ݺߣshows by User: JiangZhu / http://www.slideshare.net/images/logo.gif ݺߣshows by User: JiangZhu / Mon, 01 Jun 2015 19:49:43 GMT ݺߣShare feed for ݺߣshows by User: JiangZhu Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-Series /slideshow/defense-jiang0429/48858166 defense-jiang-0429-150601194943-lva1-app6892
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.]]>
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.]]> Mon, 01 Jun 2015 19:49:43 GMT /slideshow/defense-jiang0429/48858166 JiangZhu@slideshare.net(JiangZhu) Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-Series JiangZhu Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/defense-jiang-0429-150601194943-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.
Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-Series from Jiang Zhu
]]> 720 3 https://cdn.slidesharecdn.com/ss_thumbnails/defense-jiang-0429-150601194943-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Core of Personalization at Polyvore: Style Profile /slideshow/post1-coreofpersonalizationatpolyvorestyleprofile-1/43669580 post1coreofpersonalizationatpolyvorestyleprofile1-150119115447-conversion-gate02
Over the past year, our engineering team has undertaken the task of creating a more personalized experience for our users. We already have an amazing community of designers, artists, and fashion enthusiasts who come to Polyvore to get inspired around shopping. However, we felt that with a little bit of machine learning we could help users discover and shop for even more products that they may not have found on their own. In this blog post well walk through some of the ways we are using machine learning to understand our users individual style, which we call a Style Profile, to recommend more personalized products and outfits.]]>
Over the past year, our engineering team has undertaken the task of creating a more personalized experience for our users. We already have an amazing community of designers, artists, and fashion enthusiasts who come to Polyvore to get inspired around shopping. However, we felt that with a little bit of machine learning we could help users discover and shop for even more products that they may not have found on their own. In this blog post well walk through some of the ways we are using machine learning to understand our users individual style, which we call a Style Profile, to recommend more personalized products and outfits.]]> Mon, 19 Jan 2015 11:54:47 GMT /slideshow/post1-coreofpersonalizationatpolyvorestyleprofile-1/43669580 JiangZhu@slideshare.net(JiangZhu) Core of Personalization at Polyvore: Style Profile JiangZhu Over the past year, our engineering team has undertaken the task of creating a more personalized experience for our users. We already have an amazing community of designers, artists, and fashion enthusiasts who come to Polyvore to get inspired around shopping. However, we felt that with a little bit of machine learning we could help users discover and shop for even more products that they may not have found on their own. In this blog post well walk through some of the ways we are using machine learning to understand our users individual style, which we call a Style Profile, to recommend more personalized products and outfits. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/post1coreofpersonalizationatpolyvorestyleprofile1-150119115447-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Over the past year, our engineering team has undertaken the task of creating a more personalized experience for our users. We already have an amazing community of designers, artists, and fashion enthusiasts who come to Polyvore to get inspired around shopping. However, we felt that with a little bit of machine learning we could help users discover and shop for even more products that they may not have found on their own. In this blog post well walk through some of the ways we are using machine learning to understand our users individual style, which we call a Style Profile, to recommend more personalized products and outfits.
Core of Personalization at Polyvore: Style Profile from Jiang Zhu
]]> 947 5 https://cdn.slidesharecdn.com/ss_thumbnails/post1coreofpersonalizationatpolyvorestyleprofile1-150119115447-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds document 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data and Internet of Things: A Roadmap For Smart Environments, Fog Computing: A Platform for Internet of Things and Analytics /slideshow/314276-1-en7chapteronlinepdf/32291756 3142761en7chapteronlinepdf-140313174342-phpapp01
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]]> Thu, 13 Mar 2014 17:43:42 GMT /slideshow/314276-1-en7chapteronlinepdf/32291756 JiangZhu@slideshare.net(JiangZhu) Big Data and Internet of Things: A Roadmap For Smart Environments, Fog Computing: A Platform for Internet of Things and Analytics JiangZhu <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3142761en7chapteronlinepdf-140313174342-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Big Data and Internet of Things: A Roadmap For Smart Environments, Fog Computing: A Platform for Internet of Things and Analytics from Jiang Zhu
]]> 2309 8 https://cdn.slidesharecdn.com/ss_thumbnails/3142761en7chapteronlinepdf-140313174342-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 KeySens: Passive User Authentication Through Micro Behavior Modeling of Soft Keyboard Interaction /slideshow/mobi-case-keysens-presentation/30364300 mobicasekeysenspresentation-140123162945-phpapp02
KeySens: Passive User Authentication through Micro behavior Modeling of Soft Keyboard Interaction ]]>
KeySens: Passive User Authentication through Micro behavior Modeling of Soft Keyboard Interaction ]]> Thu, 23 Jan 2014 16:29:45 GMT /slideshow/mobi-case-keysens-presentation/30364300 JiangZhu@slideshare.net(JiangZhu) KeySens: Passive User Authentication Through Micro Behavior Modeling of Soft Keyboard Interaction JiangZhu KeySens: ?Passive User Authentication through Micro behavior Modeling ?of Soft Keyboard Interaction <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mobicasekeysenspresentation-140123162945-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> KeySens: ?Passive User Authentication through Micro behavior Modeling ?of Soft Keyboard Interaction
KeySens: Passive User Authentication Through Micro Behavior Modeling of Soft Keyboard Interaction from Jiang Zhu
]]> 1599 8 https://cdn.slidesharecdn.com/ss_thumbnails/mobicasekeysenspresentation-140123162945-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Art and Science of Web Sites Performance: A Front-end Approach /slideshow/webperf-20120907/29191611 webperf-2012-09-07-131213162232-phpapp01
People love fast web sites, but up until now developers have been focusing on the wrong area. Network (TCP, buffers, routing) performance and Backend (web server, database, etc.) performance are important for reducing hardware costs and improving efficiency, but for most pages 80% of the load time is spent on the frontend (HTML, CSS, JavaScript, images, iframes, and others). We will talk about the best practices for making web pages faster, provide case study from top web site, and introduce the tools we use for researching performance. In addition to know how to improve web performance, we will also try to gain an understanding of the fundamentals of how the Internet works including DNS, HTTP, and browsers. This talks was given as an Educational Series called Fog Computing Reading Group at Cisco Advanced Architecture and Research. The content is derived from the materials by Steven Sounders (Google/Stanford), Collin Jackson (Stanford/CMU) and Daniel Austin (eBay). ]]>
People love fast web sites, but up until now developers have been focusing on the wrong area. Network (TCP, buffers, routing) performance and Backend (web server, database, etc.) performance are important for reducing hardware costs and improving efficiency, but for most pages 80% of the load time is spent on the frontend (HTML, CSS, JavaScript, images, iframes, and others). We will talk about the best practices for making web pages faster, provide case study from top web site, and introduce the tools we use for researching performance. In addition to know how to improve web performance, we will also try to gain an understanding of the fundamentals of how the Internet works including DNS, HTTP, and browsers. This talks was given as an Educational Series called Fog Computing Reading Group at Cisco Advanced Architecture and Research. The content is derived from the materials by Steven Sounders (Google/Stanford), Collin Jackson (Stanford/CMU) and Daniel Austin (eBay). ]]> Fri, 13 Dec 2013 16:22:32 GMT /slideshow/webperf-20120907/29191611 JiangZhu@slideshare.net(JiangZhu) Art and Science of Web Sites Performance: A Front-end Approach JiangZhu People love fast web sites, but up until now developers have been focusing on the wrong area. Network (TCP, buffers, routing) performance and Backend (web server, database, etc.) performance are important for reducing hardware costs and improving efficiency, but for most pages 80% of the load time is spent on the frontend (HTML, CSS, JavaScript, images, iframes, and others). We will talk about the best practices for making web pages faster, provide case study from top web site, and introduce the tools we use for researching performance. In addition to know how to improve web performance, we will also try to gain an understanding of the fundamentals of how the Internet works including DNS, HTTP, and browsers. This talks was given as an Educational Series called Fog Computing Reading Group at Cisco Advanced Architecture and Research. The content is derived from the materials by Steven Sounders (Google/Stanford), Collin Jackson (Stanford/CMU) and Daniel Austin (eBay). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webperf-2012-09-07-131213162232-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> People love fast web sites, but up until now developers have been focusing on the wrong area. Network (TCP, buffers, routing) performance and Backend (web server, database, etc.) performance are important for reducing hardware costs and improving efficiency, but for most pages 80% of the load time is spent on the frontend (HTML, CSS, JavaScript, images, iframes, and others). We will talk about the best practices for making web pages faster, provide case study from top web site, and introduce the tools we use for researching performance. In addition to know how to improve web performance, we will also try to gain an understanding of the fundamentals of how the Internet works including DNS, HTTP, and browsers. This talks was given as an Educational Series called Fog Computing Reading Group at Cisco Advanced Architecture and Research. The content is derived from the materials by Steven Sounders (Google/Stanford), Collin Jackson (Stanford/CMU) and Daniel Austin (eBay).
Art and Science of Web Sites Performance: A Front-end Approach from Jiang Zhu
]]> 2484 6 https://cdn.slidesharecdn.com/ss_thumbnails/webperf-2012-09-07-131213162232-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Improving Web Siste Performance Using Edge Services in Fog Computing Architecture /slideshow/web-perf2013/28937883 webperf2013-131205143032-phpapp02
We consider web optimization within Fog Computing context. We apply existing methods for web optimization in a novel manner, such that these methods can be combined with unique knowledge that is only available at the edge (Fog) nodes. More dynamic adaptation to the users conditions (eg. network status and devices computing load) can also be accomplished with network edge specific knowledge. As a result, a users webpage rendering performance is improved beyond that achieved by simply applying those methods at the webserver or CDNs.]]>
We consider web optimization within Fog Computing context. We apply existing methods for web optimization in a novel manner, such that these methods can be combined with unique knowledge that is only available at the edge (Fog) nodes. More dynamic adaptation to the users conditions (eg. network status and devices computing load) can also be accomplished with network edge specific knowledge. As a result, a users webpage rendering performance is improved beyond that achieved by simply applying those methods at the webserver or CDNs.]]> Thu, 05 Dec 2013 14:30:32 GMT /slideshow/web-perf2013/28937883 JiangZhu@slideshare.net(JiangZhu) Improving Web Siste Performance Using Edge Services in Fog Computing Architecture JiangZhu We consider web optimization within Fog Computing context. We apply existing methods for web optimization in a novel manner, such that these methods can be combined with unique knowledge that is only available at the edge (Fog) nodes. More dynamic adaptation to the users conditions (eg. network status and devices computing load) can also be accomplished with network edge specific knowledge. As a result, a users webpage rendering performance is improved beyond that achieved by simply applying those methods at the webserver or CDNs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webperf2013-131205143032-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We consider web optimization within Fog Computing context. We apply existing methods for web optimization in a novel manner, such that these methods can be combined with unique knowledge that is only available at the edge (Fog) nodes. More dynamic adaptation to the users conditions (eg. network status and devices computing load) can also be accomplished with network edge specific knowledge. As a result, a users webpage rendering performance is improved beyond that achieved by simply applying those methods at the webserver or CDNs.
Improving Web Siste Performance Using Edge Services in Fog Computing Architecture from Jiang Zhu
]]> 3755 7 https://cdn.slidesharecdn.com/ss_thumbnails/webperf2013-131205143032-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Guest Lecture: SenSec - Mobile Security through BehavioMetrics /slideshow/sen-sec-mobileseclecturejiangzhu/26835877 sensecmobileseclecturejiangzhu-131003132542-phpapp01
Guest Lecure 14-829: Mobile Security Course website: http://wnss.sv.cmu.edu/courses/14829/f13/]]>
Guest Lecure 14-829: Mobile Security Course website: http://wnss.sv.cmu.edu/courses/14829/f13/]]> Thu, 03 Oct 2013 13:25:42 GMT /slideshow/sen-sec-mobileseclecturejiangzhu/26835877 JiangZhu@slideshare.net(JiangZhu) Guest Lecture: SenSec - Mobile Security through BehavioMetrics JiangZhu Guest Lecure 14-829: Mobile Security Course website: http://wnss.sv.cmu.edu/courses/14829/f13/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sensecmobileseclecturejiangzhu-131003132542-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Guest Lecure 14-829: Mobile Security Course website: http://wnss.sv.cmu.edu/courses/14829/f13/
Guest Lecture: SenSec - Mobile Security through BehavioMetrics from Jiang Zhu
]]> 954 3 https://cdn.slidesharecdn.com/ss_thumbnails/sensecmobileseclecturejiangzhu-131003132542-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ICNC 2013 SenSec Presentation /slideshow/icnc-2013-sensec-presentation/16298894 sensecicnc2013-130201150729-phpapp02
We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms. ]]>
We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms. ]]> Fri, 01 Feb 2013 15:07:29 GMT /slideshow/icnc-2013-sensec-presentation/16298894 JiangZhu@slideshare.net(JiangZhu) ICNC 2013 SenSec Presentation JiangZhu We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sensecicnc2013-130201150729-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user&#39;s privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms.
ICNC 2013 SenSec Presentation from Jiang Zhu
]]> 469 3 https://cdn.slidesharecdn.com/ss_thumbnails/sensecicnc2013-130201150729-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 BehavioMetrics: A Big Data Approach /slideshow/behaviometrics-a-big-data-approach/15954167 behaviometrics-jiangzhu-public-130111141515-phpapp01
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.]]>
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.]]> Fri, 11 Jan 2013 14:15:15 GMT /slideshow/behaviometrics-a-big-data-approach/15954167 JiangZhu@slideshare.net(JiangZhu) BehavioMetrics: A Big Data Approach JiangZhu The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/behaviometrics-jiangzhu-public-130111141515-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users&#39; behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.
BehavioMetrics: A Big Data Approach from Jiang Zhu
]]> 1026 3 https://cdn.slidesharecdn.com/ss_thumbnails/behaviometrics-jiangzhu-public-130111141515-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Ƽ㷢չ״-2010 /slideshow/2010-14025701/14025701 2010-10-16-120821002839-phpapp02
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]]> Tue, 21 Aug 2012 00:28:38 GMT /slideshow/2010-14025701/14025701 JiangZhu@slideshare.net(JiangZhu) Ƽ㷢չ״-2010 JiangZhu <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2010-10-16-120821002839-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
ݬ硣NĽĥԸ人-2010 from Jiang Zhu
]]> 1201 3 https://cdn.slidesharecdn.com/ss_thumbnails/2010-10-16-120821002839-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SenSec: Mobile Application Security through Passive Sensing /slideshow/sensec/14025693 sensecfinal-120821002453-phpapp02
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SenSec: Mobile Application Security through Passive Sensing from Jiang Zhu
]]> 634 2 https://cdn.slidesharecdn.com/ss_thumbnails/sensecfinal-120821002453-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mobile privacysurvey presentation /slideshow/mobile-privacysurvey-presentation/14025680 mobileprivacysurvey-presentation-120821002315-phpapp01
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]]> Tue, 21 Aug 2012 00:23:12 GMT /slideshow/mobile-privacysurvey-presentation/14025680 JiangZhu@slideshare.net(JiangZhu) Mobile privacysurvey presentation JiangZhu <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mobileprivacysurvey-presentation-120821002315-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Mobile privacysurvey presentation from Jiang Zhu
]]> 592 3 https://cdn.slidesharecdn.com/ss_thumbnails/mobileprivacysurvey-presentation-120821002315-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Icccn2011 jiang-0802 /slideshow/icccn2011-jiang0802/14022590 icccn2011-jiang-0802-120820160745-phpapp02
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]]> Mon, 20 Aug 2012 16:07:43 GMT /slideshow/icccn2011-jiang0802/14022590 JiangZhu@slideshare.net(JiangZhu) Icccn2011 jiang-0802 JiangZhu <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icccn2011-jiang-0802-120820160745-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Icccn2011 jiang-0802 from Jiang Zhu
]]> 375 5 https://cdn.slidesharecdn.com/ss_thumbnails/icccn2011-jiang-0802-120820160745-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-JiangZhu-48x48.jpg?cb=1653280558 Specialties: Machine learning and data mining on network, social and other large scale data source. Data driven user behavior analysis and it's applications. Service routing. Network Distributed Database. Streaming video server, content delivery, P2P streaming, network appliance, layer 2 and layer 3 protocols, high performance network server design. Capability to build, grow and lead engineering teams with solid executions on software design and development process, close collaboration with TMEs and PMs, bootstrap, grow and manage virtual teams from different geographic locations and various cultures (US, Europe and China), and track records of delivering quality results in a timely ma... https://cdn.slidesharecdn.com/ss_thumbnails/defense-jiang-0429-150601194943-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/defense-jiang0429/48858166 Behaviometrics: Behavi... https://cdn.slidesharecdn.com/ss_thumbnails/post1coreofpersonalizationatpolyvorestyleprofile1-150119115447-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/post1-coreofpersonalizationatpolyvorestyleprofile-1/43669580 Core of Personalizatio... https://cdn.slidesharecdn.com/ss_thumbnails/3142761en7chapteronlinepdf-140313174342-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/314276-1-en7chapteronlinepdf/32291756 Big Data and Internet ...