CVPRに採択されたドメイン適応の論文を日本まとめました。
* Domain Adaptive Faster R-CNN for Object Detection in the Wild
* Learning to Adapt Structured Output Space for Semantic Segmentation
2018/07/01のCVPR2018読み会の発表資料です。
論文
- ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
- Learning to Adapt Structured Output Space for Semantic Segmentation
Mining and Managing Large-scale Linked Open DataMOVING Project
Linked Open Data (LOD) is about publishing and interlinking data of different origin and purpose on the web. The Resource Description Framework (RDF) is used to describe data on the LOD cloud. In contrast to relational databases, RDF does not provide a fixed, pre-defined schema. Rather, RDF allows for flexibly modeling the data schema by attaching RDF types and properties to the entities. Our schema-level index called SchemEX allows for searching in large-scale RDF graph data. The index can be efficiently computed with reasonable accuracy over large-scale data sets with billions of RDF triples, the smallest information unit on the LOD cloud. SchemEX is highly needed as the size of the LOD cloud quickly increases. Due to the evolution of the LOD cloud, one observes frequent changes of the data. We show that also the data schema changes in terms of combinations of RDF types and properties. As changes cannot capture the dynamics of the LOD cloud, current work includes temporal clustering and finding periodicities in entity dynamics over large-scale snapshots of the LOD cloud with about 100 million triples per week for more than three years.
Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...Ansgar Scherp
We propose a pipeline for text extraction from infographics
that makes use of a novel combination of data mining and computer vision techniques. The pipeline defines a sequence of steps to identify characters, cluster them into text lines, determine their rotation angle, and apply state-of-the-art OCR to recognize the text. In this paper, we formally define the pipeline and present its current implementation. In addition, we have conducted preliminary evaluations over a data corpus of 121 manually annotated infographics from a broad range of illustration types such as bar charts, pie charts, and line charts, maps, and others. We assess the results of our text extraction pipeline by comparing it with two baselines. Finally, we sketch an outline for future work and possibilities for improving the pipeline. - http://ceur-ws.org/Vol-1458/
• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
2018/07/01のCVPR2018読み会の発表資料です。
論文
- ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
- Learning to Adapt Structured Output Space for Semantic Segmentation
Mining and Managing Large-scale Linked Open DataMOVING Project
Linked Open Data (LOD) is about publishing and interlinking data of different origin and purpose on the web. The Resource Description Framework (RDF) is used to describe data on the LOD cloud. In contrast to relational databases, RDF does not provide a fixed, pre-defined schema. Rather, RDF allows for flexibly modeling the data schema by attaching RDF types and properties to the entities. Our schema-level index called SchemEX allows for searching in large-scale RDF graph data. The index can be efficiently computed with reasonable accuracy over large-scale data sets with billions of RDF triples, the smallest information unit on the LOD cloud. SchemEX is highly needed as the size of the LOD cloud quickly increases. Due to the evolution of the LOD cloud, one observes frequent changes of the data. We show that also the data schema changes in terms of combinations of RDF types and properties. As changes cannot capture the dynamics of the LOD cloud, current work includes temporal clustering and finding periodicities in entity dynamics over large-scale snapshots of the LOD cloud with about 100 million triples per week for more than three years.
Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...Ansgar Scherp
We propose a pipeline for text extraction from infographics
that makes use of a novel combination of data mining and computer vision techniques. The pipeline defines a sequence of steps to identify characters, cluster them into text lines, determine their rotation angle, and apply state-of-the-art OCR to recognize the text. In this paper, we formally define the pipeline and present its current implementation. In addition, we have conducted preliminary evaluations over a data corpus of 121 manually annotated infographics from a broad range of illustration types such as bar charts, pie charts, and line charts, maps, and others. We assess the results of our text extraction pipeline by comparing it with two baselines. Finally, we sketch an outline for future work and possibilities for improving the pipeline. - http://ceur-ws.org/Vol-1458/
• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
Polar Domain Discovery with Sparkler - EarthCubeKaranjeet Singh
Polar Deep Insights with Domain Discovery and Sparkler (Spark Crawler). Presented at EarthCube All Hands Meeting 2017! #ECAHM2017 #USCDataScience #IRDS
From Thousands of Hours to a Couple of Minutes: Automating Exploit Generation...Priyanka Aash
Writing a working exploit for a vulnerability is generally challenging, time-consuming, and labor-intensive. To address this issue, automated exploit generation techniques can be adopted. In practice, existing techniques however exhibit an insufficient ability to craft exploits, particularly for the kernel vulnerabilities. On the one hand, this is because their technical approaches explore exploitability only in the context of a crashing process whereas generating an exploit for a kernel vulnerability typically needs to vary the context of a kernel panic. On the other hand, this is due to the fact that the program analysis techniques used for exploit generation are suitable only for simple programs but not the OS kernel which has higher complexity and scalability.
In this talk, we will introduce and release a new exploitation framework to fully automate the exploitation of kernel vulnerabilities. Technically speaking, our framework utilizes a kernel fuzzing technique to diversify the contexts of a kernel panic and then leverages symbolic execution to explore exploitability under different contexts. We demonstrate that this new exploitation framework facilitates exploit crafting from many aspects.
First, it augments a security analyst with the ability to automate the identification of system calls that he needs to take advantages for vulnerability exploitation. Second, it provides security analysts with the ability to achieve security mitigation bypassing. Third, it allows security analysts to automatically generate exploits with different exploitation objectives (e.g., privilege escalation or data leakage). Last but not least, it equips security analysts with an ability to generate exploits even for those kernel vulnerabilities for which the exploitability has not yet been confirmed or verified.
Along with this talk, we will also release many unpublished working exploits against several kernel vulnerabilities. It should be noted that, the vulnerabilities we experimented cover primarily Use-After-Free and heap overflow. Among all these test cases, more than 50% of them do not have working exploits publicly available. To illustrate this release, I have already disclosed one working exploit at my personal website (http://ww9210.cn/). The exploit released on my site pertains to CVE-2017-15649 for which there has not yet been an exploit publicly available with the demonstration of bypassing SMAP.
A Semantic-Based Approach to Attain Reproducibility of Computational Environm...Idafen Santana Pérez
Slides from our presentation at the 1st International Workshop on Reproducibility in Parallel Computing (REPPAR'14) in conjunction with Euro-Par 2014 (August 25-29)
AUTOMATED DATA EXPLORATION - Building efficient analysis pipelines with DaskVíctor Zabalza
# Talk given at PyCon UK 2017
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.
In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
Polar Domain Discovery with Sparkler - EarthCubeKaranjeet Singh
Polar Deep Insights with Domain Discovery and Sparkler (Spark Crawler). Presented at EarthCube All Hands Meeting 2017! #ECAHM2017 #USCDataScience #IRDS
From Thousands of Hours to a Couple of Minutes: Automating Exploit Generation...Priyanka Aash
Writing a working exploit for a vulnerability is generally challenging, time-consuming, and labor-intensive. To address this issue, automated exploit generation techniques can be adopted. In practice, existing techniques however exhibit an insufficient ability to craft exploits, particularly for the kernel vulnerabilities. On the one hand, this is because their technical approaches explore exploitability only in the context of a crashing process whereas generating an exploit for a kernel vulnerability typically needs to vary the context of a kernel panic. On the other hand, this is due to the fact that the program analysis techniques used for exploit generation are suitable only for simple programs but not the OS kernel which has higher complexity and scalability.
In this talk, we will introduce and release a new exploitation framework to fully automate the exploitation of kernel vulnerabilities. Technically speaking, our framework utilizes a kernel fuzzing technique to diversify the contexts of a kernel panic and then leverages symbolic execution to explore exploitability under different contexts. We demonstrate that this new exploitation framework facilitates exploit crafting from many aspects.
First, it augments a security analyst with the ability to automate the identification of system calls that he needs to take advantages for vulnerability exploitation. Second, it provides security analysts with the ability to achieve security mitigation bypassing. Third, it allows security analysts to automatically generate exploits with different exploitation objectives (e.g., privilege escalation or data leakage). Last but not least, it equips security analysts with an ability to generate exploits even for those kernel vulnerabilities for which the exploitability has not yet been confirmed or verified.
Along with this talk, we will also release many unpublished working exploits against several kernel vulnerabilities. It should be noted that, the vulnerabilities we experimented cover primarily Use-After-Free and heap overflow. Among all these test cases, more than 50% of them do not have working exploits publicly available. To illustrate this release, I have already disclosed one working exploit at my personal website (http://ww9210.cn/). The exploit released on my site pertains to CVE-2017-15649 for which there has not yet been an exploit publicly available with the demonstration of bypassing SMAP.
A Semantic-Based Approach to Attain Reproducibility of Computational Environm...Idafen Santana Pérez
Slides from our presentation at the 1st International Workshop on Reproducibility in Parallel Computing (REPPAR'14) in conjunction with Euro-Par 2014 (August 25-29)
AUTOMATED DATA EXPLORATION - Building efficient analysis pipelines with DaskVíctor Zabalza
# Talk given at PyCon UK 2017
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.
In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
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Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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2. • Domain Adaptive Faster R-CNN for Object Detection in theWild.
• Y. Tsai et al.
• CVPR 2018
• Learning to Adapt Structured Output Space for Semantic Segmentation.
• Y. Chen et al.
• CVPR 2018
• CVPR accept,
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