The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data

We present 2DToonShade: a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and recent state of the art.
Keywords: Toon shading | Cel shading | Hand-drawn animation | Image-based rendering | Non-photorealistic-rendering

Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.
Keywords: Artificial intelligence | machine learning | classification | social media| Facebook | discrimination | bias

Data activism, promoting new forms of civic and political engagement, has emerged as a response to problematic aspects of datafication that include tensions between data openness and data ownership, and asymmetries in terms of data usage and distribution. In this article, we discuss MyData, a data activism initiative originating in Finland, which aims to shape a more sustainable citizen-centric data economy by means of increasing individuals’ control of their personal data. Using data gathered during long-term participant-observation in collaborative projects with data activists, we explore the internal tensions of data activism by first outlining two different social imaginaries – technological and socio-critical – within MyData, and then merging them to open practical and analytical space for engaging with the socio-technical futures currently in the making. While the technological imaginary favours data infrastructures as corrective measures, the socio-critical imaginary questions the effectiveness of technological correction. Unpacking them clarifies the kinds of political and social alternatives that different social imaginaries ascribe to the notions underlying data activism, and highlights the need to consider the social structures in play. The more far-reaching goal of our exercise is to provide practical and analytical resources for critical engagement in the context of data activism. By merging technological and socio-critical imaginaries in the work of reimagining governing structures and knowledge practices alongside infrastructural arrangements, scholars can depart from the most obvious forms of critique, influence data activism practice, and formulate data ethics and data futures.
Keywords: Datafication | social imaginary | data activism | MyData | data ethics | socio-technical futures

Medical research data is sensitive personal data that needs to be protected from unauthorized access and unintentional disclosure. In a research setting, sharing of (big) data within the scientific community is necessary in order to make progress and maximize scientific benefits derived from valuable and costly data. At the same time, convincingly protecting the privacy of people (patients) participating in medical research is a prerequisite for maintaining trust and willingness to share. In this commentary, we will address this issue and the pitfalls involved in the context of the PEP project1 that provides the infrastructure for the Personalized Parkinson’s Project,2 a large cohort study on Parkinson’s disease from Radboud University Medical Center (Radboudumc), in cooperation with Verily life Sciences, an Alphabet subsidiary.
Keywords:Big Data | GDPR compliance | informed consent | medical cohort study | polymorphic encryption | privacy by design