The Proof-of-Work algorithm that underlies Bitcoin, Ethereum w , 1 and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This article describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this article, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum w , 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10th to 20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms. 2

Gas sensors have the potential to assist cooking by providing feedback on the cooking process and by further automating cooking. In this work, we explored the potential use of gas sensors to monitor food during the cooking process. Focusing on dry cooking, we collected gas emissions using 13 sensors during trials in which food was cooked to various degrees of doneness. Using decision tree classifiers, we were able to predict doneness for waffles and popcorn with 73% and 85% accuracy, respectively. We reflect on the potential reasons for this variation and the ways in which gas sensors might reliably be used in ubicomp applications to support cooking.

What happens if efforts to achieve sustainability fail? Research in many fields argues that contemporary global industrial civilization will not persist indefinitely in its current form, and may, like many past human societies, eventually collapse. Arguments in environmental studies, anthropology, and other fields indicate that this transformation could begin within the next half-century. While imminent collapse is far from certain, it is prudent to consider now how to develop sociotechnical systems for use in these scenarios. We introduce the notion of collapse informatics—the study, design, and development of sociotechnical systems in the abundant present for use in a future of scarcity. We sketch the design space of collapse informatics and a variety of example projects. We ask how notions of practice—theorized as collective activity in the “here and now”—can shift to the future since collapse has yet to occur.

Research in many fields argues that contemporary global industrial civilization will not persist indefinitely in its current form, and may, like many past human societies, eventually collapse. Arguments in environmental studies, anthropology, and other fields indicate that this transformation could begin within the next half-century. While imminent collapse is far from certain, it is prudent to consider now how to develop sociotechnical systems for use in these scenarios. We introduce the notion of collapse informatics---the study, design, and development of sociotechnical systems in the abundant present for use in a future of scarcity---as a complement to ICT4D and mitigation-oriented sustainable HCI. We draw on a variety of literatures to offer a set of relevant concepts and articulate the relationships among them to orient and evaluate collapse informatics work. Observing that collapse informatics poses a unique class of cross-cultural design problems, we sketch the design space of collapse informatics and provide a variety of example projects. We explore points of connection and distinction between collapse informatics and sustainable HCI, ICT4D, and crisis informatics. Finally, we discuss next steps and comment on the potential value of collapse informatics work even in the event that collapse never occurs.

In this paper we demonstrate a Markov model based technique for recognizing gestures from accelerometers that explicitly represents duration. We do this by embedding an Erlang-Cox state transition model, which has been shown to accurately represent the first three moments of a general distribution, within a Dynamic Bayesian Network (DBN). The transition probabilities in the DBN can be learned via Expectation-Maximization or by using closed-form solutions. We test this modeling technique on 10 hours of data collected from accelerometers worn by babies pre-categorized as high-risk in the Newborn Intensive Care Unit (NICU) at UCI. We show that by treating instantaneous machine learning classification values as observations and explicitly modeling duration, we improve the recognition of Cramped Synchronized General Movements, a motion highly correlated with an eventual diagnosis of Cerebral Palsy.

Interventions to support children with cognitive and social developmental disabilities often include visual elements. Use of visual artifacts has been shown to increase the communication and understanding levels of children with disabilities. We describe a research agenda for expanding these capabilities using interactive, collaborative and intelligent systems.

In this paper we report on the results of a large scale user survey investigating the status setting and interruption management behavior of mobile instant messaging (IM) users with existing systems. The motivation for this study was to inform the design of interface tools that support users by setting contextually appropriate awareness messages. Our results demonstrate that many desktop IM practices have been appropriated by mobile laptop users, but in the face of increasingly situated computer usage and an "always online" culture, several frictions are emerging between desktop and mobile practices. We find that common assumptions about IM users and the established awareness cues are failing and users are frequently embarrassed and interrupted with negative and sometimes threatening consequences.

The ability to determine what day-to-day activity (such as cooking pasta, taking a pill, or watching a video) a person is performing is of interest in many application domains. A system that can do this requires models of the activities of interest, but model construction does not scale well: humans must specify low-level details, such as segmentation and feature selection of sensor data, and high-level structure, such as spatio-temporal relations between states of the model, for each and every activity. As a result, previous practical activity recognition systems have been content to model a tiny fraction of the thousands of human activities that are potentially useful to detect. In this paper, we present an approach to sensing and modeling activities that scales to a much larger class of activities than before. We show how a new class of sensors, based on Radio Frequency Identification (RFID) tags, can directly yield semantic terms that describe the state of the physical world. These sensors allow us to formulate activity models by translating labeled activities, such as 'cooking pasta', into probabilistic collections of object terms, such as 'pot'. Given this view of activity models as text translations, we show how to mine definitions of activities in an unsupervised manner from the web. We have used our technique to mine definitions for over 20,000 activities. We experimentally validate our approach using data gathered from actual human activity as well as simulated data.