The Evolution of Hadoop at Spotify - Through Failures and PainRafał Wojdyła
The quickest way to learn and evolve infrastructure is by encountering obstacles and being forced to overcome limitations that keep you inches away from project goals. At Spotify, we’ve encountered many of these obstacles and frustrations as we grew our Hadoop cluster from a few machines in an office closet aggregating played song events for financial reports, to our current 900 node cluster that plays a large role in many features that you see in our application today.
Two members of Spotify’s Hadoop ‘squad’ will weave in war stories, failures, frustrations and lessons learned to describe the Hadoop/Big Data architecture at Spotify and talk about how that architecture has evolved.
We’ll talk about how and why we use a number of tools, including Apache Falcon and Apache Bigtop to test changes; Apache Crunch, Scalding and Hive w/ Tez to build features and provide analytics; and Snakebite and Luigi, two in-house tools created to overcome common frustrations.
Tutorial on neural vocoders at the 2021 Speech Processing Courses in Crete, "Inclusive Neural Speech Synthesis."
Presenters: Xin Wang and Junichi Yamagishi, National Institute of Informatics, Japan
From the NYC Machine Learning meetup on Jan 17, 2013: http://www.meetup.com/NYC-Machine-Learning/events/97871782/
Video is available here: http://vimeo.com/57900625
The Evolution of Hadoop at Spotify - Through Failures and PainRafał Wojdyła
The quickest way to learn and evolve infrastructure is by encountering obstacles and being forced to overcome limitations that keep you inches away from project goals. At Spotify, we’ve encountered many of these obstacles and frustrations as we grew our Hadoop cluster from a few machines in an office closet aggregating played song events for financial reports, to our current 900 node cluster that plays a large role in many features that you see in our application today.
Two members of Spotify’s Hadoop ‘squad’ will weave in war stories, failures, frustrations and lessons learned to describe the Hadoop/Big Data architecture at Spotify and talk about how that architecture has evolved.
We’ll talk about how and why we use a number of tools, including Apache Falcon and Apache Bigtop to test changes; Apache Crunch, Scalding and Hive w/ Tez to build features and provide analytics; and Snakebite and Luigi, two in-house tools created to overcome common frustrations.
Tutorial on neural vocoders at the 2021 Speech Processing Courses in Crete, "Inclusive Neural Speech Synthesis."
Presenters: Xin Wang and Junichi Yamagishi, National Institute of Informatics, Japan
From the NYC Machine Learning meetup on Jan 17, 2013: http://www.meetup.com/NYC-Machine-Learning/events/97871782/
Video is available here: http://vimeo.com/57900625
Automatic Music Composition with Transformers, Jan 2021Yi-Hsuan Yang
An up-to-date version of slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/), focusing on introducing the following two publications from our group.
[1] "Pop Music Transformer: Beat-based modeling and generation of expressive Pop piano compositions," in Proc. ACM Multimedia, 2020.
[2] "Compound Word Transformer: Learning to compose full-song music over dynamic directed hypergraphs," in Proc. AAAI 2021.
For the last version of the slides, please visit: https://www2.slideshare.net/affige/research-on-automatic-music-composition-at-the-taiwan-ai-labs-april-2020/edit?src=slideview
MMCF: Multimodal Collaborative Filtering for Automatic Playlist ConitnuationHojin Yang
The slides used for presentation in the 'ecSys challenge workshop 2018'. The challenge is co-organized by Spotify. Our team('hello world!') won the 2nd place.
Machine Learning and Big Data for Music Discovery at SpotifyChing-Wei Chen
Spotify is the world’s largest on-demand music streaming company, with over 100 million active users who generate around 2TB of interaction data every day. With over 30 million songs to choose from, discovery and personalization play an essential role in helping users discover the best music for them. In this talk, given at the newly opened Galvanize space in NYC in March 2017, we’ll explain how Spotify uses Latent Space Models and Deep Learning to power features such as Discover Weekly and Release Radar.
Building Data Pipelines for Music Recommendations at SpotifyVidhya Murali
In this talk, we will get into the architectural and functional details as to how we build scalable and robust data pipelines for music recommendations at Spotify. We will also discuss some of the challenges and an overview of work to address these challenges.
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
Automatic Music Composition with Transformers, Jan 2021Yi-Hsuan Yang
An up-to-date version of slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/), focusing on introducing the following two publications from our group.
[1] "Pop Music Transformer: Beat-based modeling and generation of expressive Pop piano compositions," in Proc. ACM Multimedia, 2020.
[2] "Compound Word Transformer: Learning to compose full-song music over dynamic directed hypergraphs," in Proc. AAAI 2021.
For the last version of the slides, please visit: https://www2.slideshare.net/affige/research-on-automatic-music-composition-at-the-taiwan-ai-labs-april-2020/edit?src=slideview
MMCF: Multimodal Collaborative Filtering for Automatic Playlist ConitnuationHojin Yang
The slides used for presentation in the 'ecSys challenge workshop 2018'. The challenge is co-organized by Spotify. Our team('hello world!') won the 2nd place.
Machine Learning and Big Data for Music Discovery at SpotifyChing-Wei Chen
Spotify is the world’s largest on-demand music streaming company, with over 100 million active users who generate around 2TB of interaction data every day. With over 30 million songs to choose from, discovery and personalization play an essential role in helping users discover the best music for them. In this talk, given at the newly opened Galvanize space in NYC in March 2017, we’ll explain how Spotify uses Latent Space Models and Deep Learning to power features such as Discover Weekly and Release Radar.
Building Data Pipelines for Music Recommendations at SpotifyVidhya Murali
In this talk, we will get into the architectural and functional details as to how we build scalable and robust data pipelines for music recommendations at Spotify. We will also discuss some of the challenges and an overview of work to address these challenges.
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
The Skype for Business (Lync) apps are one of the ubiquitous aspect of the product. Mobility is cross platform (Android, IOS and Windows are supported), has specific requirements and (in Skype for Business) adds some specific limits for clients on authentication, security and features. As part of the default server features, mobility is now both easier and more critical to understand. In this session, we will see what has been made available for the mobile users and what will be released. Configurations, requirements and deployment suggestions will be explained for on-premises, Cloud and hybrid deployments
[2014 CodeEngn Conference 10] 심준보 - 급전이 필요합니다GangSeok Lee
2014 CodeEngn Conference 10
열혈 취약점 헌터들의 고분군투기!
취약점을 찾게되면 어떤 일이 벌어질까? 급전이 필요한 외롭고 찌질한 대한민국 해커들의 급전을 위한 취약점 찾기 여행기. 과연 우리는 취약점을 찾고 급전을 만들어 외롭고 찌질한 이 상황을 타개할 수 있을 것인가?
http://codeengn.com/conference/10
http://codeengn.com/conference/archive
AppCheck Pro 랜섬웨어 백신은 “상황 인식 기반 랜섬웨어 행위 탐지(Context-awareness based ransomware behavior detection)” 기술이 적용된 캅(CARB)엔진으로 현재까지 발견된 패턴 뿐 아니라 차후 출현 가능한 랜섬웨어까지도 탐지하여 기존 백신의 탐지 및 대응 방식으로는 빠르게 대응할 수 없는 랜섬웨어 위협으로부터 가장 확실하고 안전하게 방어할 수 있습니다
OWASP에 대응할 수 있는 애플리케이션/웹 보안 솔루션 앱스캔(AppScan)의 Source Editin에 대한 자료입니다.
App Scan Standard Edition에 대한 자료는 이쪽으로!
☞https://www.slideshare.net/eunoakcho/appscan-standard
OWASP에 대응할 수 있는 애플리케이션/웹 보안 솔루션 앱스캔(AppScan)의 Standard Editin에 대한 자료입니다.
AppScan의 다른 에디션인 Source Edition 에 대한 설명은 이쪽으로!
☞https://www.slideshare.net/eunoakcho/ibm-app-scan-source-edition
[Main Session] 보안을 고려한 애플리케이션 개발 공정 및 실무적 수행 방법 소개 Oracle Korea
애플리케이션 개발부터 배포 및 운영에 이르는 전 단계에서 보안을 강화하는 개념인 “소프트웨어 보안 보증”(Software Security Assurance)의 개념과 안전한 소프트웨어 개발 공정(S-SDLC), 그리고 실제 적용 방법을 간단하게 살펴보고자 합니다. 구체적으로 젠킨스와 Secure Coding 솔루션인 정적 보안 분석 툴을 연계하여 소스 코드의 취약점을 진단하는 과정을 살펴보고, 보안적으로 취약한 웹 애플리케이션을 Production 환경에서 보호하는 RASP 기술도 함께 시연할 예정입니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
[2013 CodeEngn Conference 09] 제갈공맹 - MS 원데이 취약점 분석 방법론GangSeok Lee
2013 CodeEngn Conference 09
MS 윈도우의 원데이 패치에 대해서 분석 및 접근 방법을 살펴본다. 또한, 최근에 나온 원데이 취약점 패치 분석을 진행하며 필요한 팁에 대해서 알아보고자 한다.
http://codeengn.com/conference/09
http://codeengn.com/conference/archive
Similar to 백신 프로그램의 원리와 동작 차민석 20151117_security plus 발표판 (20)
Targeted attacks on major industry sectors in south korea 20171201 cha minseo...Minseok(Jacky) Cha
Targeted Attacks on Major Industry Sectors in South Korea
Andariel group, Threat group behind Operation Red Dot, Threat group behind Operation Bitter Biscuit
