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- item-1 hasContent mingxuan-sun.
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- 180 creator jan-odvarko.
- 180 creator john-barton.
- 180 type InProceedings.
- 180 label "Dynamic and Graphical Web Page Breakpoints".
- 180 sameAs 180.
- 180 abstract "Breakpoints are perhaps the quintessential feature of a debugger: they allow a developer to stop time and study the program state. Breakpoints are typically specified by selecting a line of of source code. For large, complex, web pages with multiple developers, the relevant source line for a given user interface problem may not be known to the developer. In this paper we describe the implementation of breakpoints in dynamically created source, and on error messages, network events, DOM mutation, DOM object property changes, and CSS style rule updates. Adding these domain-specific breakpoints to a general-purpose debugger for Javascript allows the developer to initiate the debugging process via Web page abstractions rather than lower level source code views. The breakpoints are implemented in the open source Firebug project, version 1.5, for the Firefox Web browser.".
- 180 hasAuthorList authorList.
- 180 isPartOf proceedings.
- 180 keyword "Processes".
- 180 keyword "methods for Web application development".
- 180 title "Dynamic and Graphical Web Page Breakpoints".
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- item-1 hasContent john-barton.
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- item-2 hasContent jan-odvarko.
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- 198 creator alessandra-sala.
- 198 creator ben-zhao.
- 198 creator christo-wilson.
- 198 creator haitao-zheng.
- 198 creator lili-cao.
- 198 creator robert-zablit.
- 198 type InProceedings.
- 198 label "Measurement-Calibrated Graph Models for Social Network Experiments".
- 198 sameAs 198.
- 198 abstract "Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Since gathering real graph datasets through direct measurements is costly in time and resources, researchers are anonymizing and distributing valuable datasets to the community. However, performing experiments using shared real datasets faces three significant disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large graph datasets, and the fact that the small number of available social graphs limits the statistical confidence in the results. The use of trace-driven graph models is an attractive alternative to sharing datasets. Researchers can ``fit'' a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of trace-driven synthetic graphs using six popular graph models and real social graphs measured from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one of these models produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.".
- 198 hasAuthorList authorList.
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- 198 keyword "Social data analysis".
- 198 keyword "analytics".
- 198 title "Measurement-Calibrated Graph Models for Social Network Experiments".
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- item-1 hasContent alessandra-sala.
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- item-6 hasContent ben-zhao.
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- 206 creator david-soukal.
- 206 creator fritz-behr.
- 206 creator hongwen-kang.
- 206 creator kuansan-wang.
- 206 creator zijian-zheng.
- 206 type InProceedings.
- 206 label "0-Cost Semisupervised Bot Detection for Search Engines".
- 206 sameAs 206.
- 206 abstract "In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. This is a crucial problem for web search engine because bot traffic significantly affect both the realtime search engine performance and the quality of the offline data-mining results of the search logs. However, the enormous amount of search data pose a challenging problem for traditional approaches that rely on fully annotated training samples. To this end, we propose a novel semi-supervised framework that addresses the problem in multiple fronts. First, we use the CAPTCHA technique and simple heuristics to extract from the data logs search sessions that are likely to be recordings of genuine human users and bot generated. This step outputs a large set of training samples with initial labels, though directly using these training data sources is problematic because they are biased samples of the whole data space and therefoe can lead a machine learning system to acquire a skewed decision boundary that does not generalize well for unseen data. To tackle this problem, we further develop a semi-supervised learning algorithm that takes advantage of unlabeled data to improve the classification performance. These two proposed algorithms are seamlessly combined and have the following advantages. First, it becomes very cost efficient to generate large number of labeled data to initialize the training process. Second, our semi-supervised learning approach is very effective and resilient against the bias issue in the data generation process. In our experiment, the proposed approach showed significant (i.e. 2~3:1) improvement compared to the traditional supervised approach.".
- 206 hasAuthorList authorList.
- 206 isPartOf proceedings.
- 206 keyword "Query log".
- 206 keyword "click trail".
- 206 keyword "traffic data mining".
- 206 title "0-Cost Semisupervised Bot Detection for Search Engines".
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