Declarative Thinking and Programming

Before we actually dive into this topic, imagine the following: You just moved to a new place and the time is ripe for a little house-warming dinner with your best friends Alice and Bob. Since Alice is really tech-savvy you just send her a digital invitation with date, time and of course your new address that she can add to her calendar with a single click. With good old Bob is a bit more difficult, he is having a real struggle with modern IT. That’s why you decide to send him an e-mail not only including the time and location but also a suggestion which train to take to your city, details about the trams, stops, the right street and so. Weiterlesen

Anomaly Detection: (Dis-)advantages of k-means clustering

In the previous post we talked about network anomaly detection in general and introduced a clustering approach using the very popular k-means algorithm. In this blog post we will show you some of the advantages and disadvantages of using k-means. Furthermore we will give a general overview about techniques other than clustering which can be used for anomaly detection. Weiterlesen

Migrating an embedded Android setup: Porting the Kernel Driver (Part 2)

After getting the display up and running, we’ll have a look at the kernel drivers. It would be way too much work describing each kernel driver in detail, so I will concentrate on the changes needed to port them to the newer kernel version, 3.14 to be exact. A more thorough introduction to the sensor driver and the whole sensor integration can be found here. Of course I learned a lot since I wrote my previous series of articles so I improved the driver quite a bit. Both devices are connected to the Wandboard via the I2C-bus, so they are working really similar at this level. Just controlling it, reading data and sleep management differs for each device.
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Affective Robots: Emotionally Intelligent Machines

Automatic emotion recognition is an emerging area which leverages and combines knowledge from multiple fields such as machine learning, computer vision and signal processing. It has potential applications in many areas including healthcare, robotic assistance, education, market survey and advertising. Another usage of this information is to improve Human Computer Interaction with what can be described as Affective Computing, an interdisciplinary field that expands into otherwise unrelated fields like psychology and cognitive science. The concept of „affective robots“ refers to leveraging these emotional capabilities in humanoid robots to respond in the most appropriate way based on the user’s current mood and personality traits. In this article, we explore the emotion recognition capabilities of Pepper the robot and how they perform in contrast to other cutting-edge approaches. Weiterlesen

Migrating an embedded Android setup: What could possibly go wrong? (Part 1)

Android updates are rare, especially for development boards. We were running such a deprecated board once built to demonstrate our knowledge in embedded Android. Since we didn’t want to rely on a deprecated showcase, we decided to build a completely new setup bringing together the old show case, an ordinary Android extended with a line LCD display, usable via an SDK-Add-on, and an integrated sensor, previously described here.
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Powering a Data Hub at Otto Group BI with Schedoscope

In order to build data services or advanced machine learning models, organizations must integrate large amounts of information from diverse sources. As a central place to consolidate as many data sources as possible we often find what is fashionably called a data lake. Building a data lake usually starts by collecting as much data in raw form as possible. The idea is to give data scientists simple access to all available data so that they can combine information in ways not yet anticipated. Hadoop is the preferred choice for such a system because it is able to store vast amounts of data in a cost-efficient manner and is largely agnostic to structure. Weiterlesen

Causal Inference and Propensity Score Methods

In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. Rarely do we think about causation and the actual effect of a single feature variable or covariate on the target or response. Some even go so far as to say that „correlation trumps causation“ like in the book „Big Data: A Revolution That Will Transform How We Live, Work, and Think“ by Viktor Mayer-Schönberger and Kenneth Cukier. Following their reasoning, with Big Data there is no need to think about causation anymore, since nonparametric models will do just fine using correlation alone. For many practical use cases, this point of view may seem acceptable — but surely not for all.

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