Good day my name is Victoria and I will your conference operator. At this time I would like welcome everyone to the salesforce.com, All lines have been placed on mute to prevent any background noise. After the speakers' there will be question-and-answer session. If you would to ask a question during this time simply press star then the number one on your telephone keypad. If you would like to withdraw your question press the pound key. I would now like to turn the call over to John Cummings, Vice President of Investor Relations. Our first quarter results press release, SEC filings and the replay of today's call can be found on our IR website at www.Salesforce.com/inverstor. And with me today on the call is Marc Benioff, Chairman and CEO, Keith Block, Vice Chairman President and Mark Hawkins, CFO. As a reminder, our commentary today will primarily be in non-GAAP terms. Reconciliations between our GAAP and non-GAAP results and guidance can be found in our earnings press release. Also some of our comments today may also contain forward-looking statements, which are subject to risks, uncertainties and assumptions.

May 18 (Reuters) - Salesforce.com Inc, a marketing-and-sales software provider for 17 years, may be joining the ranks of the technology old-guard, but the 27 percent first-quarter revenue growth it posted on Wednesday still showed some startup zip. The company also provided a rosy outlook, raising its full-year revenue forecast and predicting that technology like artificial intelligence would drive future growth. Shares of Salesforce, seen as a barometer for the cloud-computing sector, rose 5.9 percent in after-hours trading. Cloud-delivered software, provided online on a subscription basis, has gained popularity with businesses due to its flexibility and cost benefits. Salesforce, founded in 1999, has won market share from more traditional software providers such as Oracle Corp and SAP AG, Chief Executive Marc Benioff reminded analysts on a conference call after the company reported its earnings.

I've written a number of blog posts about regression analysis and I've collected them here to create a regression tutorial. This tutorial covers many aspects of regression analysis including: choosing the type of regression analysis to use, specifying the model, interpreting the results, determining how well the model fits, making predictions, and checking the assumptions. At the end, I include examples of different types of regression analyses. If you're learning regression analysis right now, you might want to bookmark this tutorial! Before we begin the regression analysis tutorial, there are several important questions to answer.

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

According to a new market research report, "Speech Analytics Market by Type (Solutions (Speech Engine, Indexing, Analysis and Query Tools, and Dash Boards and Reporting Tool) and Services), by Deployment Type (On- Remise, Cloud), by Organization Size, by Vertical, & by Region – Global Forecast to 2020?, published by MarketsandMarkets, the market size is estimated to grow from USD 589.2 Million in 2015 to USD 1.60 Billion by 2020, at an estimated Compound Annual Growth Rate (CAGR) of 22.0% from 2015 to 2020. Browse 66 market data Tables and 25 Figures spread through 145 Pages and in-depth TOC on"Speech Analytics Market" Early buyers will receive 10% customization on this report. The speech analytics technology is witnessing the rising demand due to the need to decipher hidden insights from the customer interaction data. Rising number of contact centers, importance of customer feedback, need for Customer Relationship Management (CRM), product development, increasing competition among organizations, and stringent compliance management are some of the factors deriving the demand of speech analytics solutions and services. The speech analytics solutions are expected to dominate the market from 2015 to 2020, with larger market share than the service segment, due to growing trends of analytical insights from customer interaction data.

Using text mining and machine learning based on the SAP HANA platform, the initiative aims to help companies review job descriptions, performance reviews and similar people processes for potential bias and suggest changes to encourage equity. The announcement was made at the 28th annual SAPPHIRE NOW conference. These new capabilities will complement existing SAP SuccessFactors offerings that already help address inequity. Analytics and reports focused on diversity and inclusion are available to help organizations identify and track where biases exist in talent acquisition and management processes -- recruiting, compensation, succession and the like -- coupled with guidance on actions to take to address those biases. SAP is also exploring applications for mentoring programs that will help people from historically disadvantaged groups more effectively navigate and develop their careers, as well as tools for balancing family and work that will integrate elements of benefits, scheduling and management into a single process.

Developing effective tools against cancer has been a long, complicated endeavor with successes and disappointments. Despite all, cancer remains the leading cause of death worldwide. Now, machine learning and data analytics are being recruited as tools in the effort fight the disease and show significant promise according to two recent papers. In one paper – An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer – researchers from MIT and Stanford "propose models that use machine learning and optimization to suggest regimens to be tested in phase II and phase III trials." Their work, published in March in Management Science, could help cut costs and speed clinical trials.

Google is catching up to competitors Facebook, Apple and Amazon in messaging, video calling and home speaker-embedded digital assistants. But it's taking the lead in virtual reality and may have changed mobile phones forever with a new twist on mobile apps that allows them to play without being installed. That's the conclusion of Jan Dawson, an analyst with Jackdaw Research, who was at the Google I/O annual developers conference Wednesday in Mountain View, California. Dawson said Google's new Allo app focuses on the search giant's strengths in search and natural language recognition, but may have come too late behind bigger rivals to gain much use. In a research note he praised Google's new Daydream virtual reality platform, but noted it'll take time to become popular because the high bar for specifications means no devices can support it yet.

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorithm that, given a HIT, decides for any given set of worker answers if the system should stop and output an answer or iterate and ask one more worker. Knowing the historic performance of a worker in the form of a quality score can be beneficial in such a scenario. In this paper we investigate how to devise better stopping rules given such quality scores. We also suggest adaptive exploration as a promising approach for scalable and automatic creation of ground truth. We conduct a data analysis on an industrial crowdsourcing platform, and use the observations from this analysis to design new stopping rules that use the workers' quality scores in a non-trivial manner. We then perform a simulation based on a real-world workload, showing that our algorithm performs better than the more naive approaches.

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.