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"abstract": "In machine learning, the power of an approach is measured by its capability to be adapted for different applications and using different formats of data. Spectral Clustering is an unsupervised method that can be adopted for many research fields in and beyond computer science. In this paper, we present and analyze the existing algorithms of spectral clustering, and based on their limits we propose our modified version to deal with the most common challenge in this context which is the dynamic estimation of the output number of clusters.",
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"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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"abstract": "Spectral clustering (SC) is an important data mining method in bioinformatics due to the collection of big datasets. Calculating the number of clusters and feature vectors are two main steps of SC, both of which are related to eigenproblem. We propose a novel SC method using an adaptive filter which accelerates the SC and completely sidestepping the eigensolver. Results: We are inspired by two theories of different disciplines. The first theory is Density of State (DOS), a concept of solid-state physics. DOS roughly represents the distribution of eigenvalues. We propose a more accurate DOS and integrate it to obtain the number of clusters. The second theory is Graph Signal Processing (GSP) in irregular graph. Based on existing dimension reduction method, we use GSP to compute the feature vectors. We use Chebyshev polynomials to compute DOS and GSP and let them share the polynomials. Both the computational complexity and storage complexity are low.",
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"content": "Spectral clustering (SC) is an important data mining method in bioinformatics due to the collection of big datasets. Calculating the number of clusters and feature vectors are two main steps of SC, both of which are related to eigenproblem. We propose a novel SC method using an adaptive filter which accelerates the SC and completely sidestepping the eigensolver. Results: We are inspired by two theories of different disciplines. The first theory is Density of State (DOS), a concept of solid-state physics. DOS roughly represents the distribution of eigenvalues. We propose a more accurate DOS and integrate it to obtain the number of clusters. The second theory is Graph Signal Processing (GSP) in irregular graph. Based on existing dimension reduction method, we use GSP to compute the feature vectors. We use Chebyshev polynomials to compute DOS and GSP and let them share the polynomials. Both the computational complexity and storage complexity are low.",
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"Bioinformatics",
"Chebyshev Approximation",
"Data Mining",
"Eigenvalues And Eigenfunctions",
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"abstract": "Spectral clustering and DBSCAN are two famous clustering methods, the former reduces data dimensionality by spectrum of similarity matrix, and then utilizes kmeans to cluster data in low dimensional space. While DBSCAN performs clustering by finding different density regions that depart from each other, which makes it seems quite different from spectral clustering, and there is little literal discusses the relationship between them. In this paper, we revisit DBSCAN from Similarity Graph and Graph Cut point of views, uncover the underlying relationship between DBSCAN and spectral clustering, which proves that DBSCAN can be explained and rewritten under the framework of spectral clustering. Furthermore, eigenvectors are often approximately resolved, and k-means is usually used in the final stage, often converges in local optimization. Hence, we rewrite spectral clustering by using nearest neighbor query instead of k-means to obtain exact result. Experimental results address that the revised spectral clustering method can obtain the same result as DBSCAN on core point set. Therefore, we come to the conclusion that DBSCAN is semi-spectral clustering. The work of this paper theoretically illustrates that spectral clustering is as good as DBSCAN, and both algorithms can be replaced with each other to avoid their own disadvantages.",
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"content": "Spectral clustering and DBSCAN are two famous clustering methods, the former reduces data dimensionality by spectrum of similarity matrix, and then utilizes kmeans to cluster data in low dimensional space. While DBSCAN performs clustering by finding different density regions that depart from each other, which makes it seems quite different from spectral clustering, and there is little literal discusses the relationship between them. In this paper, we revisit DBSCAN from Similarity Graph and Graph Cut point of views, uncover the underlying relationship between DBSCAN and spectral clustering, which proves that DBSCAN can be explained and rewritten under the framework of spectral clustering. Furthermore, eigenvectors are often approximately resolved, and k-means is usually used in the final stage, often converges in local optimization. Hence, we rewrite spectral clustering by using nearest neighbor query instead of k-means to obtain exact result. Experimental results address that the revised spectral clustering method can obtain the same result as DBSCAN on core point set. Therefore, we come to the conclusion that DBSCAN is semi-spectral clustering. The work of this paper theoretically illustrates that spectral clustering is as good as DBSCAN, and both algorithms can be replaced with each other to avoid their own disadvantages.",
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"normalizedAbstract": "Spectral clustering and DBSCAN are two famous clustering methods, the former reduces data dimensionality by spectrum of similarity matrix, and then utilizes kmeans to cluster data in low dimensional space. While DBSCAN performs clustering by finding different density regions that depart from each other, which makes it seems quite different from spectral clustering, and there is little literal discusses the relationship between them. In this paper, we revisit DBSCAN from Similarity Graph and Graph Cut point of views, uncover the underlying relationship between DBSCAN and spectral clustering, which proves that DBSCAN can be explained and rewritten under the framework of spectral clustering. Furthermore, eigenvectors are often approximately resolved, and k-means is usually used in the final stage, often converges in local optimization. Hence, we rewrite spectral clustering by using nearest neighbor query instead of k-means to obtain exact result. Experimental results address that the revised spectral clustering method can obtain the same result as DBSCAN on core point set. Therefore, we come to the conclusion that DBSCAN is semi-spectral clustering. The work of this paper theoretically illustrates that spectral clustering is as good as DBSCAN, and both algorithms can be replaced with each other to avoid their own disadvantages.",
"fno": "223200a257",
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"Matrix Algebra",
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"abstract": "Video summarization, which has a tremendous usage area that spreads from information retrieval to data compression, plays a crucial role in the multimedia understanding. In recent years, with the explosion of the number of videos and their area of use, video summarization became a must to signify. Therefore, this work introduces a novel approach for the summarization problem which is based on human movement understanding. Proposed system presents efficient video knowledge extraction, especially for surveillance cases. Human centric videos are analyzed with histogram of oriented gradients as feature extractor and optical flow as motion descriptor. Above these, a template matching algorithm implemented in a shrinkable and stretchable manner to search for periodicity and thereby extract patterns. Summarization is built up on the validation of these extracted patterns with a correlation based search-through subsystem.",
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"title": "Coherent event-based surveillance video synopsis using trajectory clustering",
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"abstract": "With the rapid development of the camera industry, surveillance systems become more and more popular in our daily life. However, it is very time-consuming to find out specific persons or objects from a mass of surveillance videos with long duration. For efficient browsing surveillance videos, numerous researchers are devoted to eliminating the inherent spatiotemporal redundancy for video synopsis. Nevertheless, too much information in a synopsis frame may distract viewers' attention. Therefore, we propose a novel surveillance video synopsis system using coherent event classification to alleviate the above issues. Object trajectories are extracted by background subtraction, and then clustered. Coherent events containing similar actions of objects with different moving speeds are obtained by applying the longest common subsequence algorithm to measure the similarity among trajectories. The trajectories in each cluster are rescheduled and stitched onto the background to generate synopsis videos with coherent events. Comprehensive experiments conducted on various surveillance videos demonstrate the convincing performance of our proposed system.",
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"affiliation": "Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan",
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"abstract": "Automatic detection of fight behaviors in surveillance videos is an important task for surveillance systems. In this work, we propose a novel localization guided framework for detecting fight actions in surveillance videos. Specifically, we exploit optical flow maps to extract motion activation information, which indicates the location of active regions. Then, a detection guided alignment module is designed to adjust the localized active regions. This approach employs a two-stream based 3D convolution network as the backbone network with a novel motion acceleration representation on the temporal stream. While most existing methods are still evaluated on three benchmark datasets which were not originally collected from surveillance scenarios, we present a novel Fight Action Detection in Surveillance-videos (FADS) dataset for this purpose. With a total of 1,520 video clips, the FADS is the largest known dataset in terms of number of surveillance videos with fight scenes. Experimental results on both the benchmark datasets and the FADS show that our proposed localization guided method outperforms state-of-the-art techniques.",
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"abstract": "Trip time prediction is an important problem. Taxi passengers often want to know when they will arrive at their destinations. We design a method of predicting taxi trip time by finding historical similar trips. Trips are clustered based on origin, destination, and start time. Then similar trips are mapped to road networks to find frequent sub-trajectories that are used to model travel time of the various parts of the routes. Experimental results show this method is effective.",
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"abstract": "City-wide GPS recorded taxi trip data contains rich information for traffic and travel analysis to facilitate transportation planning and urban studies. However, traditional data management techniques are largely incapable of processing big taxi trip data at the scale of hundreds of millions. In this study, we aim at utilizing the General Purpose computing on Graphics Processing Units (GPGPUs) technologies to speed up processing complex spatial queries on big taxi data on inexpensive commodity GPUs. By using the land use types of tax lot polygons as a proxy for trip purposes at the pickup and drop-off locations, we formulate a taxi trip data analysis problem as a large-scale nearest neighbor spatial query problem based on point-to-polygon distance. Experiments on nearly 170 million taxi trips in the New York City (NYC) in 2009 and 735,488 tax lot polygons with 4,698,986 vertices have demonstrated the efficiency of the proposed techniques: the GPU implementations is about 10-20X faster than the host system and completes the spatial query in about a minute by using a low-end workstation equipped with an Nvidia GTX Titan GPU device with a total equipment cost of below $2,000. We further discuss several interesting patterns discovered from the query results which warrant further study. The proposed approach can be an interesting alternative to traditional MapReduce/Hadoop based approaches to processing big data with respect to performance and cost.",
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"abstract": "To achieve smart cities, real-world trace data sensed from the GPS-enabled taxi system, which conveys underlying dynamics of people movements, could be used to make urban transportation services smarter. As an example, it will be very helpful for passengers to know how long it will take to find a taxi at a spot, since they can plan their schedule and choose the best spot to wait. In this paper, we present a method to predict the waiting time for a passenger at a given time and spot from historical taxi trajectories. The arrival model of passengers and that of vacant taxis are built from the events that taxis arrive at and leave a spot. With the models, we could simulate the passenger waiting queue for a spot and infer the waiting time. The experiment with a large-scale real taxi GPS trace dataset is carried out to verify the proposed method.",
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"affiliation": "Dept. of Comput. Sci., St. Francis Xavier Univ., Antigonish, NS, Canada",
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"affiliation": "Sch. of Traffic & Transp. Eng., Changsha Univ. of Sci. & Technol., Changsha, China",
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"abstract": "Ant Colony Optimization (ACO) is a meta-heuristic based on colony of artificial ants which work cooperatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants. One of the active research directions is the application of ACO algorithms to solve dynamic shortest path problems. Solving traffic jams is one such problem where the cost i.e. time to travel increases during rush hours resulting in tremendous strain on daily commuters and chaos. This paper describes a new approach-DSATJ (Dynamic System for Avoiding Traffic Jam) which aims at choosing an alternative optimum path to avoid traffic jam and then resuming that same path again when the traffic is regulated. The approach is inspired by variants of ACO algorithms. Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only. Randomness is introduced in the probability function to ensure maximum exploration by ants. Experiments were carried out with the partial road map of North-West region of Delhi, India, to observe the performance of our approach.",
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"content": "Ant Colony Optimization (ACO) is a meta-heuristic based on colony of artificial ants which work cooperatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants. One of the active research directions is the application of ACO algorithms to solve dynamic shortest path problems. Solving traffic jams is one such problem where the cost i.e. time to travel increases during rush hours resulting in tremendous strain on daily commuters and chaos. This paper describes a new approach-DSATJ (Dynamic System for Avoiding Traffic Jam) which aims at choosing an alternative optimum path to avoid traffic jam and then resuming that same path again when the traffic is regulated. The approach is inspired by variants of ACO algorithms. Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only. Randomness is introduced in the probability function to ensure maximum exploration by ants. Experiments were carried out with the partial road map of North-West region of Delhi, India, to observe the performance of our approach.",
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"fullName": "Punam Bedi",
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"abstract": "Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data; for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: Can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data.",
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"content": "Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data; for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: Can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data.",
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"abstract": "The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value.",
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{
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"content": "The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value.",
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"abstract": "Electroencephalogram(EEG) and eye movement have been extensively applied in the detection of anxiety disorders because they can reflect the brain functions and people's attentional bias. Although our previous work can make good use of the group structure information of EEG and eye movement signals, it mainly models the linear correlation and ignores the nonlinear correlation between two modalities. Therefore, we proposed kernel group sparse canonical correlation analysis (K-GSCCA) to study the nonlinear complex relationship and group structure information among EEG and eye movement features. Firstly, EEG signals were divided into 13 groups according to different brain regions, and eye movement signals were divided into 4 groups according to different visual behaviors. Then, we used the Gaussian kernel function to transform data into kernel space, effectively generated nonlinear cooperative fusion representation. The experimental outcomes demonstrated that K-GSCCA can be effective to solve the nonlinear correlation of group structure information between EEG and eye movement features. Using the support vector machine(SVM) classifier, we finally achieved the best classification accuracy of 87.47% in the fusion of the gamma band of EEG and eye movement features.",
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"content": "Electroencephalogram(EEG) and eye movement have been extensively applied in the detection of anxiety disorders because they can reflect the brain functions and people's attentional bias. Although our previous work can make good use of the group structure information of EEG and eye movement signals, it mainly models the linear correlation and ignores the nonlinear correlation between two modalities. Therefore, we proposed kernel group sparse canonical correlation analysis (K-GSCCA) to study the nonlinear complex relationship and group structure information among EEG and eye movement features. Firstly, EEG signals were divided into 13 groups according to different brain regions, and eye movement signals were divided into 4 groups according to different visual behaviors. Then, we used the Gaussian kernel function to transform data into kernel space, effectively generated nonlinear cooperative fusion representation. The experimental outcomes demonstrated that K-GSCCA can be effective to solve the nonlinear correlation of group structure information between EEG and eye movement features. Using the support vector machine(SVM) classifier, we finally achieved the best classification accuracy of 87.47% in the fusion of the gamma band of EEG and eye movement features.",
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"abstract": "Tumor tissue characterization can play an important role in the diagnosis and design of effective treatment strategies. In order to gather and combine the necessary tissue information, multi-modal imaging is used to derive a number of parameters indicative of tissue properties. The exploration and analysis of relationships between parameters and, especially, of differences among distinct intra-tumor regions is particularly interesting for clinical researchers to individualize tumor treatment. However, due to high data dimensionality and complexity, the current clinical workflow is time demanding and does not provide the necessary intra-tumor insight. We implemented a new application for the exploration of the relationships between parameters and heterogeneity within tumors. In our approach, we employ a well-known dimensionality reduction technique [5] to map the high-dimensional space of tissue properties into a 2D information space that can be interactively explored with integrated information visualization techniques. We conducted several usage scenarios with real-patient data, of which we present a case of advanced cervical cancer. First indications show that our application introduces new features and functionalities that are not available within the current clinical approach.",
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"Groupware",
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"Workspace Awareness",
"Distributed Software Development",
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"Groupware",
"Application Sharing",
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"affiliation": "Institute of Computer Science, Freie Universität Berlin, Germany",
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"abstract": "Collaborative writing systems allow co-authors, spread out across different locations, to work together sharing common documents. One key element that, in most cases, is poorly supported in collaborative writing systems is group awareness. Many research studies have defined several types of group awareness. The paper presents a comparative analysis of workspace and conversational awareness support in collaborative writing systems. For this purpose, we adapted R. Vertegaal's (1997) framework in order to analyze the group awareness mechanisms for collaborative writing. This framework considers the workspace and conversational awareness elements. Based on these elements, we analyzed and compared Quilt, GROVE, PREP, SASSE, Calliope and Alliance collaborative writing systems. Finally, we identified their strengths and weaknesses.",
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"content": "Collaborative writing systems allow co-authors, spread out across different locations, to work together sharing common documents. One key element that, in most cases, is poorly supported in collaborative writing systems is group awareness. Many research studies have defined several types of group awareness. The paper presents a comparative analysis of workspace and conversational awareness support in collaborative writing systems. For this purpose, we adapted R. Vertegaal's (1997) framework in order to analyze the group awareness mechanisms for collaborative writing. This framework considers the workspace and conversational awareness elements. Based on these elements, we analyzed and compared Quilt, GROVE, PREP, SASSE, Calliope and Alliance collaborative writing systems. Finally, we identified their strengths and weaknesses.",
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"Linguistics Group Awareness Support Collaborative Writing Systems Co Authors Common Documents Workspace Conversational Awareness Support Group Awareness Mechanisms Conversational Awareness Elements Quilt GROVE PREP SASSE Calliope Alliance"
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{
"affiliation": "Div. de Estudios de Posgrado de la Fac. de Ingenieria, Univ. Nacional Autonoma de Mexico, Mexico City, Mexico",
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"affiliation": "Div. de Estudios de Posgrado de la Fac. de Ingenieria, Univ. Nacional Autonoma de Mexico, Mexico City, Mexico",
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"abstract": "Knowledge awareness refers to one's awareness of the other group learners' domain knowledge. CSCL researchers maintain that knowledge awareness is an important aspect to the outcome of collaborative learning activities. Interested in groupware design to support knowledge awareness, I investigated how a shared rationale space affected one's knowledge awareness in virtual collaborative learning environment through a classroom study. In the investigation, a rationale is a learner's reasoning behind his/her decision of the tasks in the learning activity. The findings show that one's awareness of the other group learners' rationales, namely, rationale awareness, enhances one's knowledge awareness. The study suggests several design implications to support knowledge awareness, including sharing one's contextual information that reveals one's domain knowledge, highlighting the association between the knowledge awareness information and the learners who provided the information, and supporting utilization of knowledge awareness information at both individual and group levels.",
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"content": "Knowledge awareness refers to one's awareness of the other group learners' domain knowledge. CSCL researchers maintain that knowledge awareness is an important aspect to the outcome of collaborative learning activities. Interested in groupware design to support knowledge awareness, I investigated how a shared rationale space affected one's knowledge awareness in virtual collaborative learning environment through a classroom study. In the investigation, a rationale is a learner's reasoning behind his/her decision of the tasks in the learning activity. The findings show that one's awareness of the other group learners' rationales, namely, rationale awareness, enhances one's knowledge awareness. The study suggests several design implications to support knowledge awareness, including sharing one's contextual information that reveals one's domain knowledge, highlighting the association between the knowledge awareness information and the learners who provided the information, and supporting utilization of knowledge awareness information at both individual and group levels.",
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"abstract": "In this paper we investigate conceptually and empirically in a software development company whether Enterprise 2.0 components contain awareness mechanisms. As a result, we introduce additional mechanisms to take the first step to improve awareness in complex cooperative work environments. After a short introduction to the concepts awareness, awareness mechanisms, and Enterprise 2.0 we describe a case study to find out patterns of awareness in collaborative work processes and the missing awareness support. We approach the problem by trying to understand which Enterprise 2.0 components are related to which awareness mechanisms, and to which degree Enterprise 2.0 fulfills awareness requirements of complex collaborative work. Our study results in the identification of two different categories: system- and user-related awareness mechanisms. Search, extensions, and signals - supporting system-related mechanisms - are the most common components that are already established by different tools. Authoring, links, and tags - assisting user-related mechanisms - on the other hand, have not been utilized yet. They are very powerful to create context and capture collective knowledge. To support this, we introduce additional awareness mechanisms like enter, annotate, rate, share, reference, select, mark, and label, to show how these three components can be implemented in enterprises. By doing so, we present the potential of Enterprise 2.0 to support awareness in cooperative work. The new map of awareness mechanisms to Enterprise 2.0 inform not only the developers of tools supporting (aware) collaboration but also practitioners working in teams to define their requirements to such tools.",
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"content": "In this paper we investigate conceptually and empirically in a software development company whether Enterprise 2.0 components contain awareness mechanisms. As a result, we introduce additional mechanisms to take the first step to improve awareness in complex cooperative work environments. After a short introduction to the concepts awareness, awareness mechanisms, and Enterprise 2.0 we describe a case study to find out patterns of awareness in collaborative work processes and the missing awareness support. We approach the problem by trying to understand which Enterprise 2.0 components are related to which awareness mechanisms, and to which degree Enterprise 2.0 fulfills awareness requirements of complex collaborative work. Our study results in the identification of two different categories: system- and user-related awareness mechanisms. Search, extensions, and signals - supporting system-related mechanisms - are the most common components that are already established by different tools. Authoring, links, and tags - assisting user-related mechanisms - on the other hand, have not been utilized yet. They are very powerful to create context and capture collective knowledge. To support this, we introduce additional awareness mechanisms like enter, annotate, rate, share, reference, select, mark, and label, to show how these three components can be implemented in enterprises. By doing so, we present the potential of Enterprise 2.0 to support awareness in cooperative work. The new map of awareness mechanisms to Enterprise 2.0 inform not only the developers of tools supporting (aware) collaboration but also practitioners working in teams to define their requirements to such tools.",
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"title": "Handling the Missing Data Problem in Electronic Health Records for Cancer Prediction",
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"abstract": "Electronic health records (EHRs) are the records containing the patients’ clinic information. The EHRs have been widely used in disease diagnosis and therapy due to the numerous and valuable medical information in them. However, the missing data problem of EHRs hinders the usage. Replacing the missing data with mean values is an approach of data imputation. But, that method weakens the feature importance. In this study, we use the expectation-maximization (EM) algorithm to impute the missing data in EHRs. Some machine learning models, including artificial neural network, logistic regression, support vector machine, and random forests are used to evaluate the effectiveness of data imputation. The experimental results show that the prediction accuracies of cancers by using those models on the EHRs imputed by EM algorithm are higher than those by mean values, which indicates the EM algorithm is able to provide accurate estimations in data imputation of EHRs.",
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"content": "Electronic health records (EHRs) are the records containing the patients’ clinic information. The EHRs have been widely used in disease diagnosis and therapy due to the numerous and valuable medical information in them. However, the missing data problem of EHRs hinders the usage. Replacing the missing data with mean values is an approach of data imputation. But, that method weakens the feature importance. In this study, we use the expectation-maximization (EM) algorithm to impute the missing data in EHRs. Some machine learning models, including artificial neural network, logistic regression, support vector machine, and random forests are used to evaluate the effectiveness of data imputation. The experimental results show that the prediction accuracies of cancers by using those models on the EHRs imputed by EM algorithm are higher than those by mean values, which indicates the EM algorithm is able to provide accurate estimations in data imputation of EHRs.",
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