Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.
New technologies enable novel types of learning activities that differ radically from traditional approach of visiting lectures and doing homework assignments. Namely, these technologies support transforming our everyday environments into learning environments. This concept is referred to in the literature as ubiquitous learning. Enriching ubiquitous learning systems with adaptive functionality facilitates personalization of learning activities by adapting them to learners' progress and situation. In this article, we identify needs of four user roles in ubiquitous learning systems, i.e. learner, instructor, developer, and researcher. We analyze the state of the art in ubiquitous learning and find that roles other than learners have not received much attention in the literature. Finally, we propose supporting different needs identified for four user roles by adding meta-level functionality to ubiquitous learning systems. This proposal adds self-introspective capabilities to such systems to serve their users better.
Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.
Multimedia content is commonly manipulated using a keyboard and a mouse. We propose an innovative way of controlling multimedia services using mobile phones equipped with NFC (Near Field Communication) compliant RFID readers. A user activates multimedia content by touching an RFID tag. This action configures the mobile phone as a remote control for the multimedia content. The user sends commands such as play, pause and stop, using the phone's UI. The multimedia content is shown on a wall display. This approach has several benefits: a common and easy-to-use user interface, off-the-self components, and lightweight implementation. We have not created any new technology, but our system makes use of widely used ones. The innovation of our system comes up on how all these technologies are glued together to get a light and flexible system. We present three prototypes: an interactive advertising catalogue, a video player and a slideshow viewer.
Abstract. We contribute in this study a first step in theory-based understanding on how creativity in collaborative design sessions relates to the elements that are present in a creative act. These elements include group composition, objects present, practices used, and previous knowledge of the participants. The context of this study was our search for lightweight methods for technology design with children, which can be used in a school context with large groups, will require as little amount of training as possible, and can be set up quickly. We formed a mixed group, consisting of young children, an older child and an adult, with the aim of involving children in creative collaborative brainstorming during the very early phases of design, so as to come up with fruitful ideas for technology development. We report our process and examine the implications of our results in relation to different elements that trigger and affect creativity in the collaborative design process. Use of Vygotsky's cycle of creativity as our theoretical lens together with timeline analysis method presented in the paper were essential for seeing beneath the surface of what happened in this complex, collaborative creative process. Our results can be used for further methodological development of creative collaborative sessions, both with children and adults.2
Sentiment analysis, also known as opinion mining, plays a big role in both private and public sector Business Intelligence (BI); it attempts to improve public and customer experience. Nevertheless, de-identified sentiment scores from public social media posts can compromise individual privacy due to their vulnerability to record linkage attacks. Established privacy-preserving methods like k-anonymity, l-diversity and tcloseness are offline models exclusively designed for data at rest. Recently, a number of online anonymization algorithms (CASTLE, SKY, SWAF) have been proposed to complement the functional requirements of streaming applications, but without open-source implementation. In this paper, we present a reusable Apache NiFi dataflow that buffers tweets from multiple edge devices and performs anonymized sentiment analysis in real-time, using randomization. The solution can be easily adapted to suit different scenarios, enabling researchers to deploy custom anonymization algorithms.
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