Merge pull request #2 from papers-we-love/master

update jun 2019

artificial_intelligence/README.md

@@ -10,3 +10,6 @@
    * [:open_file_folder: Summary of Papers](judea_pearl/)
 
 * [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf) by Silver et al.
+
+* [A Universal Music Translation Network (2018)](https://arxiv.org/pdf/1805.07848.pdf) by Noam Mor, Lior Wolf, Adam Polyak & Yaniv Taigman
+    > This paper proposes a method for translating music across musical instruments, genres, and styles. It is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-independent encoder enables translation even from musical domains that were not seen during training. The method is unsupervised and does not rely on supervision in the form of matched samples between domains or musical transcriptions. This method is evaluated on NSynth, as well as on a dataset collected from professional musicians, and achieve convincing translations, even when translating from whistling, potentially enabling the creation of instrumental music by untrained humans. 

combinatory_logic/README.md

@@ -1,12 +1,14 @@
 # Combinatory Logic
 
 * [Flattening Combinators: Surviving Without Parentheses]
-  by Chris Okasaki (2003)
+  by Chris Okasaki (2003) ([DOI])
 
 * [Combinatorial Analysis and Computers]
   by Marshall Hall Jr. and Donald E. Knuth (1965)
 
 [Flattening Combinators: Surviving Without Parentheses]:
-    http://www.westpoint.edu/eecs/SiteAssets/SitePages/Faculty%20Publication%20Documents/Okasaki/jfp03flat.pdf
+    https://web.archive.org/web/20170828181221/http://www.westpoint.edu:80/eecs/SiteAssets/SitePages/Faculty%20Publication%20Documents/Okasaki/jfp03flat.pdf
+[DOI]:
+    https://doi.org/10.1017/S0956796802004483
 [Combinatorial Analysis and Computers]:
     http://poncelet.math.nthu.edu.tw/disk5/js/computer/hall-knuth.pdf

data_structures/README.md

@@ -4,6 +4,7 @@
 * [Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue Algorithms](http://www.research.ibm.com/people/m/michael/podc-1996.pdf)
 * [RRB-Trees: Efficient Immutable Vectors](http://infoscience.epfl.ch/record/169879/files/RMTrees.pdf)
 * [Cuckoo Hashing](http://www.it-c.dk/people/pagh/papers/cuckoo-jour.pdf)
+* [Fenwick Tree](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.8917&rep=rep1&type=pdf)
 * [Hopscotch Hashing](http://mcg.cs.tau.ac.il/papers/disc2008-hopscotch.pdf)
 
 ## Hosted Papers

distributed_systems/README.md

@@ -9,7 +9,7 @@
 
 ## External Papers
 
-* [:scroll:](a-note-on-distributed-computing.pdf) [A Note on Distributed Computing](http://www.eecs.harvard.edu/~waldo/Readings/waldo-94.pdf)
+* [:scroll:](a-note-on-distributed-computing.pdf) [A Note on Distributed Computing](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5A960A7B16A7BB42A99D7B6EFC30FB25?doi=10.1.1.41.7628&rep=rep1&type=pdf)
 
 * [A simple totally ordered broadcast protocol](http://diyhpl.us/~bryan/papers2/distributed/distributed-systems/zab.totally-ordered-broadcast-protocol.2008.pdf)
 

machine_learning/README.md

@@ -19,6 +19,12 @@
 
 * [Multiple Narrative Disentanglement: Unraveling *Infinite Jest*](http://dreammachin.es/p1-wallace.pdf) - uses an unsupervised approach to natural language processing to classify narrators in David Foster Wallace's 1,000-page novel.
 
+* [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - This paper introduces AlexNet, a neural network architecture which dramatically improved over the state-of-the-art in image classification algorithms and is widely regarded as a breakthrough moment for deep learning.
+
+* [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf) - This paper introduces the foundations of the rapidly emerging field of interpretable machine learning.
+
+* [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf) - This seminal paper introduces a method to distill information from an ensemble of neural networks into a single model.
+
 ## Hosted Papers
 
 * :scroll: **[A Sparse Johnson-Lindenstrauss Transform](dimensionality_reduction/a-sparse-johnson-lindenstrauss-transform.pdf)**

software_engineering/README.md

@@ -0,0 +1,10 @@
+## Software Engineering 
+
+* [Studying Attitudes and Social Norms in Agile Software Development (2019)](https://arxiv.org/pdf/1904.09225.pdf) by Lucas Gren
+    > This paper reviews research on attitudes and social norms and connect it to the agile software development context. Author proposes additional theories from social psychology that would most certainly be useful for further sense-making of human factors-related research on agile teams.
+
+* [Happiness and the productivity of software engineers (2019)](https://arxiv.org/ftp/arxiv/papers/1904/1904.08239.pdf) by Daniel Graziotin & Fabian Fagerholm
+    > This paper provides an overview of studies on the happiness of software developers. You will learn why it is important to make software developers happy, how happy they really are, what makes them unhappy, and what is expected for their productivity while developing software.
+
+* [Why Software Projects need Heroes (Lessons Learned from 1100+ Projects) (2019)](https://arxiv.org/pdf/1904.09954.pdf) by Suvodeep Majumder, Joymallya Chakraborty, Amritanshu Agrawal & Tim Menzies
+    > This paper explores the effect of having heroes in projects, from a code quality perspective. Authors identify the hero developers of 1100+ open source GitHub projects. Based on the analysis, they find the majority of all projects are *"hero" projects* and commits from *hero developers* (who contribute the most code) result in far fewer bugs than other developers -- which is contrary to the literature.