Clusters defined over a dataset by unsupervised clustering often present groupings which differ from the expected solution. This is primarily the case when some scarce knowledge of the problem exists beforehand that partially identifies desired characteristics of clusters. However conventional clustering algorithms are not defined to expect any supervision from the external world, as they are supposed to be completely unsupervised. As a result they can not benefit or effectively take into account available information about the use or properties of the clusters. In this paper we propose a reinforcement learning approach to address this problem where existing, unmodified unsupervised clustering algorithms are augmented in a way that the available sparse information is utilized to achieve more appropriate clusters. Our model works with any clustering algorithm, but the input to the algorithm, instead of being the original dataset, is a scaled version of the same, where the scaling factors are determined by the reinforcement learning algorithm.

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.

Modeling and simulation of human activities is becoming a hot research area for validating activity recognition algo- rithms used to generate useful synthetic datasets for assis- tive environments and other smart spaces. Context-driven simulation, an emerging approach that utilizes abstract structures of state spaces (contexts), can enhance the scala- bility and realism of simulations. However, the context- driven approach is demanding of users’ efforts in specifying not only activity models, but also the corresponding con- texts and contextual transitions associated with these activi- ties. In this paper, we propose a method to reduce users’ ef- forts in configuring simulation by using k-means clustering and principal component analysis approaches to automate the derivation of contexts from a given set of activities. We validate our approach by comparing the actual sequenced activities with the derived sequenced activities.

Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than 0.70. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

Identifying the patterns in urban mobility is important for a variety of tasks such as transportation planning, urban resource allocation, emergency planning etc. This is evident from the large body of research on the topic, which has exploded with the vast amount of geo-tagged user-generated content from online social media. However, most of the existing work focuses on a specific setting, taking a statistical approach to describe and model the observed patterns. On the contrary in this work we introduce EigenTransitions, a spectrum-based, generic framework for analyzing spatio-temporal mobility datasets. EigenTransitions capture the anatomy of the aggregate and/or individuals’ mobility as a compact set of latent mobility patterns. Using a large corpus of geo-tagged content collected from Twitter, we utilize EigenTransitions to analyze the structure of urban mobility. In particular, we identify the EigenTransitions of a flow network between urban areas and derive hypothesis testing framework to evaluate urban mobility from both temporal and demographic perspectives. We further show how EigenTransitions not only identify latent mobility patterns, but also have the potential to support applications such as mobility prediction and inter-city comparisons. In particular, by identifying neighbors with similar latent mobility patterns and incorporating their historical transition behaviors, we proposed an EigenTransitions-based k-nearest neighbor algorithm, which can significantly improve the performance of individual mobility prediction. The proposed method is especially effective in “cold-start” scenarios where traditional methods are known to perform poorly.

We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information and mutual information. The new definition significantly weakens differential privacy, while preserving its minimalistic design features such as composition over small group and multiple queries as well as closeness to post-processing. Moreover, we show that On-Average KL-Privacy is **equivalent** to generalization for a large class of commonly-used tools in statistics and machine learning that samples from Gibbs distributions---a class of distributions that arises naturally from the maximum entropy principle. In addition, a byproduct of our analysis yields a lower bound for generalization error in terms of mutual information which reveals an interesting interplay with known upper bounds that use the same quantity.

The scikit-learn library is one of the most popular platforms for everyday machine learning and data science. The reason is because it is built upon Python, a fully featured programming language. But how do you get started with machine learning with scikit-learn. Kevin Markham is a data science trainer who created a series of 9 videos that show you exactly how to get started in machine learning with scikit-learn. In this post you will discover this series of videos and exactly what is covered, step-by-step to help you decide if the material will be useful to you.

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. Evaluate and apply the linear regression technique to estimate the relationships among variables. Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it.

Editor's note: Yesterday we posted the first 4 tips & ticks of this fascinating post. Be sure to have a look before reading the second half. One of the crucial factors in deep networks is activation function, which brings the non-linearity into networks. Here we will introduce the details and characters of some popular activation functions and give advices later in this section. The sigmoid non-linearity has the mathematical form?