Ultra-high-resolution wall-sized displays feature a very high pixel density over a large physical surface, typically covering a few square meters. they provide effective support for collaborative work sessions that in...
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this paper introduces a study to analyse the users impressions about different visualization techniques. To achieve this, we used the Spot tool, which provides a set of different visualization techniques to users with...
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the recent and increasing popularity of wireless transmission systems has initiated a strong interest in research on mobile communications. While most of this research effort focuses on unicast communications, there h...
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ISBN:
(纸本)7505350668
the recent and increasing popularity of wireless transmission systems has initiated a strong interest in research on mobile communications. While most of this research effort focuses on unicast communications, there has also been a number of proposals to support multicasting with mobile members. However, the majority of these proposals, despite being innovative and elegant, remain at a research level, i.e. it is not clear if they could realistically be deployed in real large networks. In contrast, the IETF Mobile IP working group has outlined two realistic methods that allow mobile nodes to join multicast groups. these proposals have however never been evaluated and compared. this paper therefore presents the results of a large number of simulations in which we have compared the efficiency of the two IETF solutions. We also propose the use of a new extension of the Multicast Listener Delivery (MLD) protocol, which allows to rapidly resume the delivery of multicast data to a mobile receiver that has just performed a handover. the efficiency of this proposal is also compared withthe already mentioned solutions of the IETF.
the rapid evolution of the Internet of things (IoT) and Big data technology has been generating a large amount and variety of sensing contents, including numeric measured values (e.g., timestamps, geolocations, or sen...
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ISBN:
(纸本)9781450356169
the rapid evolution of the Internet of things (IoT) and Big data technology has been generating a large amount and variety of sensing contents, including numeric measured values (e.g., timestamps, geolocations, or sensor logs) and multimedia (e.g., images, audios, and videos). In analyzing and understanding heterogeneous types of IoT-generated contents better, datavisualization is an essential component of exploratory data analyses to facilitate information perception and knowledge extraction. this study introduces a holistic approach of storing, processing, and visualizing IoT-generated contents to support context-aware spatiotemporal insight by combining deep learning techniques with a geographical map interface. visualization is provided under an interactive web-based user interface to help the an efficient visual exploration considering both time and geolocation by easy spatiotemporal query user interface'.
the proceedings contain 11 papers. the special focus in this conference is on Benchmarking, Measuring, and Optimizing. the topics include: ICBench: Benchmarking Knowledge Mastery in Introductory Computer Science ...
ISBN:
(纸本)9789819703159
the proceedings contain 11 papers. the special focus in this conference is on Benchmarking, Measuring, and Optimizing. the topics include: ICBench: Benchmarking Knowledge Mastery in Introductory Computer Science Education;generating High Dimensional Test data for Topological dataanalysis;Does AI for Science Need Another ImageNet or Totally Different Benchmarks? A Case Study of Machine Learning Force Fields;MolBench: A Benchmark of AI Models for Molecular Property Prediction;Cross-Layer Profiling of IoTBench;MMDBench: A Benchmark for Hybrid Query in Multimodal database;Benchmarking Modern databases for Storing and Profiling Very large Scale HPC Communication data;a Linear Combination-Based Method to Construct Proxy Benchmarks for Big data Workloads;AGIBench: A Multi-granularity, Multimodal, Human-Referenced, Auto-Scoring Benchmark for large Language Models.
A company's IT infrastructure delivers the basic hardware, networking, operating system, and middleware support to the business' applications. IT service providers perform incident and problem resolution, as w...
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ISBN:
(纸本)9781479927845
A company's IT infrastructure delivers the basic hardware, networking, operating system, and middleware support to the business' applications. IT service providers perform incident and problem resolution, as well as user administration and change implementation required to maintain the availability and service provided for the business. As a result, they become increasingly challenged with delivering better, faster, and cheaper services to their customers. Withthe variety of incident tickets reported on a daily basis, understanding where and how much effort is spent to resolve them is critical. Moreover, analyzing the effort data identifies opportunities for self-service and automation, as well as what modernization strategies businesses should implement to reduce incident volumes and, by association, labor effort. In this paper, we conduct a large scale study on the incident and server factors that affect technician effort and quantify their impact. We show that the nature of the incidents and their complexity, the assigned support groups, as well as the underlying OS type play a major role in how much labor effort is spent towards resolving such tickets.
Among one of the current and most topical tasks in the area of textual documents processing belongs the problem of automatic categorization. Clustering as the most common form of unsupervised learning enables automati...
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ISBN:
(纸本)9780769549347;9781467350266
Among one of the current and most topical tasks in the area of textual documents processing belongs the problem of automatic categorization. Clustering as the most common form of unsupervised learning enables automatic grouping of unlabeled documents into subsets called clusters. In this paper, the authors are concerned with results of clustering of very large electronic real-world data collections containing customers' reviews written freely, in English as a natural language. the reviews are automatically clustered into two groups that should contain either positive or negative reviews. the paper focuses on the analysis why certain reviews are assigned wrongly to a group containing mostly reviews of a different class. the assignment of a review into a certain cluster is based on its properties, i.e., on the words that appeared in the review. thus, words appearing in incorrectly categorized reviews were analyzed. It was found that words that are important from the correct classification viewpoint (and thus bearing some sentiment) are often similarly important as the words in a different set than expected, therefore do not take effect as misleading information unlike words that are much more or quite insignificant.
A word spotting system is in large parts characterized by the query modalities it is able to process. the most common modalities here are Query-by-Example and Query-by-String. However, recently a new query type has be...
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ISBN:
(纸本)9781538635865
A word spotting system is in large parts characterized by the query modalities it is able to process. the most common modalities here are Query-by-Example and Query-by-String. However, recently a new query type has been proposed: In Query-by-Online-Trajectory (QbO) the query is presented as a set of online-handwritten trajectories. In this work we devise a cross-domain word spotting framework using CNNs which is able to accomplish the QbO task. In particular, we design two different QbO systems which we evaluate in a number of experiments. We are not only able to outperform the current state of the art in QbO word spotting but also show that a system using a single CNN for both online and offline data achieves superior results compared to a system that uses a CNN for each domain individually.
the large Hadron Collider (LHC) at CERN, the European Organization for Nuclear Research, will produce unprecedented volumes of data when it starts operation in 2007. To provide for its computational needs, the LHC Com...
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this paper gives an overview of methods for utilizing large process data matrices. these data matrices are almost always of less than full statistical rank, and therefore, latent variable methods are shown to be well ...
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this paper gives an overview of methods for utilizing large process data matrices. these data matrices are almost always of less than full statistical rank, and therefore, latent variable methods are shown to be well suited to obtain useful subspace models from them for treating a variety of important industrial problems. An overview of the important concepts behind latent variable models is presented and the methods are illustrated with industrial examples in the following areas: (i) the analysis of historical databases and trouble-shooting process problems;(ii) process monitoring and FDI;(iii) extraction of information from novel multivariate sensors;(iv) process control in reduced dimensional subspaces. In each of these problems, latent variable models provide the framework on which solutions are based. (c) 2005 Elsevier Ltd. All rights reserved.
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