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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Der Feedback Loop: Kernelement eines ertragreichen Datenproduktes

In meinem Artikel über Erfolgsfaktoren habe ich 5 entscheidende Elemente bei der Umsetzung von Datenprodukten herausgearbeitet. Das wichtigste Element ist der Feedback Loop. Dabei geht es darum, die Interaktion des Nutzers mit dem Dienst zu nutzen, um den Dienst selbst zu verbessern oder Input für neue Angebote zu schaffen. Warum das sinnvoll ist, möchte ich gerne anhand von etwas mehr Details verraten. Weiterlesen

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