Supervised Learning

Analyzing User Activities Using Vector Space Model in Online Social Networks Machine Learning

The increasing popularity of internet, wireless technologies and mobile devices has led to the birth of mass connectivity and online interaction through Online Social Networks (OSNs) and similar environments. OSN reflects a social structure consist of a set of individuals and different types of ties like connections, relationships, interactions etc among them and helps its users to connect with their friends and common interest groups, share views and to pass information. Now days the users choose OSN sites as a most preferred place for sharing their updates, different views, posting photographs and would like to make it available for others for viewing, rating and making comments. The current paper aims to explore and analyze the association between the objects (like photographs, posts etc) and its viewers (friends, acquaintances etc) for a given user and to find activity relationship among them by using the TF-IDF scheme of Vector Space Model. After vectorization the vector data has been presented through a weighted graph with various properties.

Only 1 in 5 enterprises have DMARC records set up with an enforcement policy


Security company Vailmail released the Summer 2019 Email Fraud Landscape report on Tuesday highlighting recent efforts by enterprises to protect email accounts from cyberthreats. The report mostly focuses on the adoption rate of Domain-based Message Authentication, Reporting and Conformance (DMARC), a system that allows email domain owners to protect their domain from unauthorized use or "spoofing." Vailmail's researchers found that most enterprises were taking a positive step forward and saw a huge spike in DMARC adoption worldwide. Yet despite widespread adoption, the study found more than 90% of enterprise domains remain vulnerable to email impersonation attacks. By using DMARC and other similar authentication systems, domain owners can publish text files in the Domain Name System (DNS) laying out specific policies for how mail receivers should deal with unauthenticated email that appears to come from their domains.

Deep Metric Learning using Similarities from Nonlinear Rank Approximations Machine Learning

--In recent years, deep metric learning has achieved promising results in learning high dimensional semantic feature embeddings where the spatial relationships of the feature vectors match the visual similarities of the images. Similarity search for images is performed by determining the vectors with the smallest distances to a query vector . However, high retrieval quality does not depend on the actual distances of the feature vectors, but rather on the ranking order of the feature vectors from similar images. In this paper, we introduce a metric learning algorithm that focuses on identifying and modifying those feature vectors that most strongly affect the retrieval quality. We compute normalized approximated ranks and convert them to similarities by applying a nonlinear transfer function. These similarities are used in a newly proposed loss function that better contracts similar and disperses dissimilar samples. Experiments demonstrate significant improvement over existing deep feature embedding methods on the CUB-200-2011, Cars196, and Stanford Online Products data sets for all embedding sizes.

A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces Artificial Intelligence

Distributional word vectors have recently been shown to encode many of the human biases, most notably gender and racial biases, and models for attenuating such biases have consequently been proposed. However, existing models and studies (1) operate on under-specified and mutually differing bias definitions, (2) are tailored for a particular bias (e.g., gender bias) and (3) have been evaluated inconsistently and non-rigorously. In this work, we introduce a general framework for debiasing word embeddings. We operationalize the definition of a bias by discerning two types of bias specification: explicit and implicit. We then propose three debiasing models that operate on explicit or implicit bias specifications, and that can be composed towards more robust debiasing. Finally, we devise a full-fledged evaluation framework in which we couple existing bias metrics with newly proposed ones. Experimental findings across three embedding methods suggest that the proposed debiasing models are robust and widely applicable: they often completely remove the bias both implicitly and explicitly, without degradation of semantic information encoded in any of the input distributional spaces. Moreover, we successfully transfer debiasing models, by means of crosslingual embedding spaces, and remove or attenuate biases in distributional word vector spaces of languages that lack readily available bias specifications.

Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors Machine Learning

A new meta-algorithm for estimating the conditional average treatment effects is proposed in the paper. The main idea underlying the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions. Keywords: treatment effect, meta-learner, regression, treatment, control, simulation 1 Introduction One the most important problems in medicine is to choose the most appropriate treatment for a certain patient which may differ from other patients in her/his clinical or other characteristics [25]. With the increase of the amount of data and with the developing the electronic health record concept in medicine, there is a growing interest to apply machine learning methods to solve the problem of the most appropriate treatment by estimating treatment effects directly from observational data. The main peculiarity of observational data is that it contains past actions, their outcomes, but without direct access to the mechanism which gave rise to the action. Shalit at al. [34] give a clear example of observational data, when we have patient characteristics, medications (action), and outcomes, 1 arXiv:1909.03894v1

How do machine learning professionals use structured prediction?


Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.

Talk to Me: Nvidia Claims NLP Inference, Training Records


Nvidia says it's achieved significant advances in conversation natural language processing (NLP) training and inference, enabling more complex, immediate-response interchanges between customers and chatbots. And the company says it has a new language training model in the works that dwarfs existing ones. Nvidia said its DGX-2 AI platform trained the BERT-Large AI language model in less than an hour and performed AI inference in 2 milliseconds making "it possible for developers to use state-of-the-art language understanding for large-scale applications…." Training: Running the largest version of Bidirectional Encoder Representations from Transformers (BERT-Large) language model, an Nvidia DGX SuperPOD with 92 Nvidia DGX-2H systems running 1,472 V100 GPUs cut training from several days to 53 minutes. A single DGX-2 system trained BERT-Large in 2.8 days.

Nonparametric Contextual Bandits in an Unknown Metric Space Machine Learning

Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0,1] \to \mathbb{R}$ mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward functions, e.g. finite types or smooth with respect to an unknown metric space. We present a novel algorithm which learns data-driven similarities amongst the arms, in order to implement adaptive partitioning of the context-arm space for more efficient learning. We provide regret bounds along with simulations that highlight the algorithm's dependence on the local geometry of the reward functions.

Hottest day records set across Europe this year will soon be broken

New Scientist

If you suffered during the recent record-smashing heatwave across Europe, there's bad news. Such heatwaves are the new normal for countries like the UK, and we can expect even more extreme ones in the next few years, say climate scientists who have studied the event. "People say, oh this is historic and this will make history," says Friederike Otto of Oxford University in the UK. "It will probably not make history because we should expect that these records will be broken in the next few years."