
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{
"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
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"article": {
"id": "12OmNqOOrKm",
"doi": "10.1109/BIBM.2014.6999338",
"title": "The study of the constitution, mucosal inflammation, Chinese medicine syndrome types and clinical pathology in IgA nephropathy",
"normalizedTitle": "The study of the constitution, mucosal inflammation, Chinese medicine syndrome types and clinical pathology in IgA nephropathy",
"abstract": "Objective: This study is to investigate the constitution, mucosal inflammation, Chinese medicine syndrome types, clinical and pathological characteristics of patients with IgA nephropathy (IgA N)and to analysis their relationships in order to provide the scientfic basis and new ideas for the management of IgAN. Methods: Through the cross sectional and retrospective study, we investigated 262 patients with IgAN in the Hospital of Guangdong provincical TCM hospital and the Third Hospital of Peking Universitiy between January 2011 and January 2014. We collected the Chinese medicine syndrome types, clinical and pathological data, the constitution questionnaire and the mucosal inflammation. Results: The constitution of yin-yang harmony was the most common(40.5%), followed by qideficiency(31.3%). Upper respiratory infection, chronic periodontitis, scytitis were very common in patients with IgAN. Qi deficiency of spleen and kidney were the common type: 69.1%. Asymptomatic abnormal urinalysis(50.4% of the patients)and CKD 1(48.5%)were the most common clinical Presentation. HaasD was the most common pathological presentation(47.3%). Patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony presented with more nonmucosal inflammation, more CKD3-4, higher than the latter in the level of serum creatinine, urea nitrogen, serum CH50 and lower in GFR clinically as compared to those patients with the constitution of qi deficiency. Conclusions: IgAN onsets silently and asymptomatic abnormal urinalysis was the most common clinical type. Frequent upper respiratory infection, chronic periodontitis and recurrent scytitis were common in patients with IgAN. IgAN patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony had higher serum CH50 level, low incidence of mucosal inflammation, increased risk of declined renal function and more sever renal pathlogical changes than that of patients with qi deficiency of spleen and kidney and the constitution of qi deficiency.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Objective: This study is to investigate the constitution, mucosal inflammation, Chinese medicine syndrome types, clinical and pathological characteristics of patients with IgA nephropathy (IgA N)and to analysis their relationships in order to provide the scientfic basis and new ideas for the management of IgAN. Methods: Through the cross sectional and retrospective study, we investigated 262 patients with IgAN in the Hospital of Guangdong provincical TCM hospital and the Third Hospital of Peking Universitiy between January 2011 and January 2014. We collected the Chinese medicine syndrome types, clinical and pathological data, the constitution questionnaire and the mucosal inflammation. Results: The constitution of yin-yang harmony was the most common(40.5%), followed by qideficiency(31.3%). Upper respiratory infection, chronic periodontitis, scytitis were very common in patients with IgAN. Qi deficiency of spleen and kidney were the common type: 69.1%. Asymptomatic abnormal urinalysis(50.4% of the patients)and CKD 1(48.5%)were the most common clinical Presentation. HaasD was the most common pathological presentation(47.3%). Patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony presented with more nonmucosal inflammation, more CKD3-4, higher than the latter in the level of serum creatinine, urea nitrogen, serum CH50 and lower in GFR clinically as compared to those patients with the constitution of qi deficiency. Conclusions: IgAN onsets silently and asymptomatic abnormal urinalysis was the most common clinical type. Frequent upper respiratory infection, chronic periodontitis and recurrent scytitis were common in patients with IgAN. IgAN patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony had higher serum CH50 level, low incidence of mucosal inflammation, increased risk of declined renal function and more sever renal pathlogical changes than that of patients with qi deficiency of spleen and kidney and the constitution of qi deficiency.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Objective: This study is to investigate the constitution, mucosal inflammation, Chinese medicine syndrome types, clinical and pathological characteristics of patients with IgA nephropathy (IgA N)and to analysis their relationships in order to provide the scientfic basis and new ideas for the management of IgAN. Methods: Through the cross sectional and retrospective study, we investigated 262 patients with IgAN in the Hospital of Guangdong provincical TCM hospital and the Third Hospital of Peking Universitiy between January 2011 and January 2014. We collected the Chinese medicine syndrome types, clinical and pathological data, the constitution questionnaire and the mucosal inflammation. Results: The constitution of yin-yang harmony was the most common(40.5%), followed by qideficiency(31.3%). Upper respiratory infection, chronic periodontitis, scytitis were very common in patients with IgAN. Qi deficiency of spleen and kidney were the common type: 69.1%. Asymptomatic abnormal urinalysis(50.4% of the patients)and CKD 1(48.5%)were the most common clinical Presentation. HaasD was the most common pathological presentation(47.3%). Patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony presented with more nonmucosal inflammation, more CKD3-4, higher than the latter in the level of serum creatinine, urea nitrogen, serum CH50 and lower in GFR clinically as compared to those patients with the constitution of qi deficiency. Conclusions: IgAN onsets silently and asymptomatic abnormal urinalysis was the most common clinical type. Frequent upper respiratory infection, chronic periodontitis and recurrent scytitis were common in patients with IgAN. IgAN patients with qi deficiency of spleen and kidney and the constitution of yin-yang harmony had higher serum CH50 level, low incidence of mucosal inflammation, increased risk of declined renal function and more sever renal pathlogical changes than that of patients with qi deficiency of spleen and kidney and the constitution of qi deficiency.",
"fno": "06999338",
"keywords": [
"Constitution",
"Medical Diagnostic Imaging",
"Kidney",
"Pathology",
"Hospitals",
"Diseases",
"Clinical Pathological Relationship",
"Ig A Nephropathy",
"Constitution",
"Mucosal Inflammation",
"Chinese Medicine Syndrome Type"
],
"authors": [
{
"affiliation": "Renal Division, Guangdong Province Zhuhai Hospital of Chinese Medicine; Zhuhai, Guangdong provincice, China",
"fullName": "Li-Chang Liu",
"givenName": "Li-Chang",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Renal Division, Guangdong Province Hospital of Chinese Medicine; Guangzhou, Guangdong provincice, China",
"fullName": "Xu-Sheng Liu",
"givenName": "Xu-Sheng",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Renal Division, Third Hospital of Peking Universitiy; Beijing, China",
"fullName": "Tao Wang",
"givenName": "Tao",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Renal Division, Guangdong Province Zhuhai Hospital of Chinese Medicine; Zhuhai, Guangdong provincice, China",
"fullName": "Xin Liu",
"givenName": "Xin",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Renal Division, Third Hospital of Peking Universitiy; Beijing, China",
"fullName": "Hong-Lei Yu",
"givenName": "Hong-Lei",
"surname": "Yu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Renal Division, Guangdong Province Hospital of Chinese Medicine; Guangzhou, Guangdong provincice, China",
"fullName": "Chuan Zou",
"givenName": "Chuan",
"surname": "Zou",
"__typename": "ArticleAuthorType"
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"title": "Bibliometrics in syndrome distribution of anovulatory dysfunctional uterine bleeding",
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"abstract": "The purpose of this work is to discuss distribution of Chinese medical syndrome for anovulatory dysfunctional uterine bleeding (DUB) by modern literature research, in order to provide guilds and methodological references for establishing a series of scientific and rational systems for distribution of Chinese medical syndrome in anovulatory DUB. Bibliometric analysis was used in this study. The modern literatures had been retrieved, and the study subjects were modern literatures involving the classification of distribution of Chinese medical syndrome in DUB, from 1989 to 2013. In the end, totally 16 qualified articles were incorporated. The number of cases was 1249, the frequencies of syndrome occurrence were 55. The general information, the frequency of syndrome occurrence and the number of cases in literatures were analyzed. Finally, syndrome denomination database was established. The result showed that the syndrome differentiations of anovulatory DUB were mainly the intrinsic blood heat, blood stasis obstructing the uterus, kidney-yin deficiency, qi deficiency failing to control blood, kidney-yang deficiency. However, the other syndrome differentiation research gave priority to visceral pattern identification and qi-blood pattern identification. Therefore, we conclude that syndrome distributions of anovulatory DUB are mainly visceral pattern identification and qi-blood pattern identification. Moreover, the disease pathogenesis of traditional Chinese medicine closely related to qi and blood, spleen and kidney disease pathogenesis. So this disease can be treated by the theory of \"invigorating the spleen\". The study attempts to provide the theory for distribution of Chinese medical syndrome in anovulatory DUB, at the same time provide a demonstration for the similar study of other diseases.",
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"content": "The purpose of this work is to discuss distribution of Chinese medical syndrome for anovulatory dysfunctional uterine bleeding (DUB) by modern literature research, in order to provide guilds and methodological references for establishing a series of scientific and rational systems for distribution of Chinese medical syndrome in anovulatory DUB. Bibliometric analysis was used in this study. The modern literatures had been retrieved, and the study subjects were modern literatures involving the classification of distribution of Chinese medical syndrome in DUB, from 1989 to 2013. In the end, totally 16 qualified articles were incorporated. The number of cases was 1249, the frequencies of syndrome occurrence were 55. The general information, the frequency of syndrome occurrence and the number of cases in literatures were analyzed. Finally, syndrome denomination database was established. The result showed that the syndrome differentiations of anovulatory DUB were mainly the intrinsic blood heat, blood stasis obstructing the uterus, kidney-yin deficiency, qi deficiency failing to control blood, kidney-yang deficiency. However, the other syndrome differentiation research gave priority to visceral pattern identification and qi-blood pattern identification. Therefore, we conclude that syndrome distributions of anovulatory DUB are mainly visceral pattern identification and qi-blood pattern identification. Moreover, the disease pathogenesis of traditional Chinese medicine closely related to qi and blood, spleen and kidney disease pathogenesis. So this disease can be treated by the theory of \"invigorating the spleen\". The study attempts to provide the theory for distribution of Chinese medical syndrome in anovulatory DUB, at the same time provide a demonstration for the similar study of other diseases.",
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"affiliation": "Guangdong Hospital of Chinese Medicine, Guangzhou 510120, Guangdong, China",
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"abstract": "Microorganisms are being increasingly used in industrial processes due to certain beneficial phenotypes they exhibit. Improving the ability of microorganisms to exhibit these phenotypes has driven interest in identifying the genes that are responsible for a given phenotype. Some of these phenotypes are the result of various chemical compounds being modified by a series of metabolic reactions, or metabolic pathways, catalyzed by specific enzymes. Recently, comprehensive, generic metabolic networks have been defined, which describe possible ways in which certain chemical compounds may be modified by known metabolic reactions. In this paper, we aim to discover phenotype related metabolic pathways by identifying subnetworks of a generic metabolic network that are highly conserved in phenotype expressing organisms and rarely conserved in non-phenotype expressing organisms. To do this, we introduce a graph search algorithm that finds and expands highly conserved seed networks based on their evolutionary bias towards phenotype expressing organisms. We hypothesize that the evolutionarily conservation of these subnetworks in phenotype expressing organisms is likely due to the fact that they represent metabolic pathways responsible for the expression of the phenotype. We test our approach using aerobic and anaerobic organisms to identify pathways related to aerobic respiration. We find that the pathways identified by our algorithm are found primarily in aerobic organisms and that metabolic pathways known to be related to aerobic respiration are covered by the pathways identified by our algorithm. We finish by discussing the ongoing and future work related to this methodology.",
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"content": "Microorganisms are being increasingly used in industrial processes due to certain beneficial phenotypes they exhibit. Improving the ability of microorganisms to exhibit these phenotypes has driven interest in identifying the genes that are responsible for a given phenotype. Some of these phenotypes are the result of various chemical compounds being modified by a series of metabolic reactions, or metabolic pathways, catalyzed by specific enzymes. Recently, comprehensive, generic metabolic networks have been defined, which describe possible ways in which certain chemical compounds may be modified by known metabolic reactions. In this paper, we aim to discover phenotype related metabolic pathways by identifying subnetworks of a generic metabolic network that are highly conserved in phenotype expressing organisms and rarely conserved in non-phenotype expressing organisms. To do this, we introduce a graph search algorithm that finds and expands highly conserved seed networks based on their evolutionary bias towards phenotype expressing organisms. We hypothesize that the evolutionarily conservation of these subnetworks in phenotype expressing organisms is likely due to the fact that they represent metabolic pathways responsible for the expression of the phenotype. We test our approach using aerobic and anaerobic organisms to identify pathways related to aerobic respiration. We find that the pathways identified by our algorithm are found primarily in aerobic organisms and that metabolic pathways known to be related to aerobic respiration are covered by the pathways identified by our algorithm. We finish by discussing the ongoing and future work related to this methodology.",
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"content": "Most quantitative cell image-based screening analyses are dependent on thorough user supervision based on assay-specific knowledge. To minimize human bias in analysis, we introduce an automated methodology of displaying screen phenotypes using clustering that provides intuitive visuals to guide user supervision when required. Our premise is to automatically present to users an overview of screen phenotype-contents to assist in planning assay and analysis of a new screen. Our methodology starts from numerical features of cell-images, removes outliers through the density-based clustering OPTICS, identifies significant phenotypes by the Hierarchical Agglomerative Clustering and Dynamic Tree Cut techniques, and displays representative cell images as phenotype-signatures. User supervision needed to detect outliers and adjust the desired heterogeneity-level of identified phenotypes is facilitated respectively by an intuitive density plot and a systematic phenotype display. The methodology was tested on various phenotypes of the Golgi apparatus, an intracellular structure essential for cell physiology and protein secretion. The Golgi apparatus was targeted by various drug treatments. This test demonstrates the methodology's potentials by providing a comprehensive categorization of Golgi phenotypes.",
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"content": "Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and help drug development. Gene-phenotype associations are related to complex biological process and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations, existing approaches are limited to prediction accuracy. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC2NMF). Specifically, firstly we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchal phenotypic data as weighted graph constraint for a better phenotype understanding. Secondly, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us an intuitive priori that genes in a group probably result in similar phenotypes. The model allows not only to achieve a high prediction performance but also jointly to learn interpretable representation of genes and phenotypes to handle future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC2NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-art methods.",
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"abstract": "Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.",
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"content": "Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.",
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"abstract": "Prioritizing casual genes for disease phenotypes can reveal the inherent basis of human diseases and facilitate drug developments. It is widely believed that the full utilization of hierarchical structure of phenotype ontologies in HPO can improve the performance of gene prioritization significantly. However, previous works mostly utilize the hierarchical structure as graph constraint or implicit hierarchical relation, thus unable to fully depict the underlying correlations among adjacent levels of phenotype ontologies.To tackle the issue, we proposed a Multi-View Nonnegative Matrix Factorization method with Layer-Wise Explicit Hierarchical Constraint (HMNMF), which fully explores the “is-a” relationship between adjacent levels of phenotypes in HPO. Experimental results demonstrate the outstanding performance of HMNMF not only in the extraordinary global gene prioritization performance but also in the strong ability to prioritize candidate genes for totally new phenotypes.",
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"abstract": "Phenotype prediction aims to infer the traits of living organisms based on genomics data, which has important applications in biology such as cancer subtype diagnosis and crop breedings. Traditional phenotype prediction approaches only learn the low-dimensional representation of samples, but ignore the interaction between biomolecules and cannot use the topology structure of heterogeneous molecular networks. Furthermore, most of them lack interpretability and do not effectively identify key biomolecules associated with phenotypes. In this paper, we propose a heterogeneous network embedding based solution (PhenoHNE) to predict phenotype by fusing topology information of molecules. PhenoHNE firstly utilizes variational graph autoencode (VGAE) to obtain the embedding representation of molecules in the heterogeneous network. Secondly, PhenoHNE adopts multilayer perceptron (MLP) with attention mechanism to learn sample representation. Finally, it fuses molecular embedding representation and sample representation to predict the phenotype of samples. In this way, the genetics information of heterogeneous molecular network can further guide the feature learning of samples and improve the prediction performance. Experimental results on human and maize datasets confirm that PhenoHNE outperforms competitive methods by a large margin under different evaluation protocols, and it also can effectively identify the key molecules associated with phenotypes of interests.",
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"content": "Phenotype prediction aims to infer the traits of living organisms based on genomics data, which has important applications in biology such as cancer subtype diagnosis and crop breedings. Traditional phenotype prediction approaches only learn the low-dimensional representation of samples, but ignore the interaction between biomolecules and cannot use the topology structure of heterogeneous molecular networks. Furthermore, most of them lack interpretability and do not effectively identify key biomolecules associated with phenotypes. In this paper, we propose a heterogeneous network embedding based solution (PhenoHNE) to predict phenotype by fusing topology information of molecules. PhenoHNE firstly utilizes variational graph autoencode (VGAE) to obtain the embedding representation of molecules in the heterogeneous network. Secondly, PhenoHNE adopts multilayer perceptron (MLP) with attention mechanism to learn sample representation. Finally, it fuses molecular embedding representation and sample representation to predict the phenotype of samples. In this way, the genetics information of heterogeneous molecular network can further guide the feature learning of samples and improve the prediction performance. Experimental results on human and maize datasets confirm that PhenoHNE outperforms competitive methods by a large margin under different evaluation protocols, and it also can effectively identify the key molecules associated with phenotypes of interests.",
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"affiliation": "Shandong University,SDU-NTU Joint Centre For AI Research (C-FAIR),Jinan,China",
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"affiliation": "Shandong University,SDU-NTU Joint Centre For AI Research (C-FAIR),Jinan,China",
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"affiliation": "Shandong University,SDU-NTU Joint Centre For AI Research (C-FAIR),Jinan,China",
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"affiliation": "College of Elec. & Inf. Eng. BUCEA,Beijing,China",
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"title": "Finding relevant answers in software forums",
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"abstract": "Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containing relevant keywords. However, in software forums, often there are many threads containing similar keywords where each thread could contain a lot of posts as many as 1,000 or more. Manually finding relevant answers from these long threads is a painstaking task to the users. Finding relevant answers is particularly hard in software forums as: complexities of software systems cause a huge variety of issues often expressed in similar technical jargons, and software forum users are often expert internet users who often posts answers in multiple venues creating many duplicate posts, often without satisfying answers, in the world wide web. To address this problem, this paper provides a semantic search engine framework to process software threads and recover relevant answers according to user queries. Different from standard information retrieval engine, our framework infer semantic tags of posts in the software forum threads and utilize these tags to recover relevant answer posts. In our case study, we analyze 6,068 posts from three software forums. In terms of accuracy of our inferred tags, we could achieve on average an overall precision, recall and F-measure of 67%, 71%, and 69% respectively. To empirically study the benefit of our overall framework, we also conduct a user-assisted study which shows that as compared to a standard information retrieval approach, our proposed framework could increase mean average precision from 17% to 71% in retrieving relevant answers to various queries and achieve a Normalized Discounted Cumulative Gain (nDCG) @1 score of 91.2% and nDCG@2 score of 71.6%.",
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"content": "In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the limits of online education. Discussion forums are primary means of interaction among learners and instructors. However, with growing class size, students face the challenge of finding useful and informative discussion forums. This problem can be solved by matching the interest of students with thread contents. The fundamental challenge is that the student interests drift as they progress through the course, and forum contents evolve as students or instructors update them. In our paper, we propose to predict future interest trajectories of students. Our model consists of two key operations: 1) Update operation and 2) Projection operation. Update operation models the inter-dependency between the evolution of student and thread using coupled Recurrent Neural Networks when the student posts on the thread. The projection operation learns to estimate future embedding of students and threads. For students, the projection operation learns the drift in their interests caused by the change in the course topic they study. The projection operation for threads exploits how different posts induce varying interest levels in a student according to the thread structure. Extensive experimentation on three real-world MOOC datasets shows that our model significantly outperforms other baselines for thread recommendation.",
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"normalizedAbstract": "In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the limits of online education. Discussion forums are primary means of interaction among learners and instructors. However, with growing class size, students face the challenge of finding useful and informative discussion forums. This problem can be solved by matching the interest of students with thread contents. The fundamental challenge is that the student interests drift as they progress through the course, and forum contents evolve as students or instructors update them. In our paper, we propose to predict future interest trajectories of students. Our model consists of two key operations: 1) Update operation and 2) Projection operation. Update operation models the inter-dependency between the evolution of student and thread using coupled Recurrent Neural Networks when the student posts on the thread. The projection operation learns to estimate future embedding of students and threads. For students, the projection operation learns the drift in their interests caused by the change in the course topic they study. The projection operation for threads exploits how different posts induce varying interest levels in a student according to the thread structure. Extensive experimentation on three real-world MOOC datasets shows that our model significantly outperforms other baselines for thread recommendation.",
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"keywords": [
"Computer Aided Instruction",
"Distance Learning",
"Educational Courses",
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"Recurrent Neural Nets",
"Coupled Recurrent Neural Networks",
"Student Posts",
"Projection Operation",
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"Thread Structure",
"Real World MOOC Datasets",
"Learning Student Interest Trajectory",
"MOOC Thread Recommendation",
"Massive Open Online Courses",
"Online Education",
"Class Size",
"Informative Discussion Forums",
"Thread Contents",
"Student Interests",
"Forum Contents",
"Update Operation Models",
"Covid 19 Pandemic Situation",
"Computer Aided Instruction",
"Electronic Learning",
"Discussion Forums",
"Recurrent Neural Networks",
"Pandemics",
"Trajectory",
"Message Systems",
"Recommender Systems",
"Personalized Learning",
"MOO Cs"
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{
"affiliation": "University of Minnesota,Twin Cities,Minnesota,USA,55455",
"fullName": "Shalini Pandey",
"givenName": "Shalini",
"surname": "Pandey",
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{
"affiliation": "University of Massachusetts Amherst,Amherst,MA,01003",
"fullName": "Andrew Lan",
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"surname": "Lan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Minnesota,Twin Cities,Minnesota,USA,55455",
"fullName": "George Karypis",
"givenName": "George",
"surname": "Karypis",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Minnesota,Twin Cities,Minnesota,USA,55455",
"fullName": "Jaideep Srivastava",
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"abstract": "Software product forums is a platform filled with user feedback that utilises the sub-forum feature to categorise user discussion into themes. These sub-forums are very similar to classification labels that have been used to automatically classify user feedback on other platforms such as Troubleshooting and Feature Request. It would be very beneficial to the CrowdRE community if these sub-forum categories can be utilised in a research setting as it would reduce the effort required to label content for classification manually. However, no research has been done on the accuracy of these sub-forum categorisations in software product forums. In this exploratory study, we examined the accuracy of user categorised posts in two software product forums and discovered that users incorrectly categorise more than 20% of the posts during submission. Our discovery suggests that at the current stage, sub-forum categories should not be trusted as a label to classify feedback automatically.",
"abstracts": [
{
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"content": "Software product forums is a platform filled with user feedback that utilises the sub-forum feature to categorise user discussion into themes. These sub-forums are very similar to classification labels that have been used to automatically classify user feedback on other platforms such as Troubleshooting and Feature Request. It would be very beneficial to the CrowdRE community if these sub-forum categories can be utilised in a research setting as it would reduce the effort required to label content for classification manually. However, no research has been done on the accuracy of these sub-forum categorisations in software product forums. In this exploratory study, we examined the accuracy of user categorised posts in two software product forums and discovered that users incorrectly categorise more than 20% of the posts during submission. Our discovery suggests that at the current stage, sub-forum categories should not be trusted as a label to classify feedback automatically.",
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"normalizedAbstract": "Software product forums is a platform filled with user feedback that utilises the sub-forum feature to categorise user discussion into themes. These sub-forums are very similar to classification labels that have been used to automatically classify user feedback on other platforms such as Troubleshooting and Feature Request. It would be very beneficial to the CrowdRE community if these sub-forum categories can be utilised in a research setting as it would reduce the effort required to label content for classification manually. However, no research has been done on the accuracy of these sub-forum categorisations in software product forums. In this exploratory study, we examined the accuracy of user categorised posts in two software product forums and discovered that users incorrectly categorise more than 20% of the posts during submission. Our discovery suggests that at the current stage, sub-forum categories should not be trusted as a label to classify feedback automatically.",
"fno": "189800a293",
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"proceeding": {
"id": "12OmNxdVh2r",
"title": "2017 IEEE Pacific Visualization Symposium (PacificVis)",
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"title": "A visual analytics approach for understanding egocentric intimacy network evolution and impact propagation in MMORPGs",
"normalizedTitle": "A visual analytics approach for understanding egocentric intimacy network evolution and impact propagation in MMORPGs",
"abstract": "Massively Multiplayer Online Role-playing Games (MMORPGs) feature a large number of players socially interacting with one another in an immersive gaming environment. A successful MMORPG should engage players and meet their needs to achieve different categories of gratifications. Research on the evolution of player social interaction network and the dynamics of inter-player intimacy could provide insights into players' gratification-oriented behaviors in MMORPGs. Such understanding could in turn guide game designs for better engaging existing players and marketing strategies for attracting newcomers. Conventional dynamic network analysis may help investigate game-based social interactions at the macroscopic level. However, current dynamic network visualization techniques mainly focus on illustrating topological changes of the entire network, which are unsuitable for analyzing player-specific social interactions in the virtual world from an egocentric perspective. In general, game designers and operators find it difficult to analyze the way players with different gratification needs may interact with one another and the consequences on their relationships with direct ties, using a decentralized social graph with complicated time-varying structures. In this paper, we present MMOSeer, a visual analytics system for exploring the evolution of egocentric player intimacy network. MMOSeer focuses on the relationship between a player (ego) and his/her directly-linked friends (alters). We follow a user-centered design process to develop the system with game analysts and apply novel visualization techniques in conjunction with well-established algorithms to depict the evolution of intimacy egocentric network. We also derive a centrality change metric to infer how the impact of changes in an ego's interactive behaviors may propagate through the intimacy network, reshaping the structure of the alters' social circles at both micro and macro levels. Finally, we validate the usability of MMOSeer by discovering different user interaction patterns and the corresponding ego-network structural changes in a real-world gameplay dataset from a commercial MMORPG.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Massively Multiplayer Online Role-playing Games (MMORPGs) feature a large number of players socially interacting with one another in an immersive gaming environment. A successful MMORPG should engage players and meet their needs to achieve different categories of gratifications. Research on the evolution of player social interaction network and the dynamics of inter-player intimacy could provide insights into players' gratification-oriented behaviors in MMORPGs. Such understanding could in turn guide game designs for better engaging existing players and marketing strategies for attracting newcomers. Conventional dynamic network analysis may help investigate game-based social interactions at the macroscopic level. However, current dynamic network visualization techniques mainly focus on illustrating topological changes of the entire network, which are unsuitable for analyzing player-specific social interactions in the virtual world from an egocentric perspective. In general, game designers and operators find it difficult to analyze the way players with different gratification needs may interact with one another and the consequences on their relationships with direct ties, using a decentralized social graph with complicated time-varying structures. In this paper, we present MMOSeer, a visual analytics system for exploring the evolution of egocentric player intimacy network. MMOSeer focuses on the relationship between a player (ego) and his/her directly-linked friends (alters). We follow a user-centered design process to develop the system with game analysts and apply novel visualization techniques in conjunction with well-established algorithms to depict the evolution of intimacy egocentric network. We also derive a centrality change metric to infer how the impact of changes in an ego's interactive behaviors may propagate through the intimacy network, reshaping the structure of the alters' social circles at both micro and macro levels. Finally, we validate the usability of MMOSeer by discovering different user interaction patterns and the corresponding ego-network structural changes in a real-world gameplay dataset from a commercial MMORPG.",
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"authors": [
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"affiliation": "The Hong Kong University of Science and Technology, China",
"fullName": "Quan Li",
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"affiliation": "The Hong Kong University of Science and Technology, China",
"fullName": "Qiaomu Shen",
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"affiliation": "The Hong Kong University of Science and Technology, China",
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"affiliation": "NetEase, Inc., China",
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"affiliation": "The Hong Kong University of Science and Technology, China",
"fullName": "Yun Wang",
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"abstract": "This paper seeks to establish how games can be used as a didactic tool for leadership development. Two multiplayer online battle arena games (MOBA) known as Defense of the Ancients (DotA) and Heroes of Newerth (HoN) are studied together with the leadership models of Fred E. Fiedler (1967) and Kurt Lewin (1939). A close-ended survey was conducted to determine the game player's game role (carry, support or ganker) and his leadership style (authoritarian, democratic, laissez-faire). A positive correlation was established between the game player's game role and leadership style. Furthermore the causal relationship behind this dynamic was explored through propensity score matching and logistic regression. The study concludes that exercising specific roles in MOBA games lead to the development of certain leadership styles.",
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"abstract": "The objective of this study is to identify the attractive quality factors affecting MOBA game design, thus meeting consumers’ requirements and effectively improving their satisfaction. The factor analysis was carried out based on the results of 116 subjects’ evaluation of various quality attributes. According to the principal components analysis, the coefficient values of gameplay factor, usability factor, sociability factor, and sensuality factor were 0.302, 0.347, 0.442, and 0.345 respectively, and had a positive effect on satisfaction. Among these factors, sociability factor had slightly higher satisfaction evaluation than other factors. With Arena of Valor as the object of research, the application feasibility of the study was explored and verified by case study.",
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"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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"abstract": "Numerous people are suffering from sleep-related problems. To diagnose them, a prerequisite is to divide the polysomnography (PSG) data into different sleep stages. Thus, sleep stage classification is an essential step, but collecting PSG data is expensive, time-consuming, and even belated. To address this issue, using accelerometers that are widely used in smartwatches is treated as an alternative way to monitor people’s sleep conditions. However, the flexibility of deep learning models by purely using wrist-worn accelerometer data for sleep stage classification has not been investigated by researchers. To explore the answer, in this paper, we design a novel axis-aware hybrid fusion-based deep learning model, named AccSleepNet, which takes the three axes’ accelerometer data as the input simultaneously. The designed axis-aware hybrid fusion mechanism prompts the model to learn the deep features from three axes collaboratively. Finally, a classification module takes the fused feature representations from three axes as input and outputs the predicted sleep stage. Experimental results on two public datasets demonstrate the effectiveness of the proposed AccSleepNet for the sleep stage classification task compared with state-of-the-art baselines. Moreover, an ablation study validates the necessity of leveraging three axes’ accelerometer data and the superiority of the designed axis-aware hybrid fusion mechanism <sup>1</sup>.",
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"fullName": "Yuhao Zhang",
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