International Data Corporation expects the installed base of the Internet of Things will be approximately 212 billion “things” globally by the end of 2020. This is expected to include 30.1 billion installed “connected (autonomous)” things. Today, these devices are connecting to each other using multiple, and often incompatible approaches. The members of the Open Interconnect Consortium believe that in order to achieve this scale, the industry will need both the collaboration of the open source community and industry standards to drive interoperability of these devices. The Open Interconnect Consortium (OIC) will seek to define a common communication framework based on industry standard technologies to wirelessly connect and intelligently manage the flow of information among devices, regardless of form factor, operating system or service provider. OIC is the sponsor for the IoTivity Project, an open source software framework enabling seamless device-to-device connectivity to address the emerging needs of the Internet of Things.|
With salaries that range anywhere between Rs. 111,389 all the way up to Rs. 722,959 one can’t help but admire the potential of the market for software programmers and further analyze the same. Analysis of...
A few days ago, we announced the release of Spark 1.5. This release contains major under-the-hood changes that improve Spark’s performance, usability, and operational stability. Besides these changes, we have been continuously improving DataFrame API. In this blog post, we’d like to highlight three major improvements to DataFrame API in Spark 1.5, which are:
New built-in functions;Time intervals; andExperimental user-defined aggregation function (UDAF) interface.
Each Unplugged activity is available to download in PDF format, with full instructions and worksheets. Background sections explain the significance of each activity to computer science, and answers are provided for all problems. All you need for most of these activities are curiosity and enthusiasm. There are photos and videos showing some of the activities in action, and we’ve collected links to other useful resources.
We solve this problem using a semi-supervised form of logistic regression. A large portion of the model consists of “bag of words” type features from user submitted reviews on the properties. Since it is a semi-supervised technique, not only do we use the reviews on locations that we have tag votes on during training, we also use a large chunk of unlabeled data. Also, when applying the model to get the end results, we need to read and process all our reviews. On top of that, we have hundreds of different tags.
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