Nonlinearity, Multilinearity, Simultaneity

This paper addresses three paradigms in epistemological structures that could serve as preliminary classifications enabling a systematic approach to past and current media phenomena such as hypertext, diagrams and ubiquitous computing. Nonlinearity is discussed by Vilém Flusser in the context of "technical images." In his own approach to go beyond linear text, Flusser and his publisher created a digital version of his book Die Schrift on a floppy disk (1987), enabling the reader to jump between chapters or to rewrite the text. Multilinearity is a concept that is revived within the diagrammatology discourse, transcending linearity through topographical ways of reading. Current examples can be found in arts and narratives such as Chris Ware's comics, who uses diagrammatics to blur the lines between the reader and the author. Simultaneity as a technological attribute is essential to current ubiquitous and pervasive technologies and services and draws heavily on Heideggerian concepts such as readiness-to-hand and background. In this epistemological shift, the information is instantaneously organized according to the user's needs. Each of these epistemological structures offers a different idea about receiving and creating knowledge, information and communication, paving the way for narrative and media strategies that are more and more determined by a 'reader' becoming a 'user' and a 'text' becoming a 'service.' Image Credit: Chris Ware’s Diagram on the interior of the dust-jacket from Jimmy Corrigan, The Smartest Kid on Earth.

Electronic Solutions for Artificial Intelligence Healthcare

At present, diverse, innovative technology is used in electronics and ubiquitous computing environments [...]

Into the archive of ubiquitous computing: the data perfect tense and the historicization of the present

PurposeThis paper theorizes ubiquitous computing as a novel configuration of the archive. Such a configuration is characterized by shifts in agency underlying archival mechanics and a pronounced rhythmic diminution of such mechanics in which the user's experiential present tense is rendered fundamentally historical. In doing so, this paper troubles the relationship between: archival mechanics such as appraisal, accession and access; the archive as a site of historical knowledge production and the pervasiveness of data-driven daily life.Design/methodology/approachBy employing conceptual analysis, I analyze a classic vision of ubiquitous computing to describe the historicization of the present tense in an increasingly computerized world. The conceptual analysis employed here draws on an interdisciplinary set of literature from library and information science, philosophy and computing fields such as human-computer interaction (HCI) and ubiquitous computing.FindingsI present the concept of the data perfect tense, which is derived from the future perfect tense: the “will have had” construction. It refers to a historicized, data-driven and fundamentally archival present tense characterizing the user's lived world in which the goal of action is to have had created data for future unspecified use. The data perfect reifies ubiquitous computing as an archive, or a site of historical knowledge production predicated on sets of potential statements derived from data generated, appraised, acquisitioned and made accessible through and by means of pervasive “smart” objects.Originality/valueThis paper provides foundational consideration of ubiquitous computing as a configuration of the archive through the analysis of its temporalities: a rhythmic diminution that renders users' experiential present tenses as fundamentally historical, constructed through the agency of smart devices. In doing so, it: contributes to ongoing work within HCI seeking to understand the relationship between HCI and history; introduces concepts relevant to the analysis of novel technological ecologies in terms of archival theory; and constitutes preliminary interdisciplinary steps towards highlighting the relevance of theories of the archive and archival mechanics for critiquing sociotechnical concerns such as surveillance capitalism.

Owlet

Miniaturization is an inherent trend in ubiquitous computing. Insect-scale robots add new capabilities to disaster management, tiny headphones are emerging as complete computing devices, and small wearable health monitors can track vital signs around the clock. Advancements in sensing technologies play a pivotal role in this development. Spatial sensing at this form factor, however, is a skill yet to be mastered, particularly at low-frequency signals like audible sounds. Traditionally, spatial sensing requires sampling in both time and space using an array of microphones, which comes with a strict size requirement and multiplies its power requirement. In this article, we explore the possibility of an alternative design for spatial sensing for miniaturized and power-constrained devices.

A City-Wide Crowdsourcing Delivery System with Reinforcement Learning

The revolution of online shopping in recent years demands corresponding evolution in delivery services in urban areas. To cater to this trend, delivery by the crowd has become an alternative to the traditional delivery services thanks to the advances in ubiquitous computing. Notably, some studies use public transportation for crowdsourcing delivery, given its low-cost delivery network with millions of passengers as potential couriers. However, multiple practical impact factors are not considered in existing public-transport-based crowdsourcing delivery studies due to a lack of data and limited ubiquitous computing infrastructures in the past. In this work, we design a crowdsourcing delivery system based on public transport, considering the practical factors of time constraints, multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement learning model to learn the optimal order dispatching strategies from massive passenger data and package data. The order dispatching problem is formulated as a sequential decision making problem for the packages routing, i.e., select the next station for the package. A delivery time estimation module is designed to accelerate the training process and provide statistical delivery time guarantee. Three months of real-world public transportation data and one month of package delivery data from an on-demand delivery platform in Shenzhen are used in the evaluation. Compared with existing crowdsourcing delivery algorithms and widely used baselines, we achieve a 40% increase in profit rates and a 29% increase in delivery rates. Comparison with other reinforcement learning algorithms shows that we can improve the profit rate and the delivery rate by 9% and 8% by using time estimation in action filtering. We share the data used in the project to the community for other researchers to validate our results and conduct further research.1 [1].