The Experts below are selected from a list of 192 Experts worldwide ranked by ideXlab platform
Ron Gilster - One of the best experts on this subject based on the ideXlab platform.
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microsoft office sharepoint server 2007 a beginner s guide
2007Co-Authors: Ron GilsterAbstract:Set up and administer a SharePoint Server 2007 environment Get started on Microsoft Office SharePoint Server 2007 quickly and easily with help from this step-by-step guide. Using clear instructions, Microsoft Office SharePoint Server 2007: A Beginner's Guide shows you how to set up and configure SharePoint Server, collect and store data, build lists and libraries, and enable enterprise Search capabilities. You'll learn how to create portals and Web pages, secure your SharePoint Server 2007 environment, and optimize performance. Microsoft Office 2007 integration techniques are also covered. Install and configure SharePoint Server 2007 Secure your SharePoint Server network and data Easily locate files and folders using the Search Feature Simplify data collection using forms and workflows Logically organize content into lists and libraries Monitor, maintain, and back up your SharePoint Server environment Build Web applications and portals from reusable, modular Web Parts Improve efficiency using customized views and metadata schemes Seamlessly integrate with Microsoft Office Outlook 2007 Table of contents PART 1: GETTING STARTED Chapter 1: An Overview of Microsoft Office SharePoint Server 2007 Chapter 2: Plan and Configure a MOSS Implementation Chapter 3: MOSS Preinstallation Chapter 4: MOSS Installation Chapter 5: Post-Installation Configuration PART 2: MOSS ADMINISTRATION Chapter 6: MOSS Administration Chapter 7: MOSS Security Chapter 8: MOSS Search Chapter 9: MOSS Document and Record Management Chapter 10: Workflows Chapter 11: SharePoint Libraries Chapter 12: SharePoint Lists Chapter 13: Monitor MOSS Performance Chapter 14: MOSS Maintenance Chapter 15: Web Parts Chapter 16: SharePoint Views and Metadata PART 3: MOSS AND OFFICE 2007 Chapter 17: MOSS and Outlook 2007 Chapter 18: MOSS and Word 2007 Chapter 19: MOSS and Excel 2007 Chapter 20: MOSS and Access 2007 Chapter 21: MOSS and Business Intelligence Chapter 22: MOSS and XML Index
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microsoft office sharepoint server 2007 a beginner s guide
2007Co-Authors: Ron GilsterAbstract:Set up and administer a SharePoint Server 2007 environment Get started on Microsoft Office SharePoint Server 2007 quickly and easily with help from this step-by-step guide. Using clear instructions, Microsoft Office SharePoint Server 2007: A Beginner's Guide shows you how to set up and configure SharePoint Server, collect and store data, build lists and libraries, and enable enterprise Search capabilities. You'll learn how to create portals and Web pages, secure your SharePoint Server 2007 environment, and optimize performance. Microsoft Office 2007 integration techniques are also covered. Install and configure SharePoint Server 2007 Secure your SharePoint Server network and data Easily locate files and folders using the Search Feature Simplify data collection using forms and workflows Logically organize content into lists and libraries Monitor, maintain, and back up your SharePoint Server environment Build Web applications and portals from reusable, modular Web Parts Improve efficiency using customized views and metadata schemes Seamlessly integrate with Microsoft Office Outlook 2007 Table of contents PART 1: GETTING STARTED Chapter 1: An Overview of Microsoft Office SharePoint Server 2007 Chapter 2: Plan and Configure a MOSS Implementation Chapter 3: MOSS Preinstallation Chapter 4: MOSS Installation Chapter 5: Post-Installation Configuration PART 2: MOSS ADMINISTRATION Chapter 6: MOSS Administration Chapter 7: MOSS Security Chapter 8: MOSS Search Chapter 9: MOSS Document and Record Management Chapter 10: Workflows Chapter 11: SharePoint Libraries Chapter 12: SharePoint Lists Chapter 13: Monitor MOSS Performance Chapter 14: MOSS Maintenance Chapter 15: Web Parts Chapter 16: SharePoint Views and Metadata PART 3: MOSS AND OFFICE 2007 Chapter 17: MOSS and Outlook 2007 Chapter 18: MOSS and Word 2007 Chapter 19: MOSS and Excel 2007 Chapter 20: MOSS and Access 2007 Chapter 21: MOSS and Business Intelligence Chapter 22: MOSS and XML Index
Bart M Ter Haar Romeny - One of the best experts on this subject based on the ideXlab platform.
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retinal artery vein classification using genetic Search Feature selection
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Fan Huang, Behdad Dashtbozorg, Tao Tan, Bart M Ter Haar RomenyAbstract:Abstract Background and objectives: The automatic classification of retinal blood vessels into artery and vein (A/V) is still a challenging task in retinal image analysis. Recent works on A/V classification mainly focus on the graph analysis of the retinal vasculature, which exploits the connectivity of vessels to improve the classification performance. While they have overlooked the importance of pixel-wise classification to the final classification results. This paper shows that a complicated Feature set is efficient for vessel centerline pixels classification. Methods: We extract enormous amount of Features for vessel centerline pixels, and apply a genetic-Search based Feature selection technique to obtain the optimal Feature subset for A/V classification. Results: The proposed method achieves an accuracy of 90.2%, the sensitivity of 89.6%, the specificity of 91.3% on the INSPIRE dataset. It shows that our method, using only the information of centerline pixels, gives a comparable performance as the techniques which use complicated graph analysis. In addition, the results on the images acquired by different fundus cameras show that our framework is capable for discriminating vessels independent of the imaging device characteristics, image resolution and image quality. Conclusion: The complicated Feature set is essential for A/V classification, especially on the individual vessels where graph-based methods receive limitations. And it could provide a higher entry to the graph-analysis to achieve a better A/V labeling.
Antoni B Chan - One of the best experts on this subject based on the ideXlab platform.
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learning dynamic memory networks for object tracking
European Conference on Computer Vision, 2018Co-Authors: Tianyu Yang, Antoni B ChanAbstract:Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object’s appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target’s appearance variations during tracking. An LSTM is used as a memory controller, where the input is the Search Feature map and the outputs are the control signals for the reading and writing process of the memory block. As the location of the target is at first unknown in the Search Feature map, an attention mechanism is applied to concentrate the LSTM input on the potential target. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. Unlike tracking-by-detection methods where the object’s information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target’s appearance changes by updating the external memory. Moreover, unlike other tracking methods where the model capacity is fixed after offline training – the capacity of our tracker can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack performs favorably against state-of-the-art tracking methods while retaining real-time speed of 50 fps.
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learning dynamic memory networks for object tracking
European Conference on Computer Vision, 2018Co-Authors: Tianyu Yang, Antoni B ChanAbstract:Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object’s appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target’s appearance variations during tracking. An LSTM is used as a memory controller, where the input is the Search Feature map and the outputs are the control signals for the reading and writing process of the memory block. As the location of the target is at first unknown in the Search Feature map, an attention mechanism is applied to concentrate the LSTM input on the potential target. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. Unlike tracking-by-detection methods where the object’s information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target’s appearance changes by updating the external memory. Moreover, unlike other tracking methods where the model capacity is fixed after offline training – the capacity of our tracker can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack performs favorably against state-of-the-art tracking methods while retaining real-time speed of 50 fps.
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learning dynamic memory networks for object tracking
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Tianyu Yang, Antoni B ChanAbstract:Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. An LSTM is used as a memory controller, where the input is the Search Feature map and the outputs are the control signals for the reading and writing process of the memory block. As the location of the target is at first unknown in the Search Feature map, an attention mechanism is applied to concentrate the LSTM input on the potential target. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. Unlike tracking-by-detection methods where the object's information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target's appearance changes by updating the external memory. Moreover, the capacity of our model is not determined by the network size as with other trackers -- the capacity can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack performs favorably against state-of-the-art tracking methods while retaining real-time speed of 50 fps.
Simon Fong - One of the best experts on this subject based on the ideXlab platform.
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accelerated pso swarm Search Feature selection for data stream mining big data
IEEE Transactions on Services Computing, 2016Co-Authors: Simon Fong, Raymond K Wong, Athanasios V VasilakosAbstract:Big Data though it is a hype up-springing many technical challenges that confront both academic reSearch communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the Search space from which an optimal Feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight Feature selection is proposed. The Feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm Search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new Feature selection algorithm for performance evaluation.
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Feature selection in life science classification metaheuristic swarm Search
IT Professional, 2014Co-Authors: Simon Fong, Suash Deb, Xinshe YangAbstract:The purpose of classification in medical informatics is to predict the presence or absence of a particular disease as well as disease types from historical data. Medical data often contain irrelevant Features and noise, and an appropriate subset of the significant Features can improve classification accuracy. Therefore, reSearchers apply Feature selection to identify and remove irrelevant and redundant Features. The authors propose a versatile Feature selection approach called Swarm Search Feature Selection (SS-FS), based on stochastic swarm intelligence. It is designed to overcome NP-hard combinatorial Search problems such as the selection of an optimal Feature subset from an extremely large array of Features--which is not uncommon in biomedical data. SS-FS is demonstrated to be a feasible computing tool in achieving high accuracy in classification via testing with two empirical biomedical datasets. This article is part of a special issue on life sciences computing.
Athanasios V Vasilakos - One of the best experts on this subject based on the ideXlab platform.
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accelerated pso swarm Search Feature selection for data stream mining big data
IEEE Transactions on Services Computing, 2016Co-Authors: Simon Fong, Raymond K Wong, Athanasios V VasilakosAbstract:Big Data though it is a hype up-springing many technical challenges that confront both academic reSearch communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the Search space from which an optimal Feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight Feature selection is proposed. The Feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm Search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new Feature selection algorithm for performance evaluation.