The public's trust of governmental budgeting, fiscal management, and reporting is at an all-time low, especially in the aftermath of the 2008 financial crisis, where only four out of ten people in OECD countries expressed confidence in their government. Cases of fraud, bid-rigging, and pay-to-play are never far from the headlines, and have continued to undermine trust in the public servants and elected officials tasked to oversee the complex work of managing government finances. A large portion of this mistrust can be attributed to the struggle that government finance managers and auditors are facing in analyzing the increasing amount of financial data. Current financial control and audit techniques, including legislated audit requirements, are not able to scale to keep pace with the massive data explosion coming from their own accounting, payroll, and expense management systems. One government response to this issue, open data, enables a sense of fiscal transparency with the public but it doesn't replace the rigorous professional analysis required to identify fraud, errors, and omissions in large amounts of data.

Cows have their own language and talk to each other about food and the weather, according to a new study by scientists in Australia. They created a software programme dubbed'Google Translate for cows' to get a better idea of what the heifers were saying when they go'moo'. The study, by a PhD candidate from the University of Sydney, discovered that dairy cows also respond to positive and negative emotional situations. Cows each have their own individual voice and linked their moods to their'moos', said lead author Alexandra Green. Biologists made the discovery by listening to Holstein-Fresian heifer cattle, a European breed, mooing into a microphone and analysing the pitch.

Since data collection and analysis are difficult, time consuming and costly, we are always looking for a way to optimum use of data to achieve the correct decision that can be referred to diagnose and experiment of diseases in healthcare organizations [3]. In addition, common method such as angiography [5,6] in experimenting and diagnosing diseases is costly and have adverse effects for patients as healthcare resear chers are trying to utilize methods that avoid the high cost as well as the adverse effects of previous methods, which can be performed by using computer - aided disease diagnose methods means machine learning. Whereas, da ta mining process by utilizing machine learning science and database management knowledge [1] has become a robust tool for data analysis and management of health industry data which ultimately leads to knowledge extraction. It should be noted that, with the progress of technology in t he healthcare especially, healthcare industry 4.0, human lifetime has become progressive and more comfortable [ 7 ] . In this new generation, with the development of new medical devices, equipment and tools, new knowledge can be gained in the field of disease diagnosis.

Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning. Such methods are appealing for capability on large-scale sparse datasets with high computational efficiency. In this paper, we present a new framework for adapting Adam-type methods, namely AdamT. Instead of applying a simple exponential weighted average, AdamT also includes the trend information when updating the parameters with the adaptive step size and gradients. The additional terms promise an efficient movement on the complex cost surface, and thus the loss would converge more rapidly. We show empirically the importance of adding the trend component, where AdamT outperforms the vanilla Adam method constantly with state-of-the-art models on several classical real-world datasets.

Algorithmic fairness involves expressing notions such as equity, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise. Most work in the literature to date has focused on classification problems where the prediction is categorical, such as accepting or rejecting a loan application. This is in part because classification fairness measures are easily computed by comparing the rates of outcomes, leading to behaviours such as ensuring that the same fraction of eligible men are selected as eligible women. But such measures are computationally difficult to generalise to the continuous regression setting for problems such as pricing, or allocating payments. The difficulty arises from estimating conditional densities (such as the probability density that a system will over-charge by a certain amount). For the regression setting we introduce tractable approximations of the independence, separation and sufficiency criteria by observing that they factorise as ratios of different conditional probabilities of the protected attributes. We introduce and train machine learning classifiers, distinct from the predictor, as a mechanism to estimate these probabilities from the data. This naturally leads to model agnostic, tractable approximations of the criteria, which we explore experimentally.

Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy. The first experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.

Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different. But which tools you should choose to explore and visualize text data efficiently? In this article, we will discuss and implement nearly all the major techniques that you can use to understand your text data and give you a complete(ish) tour into Python tools that get the job done. In this article, we will use a million news headlines dataset from Kaggle. Now, we can take a look at the data. The dataset contains only two columns, the published date, and the news heading. For simplicity, I will be exploring the first 10000 rows from this dataset.

We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn. We propose a counter-intuitive solution that we call Mutual Information Minimising Exploration (MIME) where an agent learns a latent representation of the environment without trying to predict the future states. We show that our agent performs significantly better over sharp transition boundaries while matching the performance of surprisal driven agents elsewhere. In particular, we show state-of-the-art performance on difficult learning games such as Gravitar, Montezuma's Revenge and Doom.

Although exploring alternatives is fundamental to creating better interface designs, current processes for creating alternatives are generally manual, limiting the alternatives a designer can explore. We present Scout, a system that helps designers rapidly explore alternatives through mixed-initiative interaction with high-level constraints and design feedback. Prior constraint-based layout systems use low-level spatial constraints and generally produce a single design. Tosupport designer exploration of alternatives, Scout introduces high-level constraints based on design concepts (e.g.,~semantic structure, emphasis, order) and formalizes them into low-level spatial constraints that a solver uses to generate potential layouts. In an evaluation with 18 interface designers, we found that Scout: (1) helps designers create more spatially diverse layouts with similar quality to those created with a baseline tool and (2) can help designers avoid a linear design process and quickly ideate layouts they do not believe they would have thought of on their own.

"In the beginning of 2018, we did a survey with 1,500 companies across 15 countries. We wanted to understand the impact of digital transformation. It was interesting to see that in 2017, digital products and services made up only 6% of the economy across the Asia Pacific region. The research showed this is expected to increase to 60% by 2021," said Andrea Della Mattea – President, Microsoft APAC. As we enter a new decade with the 2020's, the world doesn't resemble what it was in 2010. Few quick taps on your phone and you'll get your lunch.