Rainbow Six Siege players around the world will not have to play a censored version of the game after a major U-turn by developers Ubisoft. The company announced earlier this month that it would be making substantial changes to the game's look to allow it to be played in China. Those included getting rid of symbols and decoration that used skulls, blood that was cleaned up and the disappearance of gambling machines. Despite assurances that the alterations were entirely superficial and would not affect gameplay, fans were immediately angry that such significant changes were being made wherever they were in the world. Though the alterations were in response to demands from the Chinese government, they were rolled out globally.

In this post, you will learn about some of the bias mitigation strategies that can be applied in ML Model Development lifecycle (MDLC) to achieve discrimination-aware Machine Learning models. The primary objective is to achieve a higher accuracy model while ensuring that the models are lesser discriminant in relation to sensitive/protected attributes. In simple words, the output of the classifier should not correlate with protected or sensitive attributes. Building such ML models becomes the multi-objective optimization problem. The quality of the classifier is measured by its accuracy and the discrimination it makes on the basis of sensitive attributes; the more accurate, the better, and the less discriminant (based on sensitive attributes), the better.

It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

In discussions hosted on discussion forums for Massive Online Open Courses (MOOCs), references to online learning resources are often of central importance. However they are usually mentioned in free text, without appropriate hyperlinking to their associated resource. Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within MOOC forums, and also benefit the contextualization of such resources across disparate views. We propose the novel problem of learning resource mention identification inMOOC forums; i.e., to identify resource mentions in discussions, and classify them into predefined resource types. As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset - dubbed the Forum Resource Mention (FoRM) dataset - to facilitate our current research and future research on this task. FoRM contains over 10, 000 real-world forum threads in collaboration with Coursera, with more than 23, 000 manually labeled resource mentions. We then formulate this task as a sequence tagging problem and investigate solutionarchitectures to address the problem. Corresponding author Email address: peterpan10211020@gmail.com (Liangming Pan) Preprint submitted to Elsevier November 22, 2018 two major challenges that hinder the application of sequence tagging models tothe task: (1) the diversity of resource mention expression, and (2) long-range contextual dependencies. We address these challenges by incorporating character-leveland thread context information into a LSTM-CRF model. First, we incorporate a character encoder to address the out-ofvocabulary problemcaused by the diversity of mention expressions. Second, to address the context dependency challenge, we encode thread contexts using anRNN-based context encoder, and apply the attention mechanism to selectively leverage useful context information during sequence tagging. Experiments onFoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify the two challenges.

On the 3:13 pm train out of San Jose on a recent Friday, I hunched over a Macbook, brow furrowed. Hundreds of miles north in a Google datacenter in Oregon, a virtual computer sprang to life. I was soon looking at the yawning blackness of a Linux command line--my new AI art studio. Some hours of Googling, mistyped commands, and muttered curses later, I was cranking out eerie portraits. I may reasonably be considered "good" with computers, but I'm no coder; I flunked out of Codecademy's easy-on-beginners online JavaScript course.

Abstract--As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graphbased architectureis employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design. I. INTRODUCTION Various models have been thoroughly investigated by the machine learning (ML) community. In theory, these models are general and applicable to both academia and industry. However, it could be time-consuming to build a solution on a specific ML task, even for a ML expert.

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers not only to accurately classify the seen classes, but also to effectively deal with the unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, experiment setup and evaluation metrics. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also overview the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.

Learning management systems are not new to corporate learning; they have been around for quite some time and each year more and more are released. What an LMS basically does is host, distribute, record and report on all learning that goes on within an organization. Apart from that, there are many more additional features that companies ask for and expect today. Probably the most difficult one to incorporate is tracking all informal learning and using the information to provide highly personalized learning. The LMS is the critical component to the entire e-learning program, acting both as the foundation (by incorporating all the modules) and as the engine (by providing the environment in which learners can access them and suggesting various topics based on curriculum and personal interest).

MIYAZAKI – Like many of Japan's smaller cities, Miyazaki has been hit by a growing labor crunch, a trend highlighted by the mere 56.8 percent of high school graduates that chose to remain in the prefecture to work -- the third worst among the country's 47 prefectures. In the hard-hit information technology sector, the city has been encouraging firms to run businesses there to help energize the area, said Tsugunobu Ogino, president of KJS Co., a Miyazaki-based IT firm that makes e-learning systems. "But they are struggling to find engineers, since many move to Tokyo," he said. Now, the city in the southern Kyushu region may have found an unexpected solution, one thousands of kilometers away: Bangladesh. The South Asian nation faces a scenario that is almost the complete inverse of Japan -- there are simply not enough jobs for its ample working population.

So, you've finished a few Unity tutorials and created a game. Now, you would like to set up an authentication system for it but don't know how? This is a tutorial for you! Through this course, you'll discover how to create a backend layer to store and retrieve data for your video games. You'll learn SQL and PHP basics and understand how Unity interacts with other systems.