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Year Published

391 Results

November 1, 2017

High-Resolution Measurement of Data Center Microbursts

ACM Internet Measurement Conference

In this study, we explore the fine-grained behaviors of a large production data center using extremely highresolution measurements (10s to 100s of microsecond) of rack-level traffic. Our results show that characterizing network events like congestion and synchronized behavior in data centers does indeed require the use of such measurements.

By: Qiao Zhang, Vincent Liu, James Hongyi Zeng, Arvind Krishnamurthy
November 1, 2017

An Empirical Characterization of IFTTT: Ecosystem, Usage, and Performance

ACM Internet Measurement Conference 2017

IFTTT is a popular trigger-action programming platform whose applets can automate more than 400 services of IoT devices and web applications. We conduct an empirical study of IFTTT using a combined approach of analyzing data collected for 6 months and performing controlled experiments using a custom testbed.

By: Xianghang Mi, Feng Qian, Ying Zhang, XiaoFeng Wang
October 30, 2017

Crowd Intelligence Enhances Automated Mobile Testing

Automated Software Engineering Conference (ASE)

In this paper we show that information extracted from crowdbased testing can enhance automated mobile testing and introduce POLARIZ, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events.

By: Ke Mao, Mark Harman, Yue Jia
October 28, 2017

SVE: Distributed Video Processing at Facebook Scale

Symposium on Operating Systems Principles (SOSP)

This paper describes the evolution from our initial monolithic encoding script (MES) system to our current Streaming Video Engine (SVE) to upload and process videos. SVE has been in production since the fall of 2015, provides lower latency than MES, supports many diverse video applications, and has proven to be reliable despite faults and overload.

By: Qi Huang, Petchean Ang, Tomasz Nykiel, Iaroslav Tverdokhlib, Amit Yajurvedi, Paul Dapolito IV, Xifan Yan, Maxim Bykov, Chuen Liang, Mohit Talwar, Abhishek Mathur, Sachin Kulkarni, Matthew Burke, Wyatt Lloyd
October 28, 2017

Canopy: An End-to-End Performance Tracing and Analysis System

Symposium on Operating Systems Principles (SOSP)

This paper presents Canopy, Facebook’s end-to-end performance tracing infrastructure. Using Canopy, Facebook engineers can query and analyze performance data in real-time.

By: Jonathan Kaldor, Jonathan Mace, Michał Bejda, Edison Gao, Wiktor Kuropatwa, Joe O’Neill, Kian Win Ong, Bill Schaller, Pingjia Shan, Brendan Viscomi, Vinod Venkataraman, Kaushik Veeraraghavan, Yee Jiun Song
October 25, 2017

DodecaPen: Accurate 6DoF Tracking of a Passive Stylus

ACM Symposium on User Interface Software and Technology (UIST)

We propose a system for real-time six degrees of freedom (6DoF) tracking of a passive stylus that achieves sub-millimeter accuracy, which is suitable for writing or drawing in mixed reality applications.

By: Po-Chen Wu, Robert Wang, Kenrick Kin, Christopher Twigg, Shangchen Han, Ming-Hsuan Yang, Shao-Yi Chien
Areas: AR/VR
October 24, 2017

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

AAAI Conference on Human Computation and Crowdsourcing (HCOMP)

In this work, we design a cooperative game – GuessWhich – to measure human-AI team performance in the specific context of the AI being a visual conversational agent.

By: Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
October 22, 2017

Predicting Deeper into the Future of Semantic Segmentation

International Conference on Computer Vision (ICCV)

The ability to predict and therefore to anticipate the future is an important attribute of intelligence. We introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future.

By: Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann LeCun
October 22, 2017

Mask R-CNN

International Conference on Computer Vision (ICCV)

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

By: Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick
October 22, 2017

Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

International Conference on Computer Vision (ICCV)

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. To address the challenges in this area, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions.

By: Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele