The lifecycle of geotagged data

The world is a big place. At any given instant something is happening somewhere, but even when nothing in particular is going on people still find ways to generate data, such as posting on s

The force within: Recommendations via gravitational attraction between items

Recommendation systems rely on various definitions of similarities. These definitions while having numerous design factors in different domains help identify and recommend relevant content. For example, similarity between users, or items, are measured based on, but not limited to, explicit feedback such as ratings, thumbs up; or/and implicit feedback such as clicks, views etc; or/and based on composition of item such as tags, metadata etc. In this paper, we explore a similarity model while very intuitive to find similar items using a very common natural law of attraction between bodies, that is gravitational law. We show how the two attributes, relative mass and distance between the bodies, of gravitation law can be interpreted for an effective personalized recommendations; in both spatial and non-spatial domains. Finally, we illustrate the use of distance and mass in a non-spatial domain and we exhibit the accuracy in recommendations against popular baselines

Knowledge engineering with image data in real-world settings

We report on experiences in adding ML-trained visual recognition modules to a human-oriented image semantic annotation tool which creates RDF descriptions of images and scene contents. We conclude that ML cannot replace expert humans but can aid them in various ways, some unexpected. Semantic markup systems can be to designed to align human and machine blind spots. Finally, we briefly outline directions for future work

Designing the club of the future with data: A case study on collaboration of creative industries

This paper reflects on the development of a multi-sensory clubbing experience which was deployed during a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, the collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders with different backgrounds

AI at the disco: Low sample frequency human activity recognition for night club experiences

Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading - -embedding accelerometer data in the Bluetooth Low Energy broadcast packet - -which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club

Social VR: A new medium for remote communication and collaboration

We are facing increasingly pressure on reducing travel and working remotely. Tools that support effective remote communication and collaboration are much needed. Social Virtual Reality (VR) is an emerging medium, which invites multiple users to join a collaborative virtual environment (VE) and has the potential to support remote communication in a natural and immersive way. We successfully organized a CHI 2020 Social VR workshop virtually on Mozilla Hubs, which invited researchers and practitioners to have a fruitful discussion over user representations and ethics, evaluation methods, and interaction techniques for social VR as an emerging immersive remote communication tool. In this CHI 2021 virtual workshop, we would like to organize it again on Mozilla Hubs, continuing the discussion about proxemics, social cues and VE designs, which were identified as important aspects for social VR communication in our CHI 2020 workshop

The club of the future: Participatory clubbing experiences

Copyrigh

Social VR: A new medium for remote communication and collaboration

There is a growing need for effective remote communication, which has many positive societal impacts, such as reducing environmental pollution and travel costs, supporting rich collaboration by remotely connecting talented people. Social Virtual Reality (VR) invites multiple users to join a collaborative virtual environment, which creates new opportunities for remote communication. The goal of social VR is not to completely replicate reality, but to facilitate and extend the existing communication channels of the physical world. Apart from the benefits provided by social VR, privacy concerns and ethical risks are raised when the boundary between the real and the virtual world is blurred. This workshop is intended to spur discussions regarding technology, evaluation protocols, application areas, research ethics and legal regulations for social VR as an emerging immersive remote communication tool