Filter (software)

Abstract: Online web services such as Google and Facebook started using personalization algorithms. Because information is customized per user by the algorithms of these services, two users who use the same search query or have the same friend list may get different results. Online services argue that by using personalization algorithms, they may show the most relevant information for each user, hence increasing user satisfaction. However, critics argue that the opaque filters used by online services will only show agreeable political viewpoints to the users and the users never get challenged by opposing perspectives. Considering users are already biased in seeking like-minded perspectives, viewpoint diversity will diminish and the users may get trapped in a “filter bubble”. This is an undesired behavior for almost all democracy models. In this thesis we first analyzed the filter bubble phenomenon conceptually, by identifying internal processes and factors in online web services that might cause filter bubbles. Later, we analyzed this issue empirically. We first studied existing metrics in viewpoint diversity research of the computer science literature. We also extended these metrics by adding a new one, namely minority access from media and communication studies. After conducting an empirical study for Dutch and Turkish Twitter users, we showed that minorities cannot reach a large percentage of users in Turkish Twittersphere. We also analyzed software tools and design attempts to combat filter bubbles. We showed that almost all of the tools implement norms required by two popular democracy models. We argue that democracy is essentially a contested concept, and other less popular democracy models should be included in the design of such tools as well.

Abstract: Methods and systems to enable system users to share, discover, stream, purchase, and otherwise support content generated by other users. An interest indication system referred to as GreenLighting may be used to tag content and/or other users of interest. The GreenLight system may then be used to search, filter, sort, and stream content based on preferences and relationships between users. The systems and methods described herein may be embodied as a website that is accessible to both content creators and end users attempting to discover and view content.

Abstract: From the Publisher: This comprehensive book takes you step-by-step through the process of writing shell scripts to solve real-world Unix problems and tasks. Each chapter begins by presenting a typical Unix challenge that you'll most likely experience at your job. With each challenge, you'll learn how to identify a specific goal and start the shell script by defining the correct command syntax to solve the problem. You'll find out how to build the shell script around the commands, filter the commands output to strip out the unneeded data, and add options that give users more flexibility on the command line. The approach used will teach you how to attack problems logically. It will also empower you to take the basic command syntax and turn it into a shell scripting solution. Throughout the book, you'll find end-to-end shell scripts that you can use to automate repetitive tasks and solve real-world system administration problems.

Abstract: An obvious feature of the big data is overload. When we are held up in the Lake of big data, it is necessary to filter the most meaningful information [Khan et al. (ICCWAMTIP 2018:232–236, 2018), (Int J Inf Technol 12(2):409–417, 2020)]. The key element is segmentation, which involves the breakdown of the dataset into smaller ones. MIMIC-III (Medical Information Mart for Intensive Care III), an open critical care database, comprises data flow that encompasses patient information during whole hospital length of stay, i.e., from the beginning of hospital admission to patients’ discharge from the hospital. As MIMIC III stores a large volume of shared data, selecting useful data using traditional data mining approach to tailor academic research would be time consuming and resource demanding. Herein, we introduced a robust Windows build-in tool known as PowerShell, which is used to segment the big data into a practical dataset. Since the PowerShell script is open on the platform to demonstrate its use for further public research, we would present the step-by-step operation here to help readers grab a general idea of its mechanism.