Blockchain technologies, such as smart contracts, present a unique interface for machine-to-machine communication that provides a secure, append-only record that can be shared without trust and without a central administrator. We study the possibilities and limitations of using smart contracts for machine-to-machine communication by designing, implementing, and evaluating AGasP, an application for automated gasoline purchases. We find that using smart contracts allows us to directly address the challenges of transparency, longevity, and trust in IoT applications. However, real-world applications using smart contracts must address their important trade-offs, such as performance, privacy, and the challenge of ensuring they are written correctly.
We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce , a machine-learning-based KBC system for richly formatted data. presents a new data model that accounts for three challenging characteristics of richly formatted data: (1) prevalent document-level relations, (2) multimodality, and (3) data variety. uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. Finally, provides a new programming model that enables users to convert domain expertise, based on multiple modalities of information, to meaningful signals of supervision for training a KBC system. -based KBC systems are in production for a range of use cases, including at a major online retailer. We compare against state-of-the-art KBC approaches in four different domains. We show that achieves an average improvement of 41 F1 points on the quality of the output knowledge base—and in some cases produces up to 1.87× the number of correct entries—compared to expert-curated public knowledge bases. We also conduct a user study to assess the usability of ’s new programming model. We show that after using for only 30 minutes, non-domain experts are able to design KBC systems that achieve on average 23 F1 points higher quality than traditional machine-learning-based KBC approaches.
Hardware component databases are vital resources in designing embedded systems. Since creating these databases requires hundreds of thousands of hours of manual data entry, they are proprietary, limited in the data they provide, and have random data entry errors. We present a machine learning based approach for creating hardware component databases directly from datasheets. Extracting data directly from datasheets is challenging because: (1) the data is relational in nature and relies on non-local context, (2) the documents are filled with technical jargon, and (3) the datasheets are PDFs, a format that decouples visual locality from locality in the document. Addressing this complexity has traditionally relied on human input, making it costly to scale. Our approach uses a rich data model, weak supervision, data augmentation, and multi-task learning to create these knowledge bases in a matter of days. We evaluate the approach on datasheets of three types of components and achieve an average quality of 77 F1 points—quality comparable to existing human-curated knowledge bases. We perform application studies that demonstrate the extraction of multiple data modalities including numerical properties and images. We show how different sources of supervision such as heuristics and human labels have distinct advantages that can be utilized together to improve knowledge base quality. Finally, we present a case study to show how this approach changes the way practitioners create hardware component knowledge bases.
Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In this paper, we show that reducing the motion-to-photon latency of a system itself is a key method for improving the compression ratio of gaze-contingent compression. Our key finding is that a client and streaming server system with sub-15ms latency can achieve 5x better compression than traditional techniques while also using simpler software algorithms than previous work.
A fundamental change in the planning and delivery of new housing projects has taken place in the last years, with the focus shifting towards adding value to projects based on a better understanding of housing preferences. This issue becomes even more critical when it is intended to the provision of affordable houses for low and middle income groups. This paper describes a model designed to help developers and housing users to achieve their expectations regarding quality, affordability and including also reasonable profits. Developed through a “methodological pluralism”, this study identifies people-oriented variables and assumptions. The model was developed based on a case study in the city of Guayaquil-Ecuador, and information obtained from field work research was used to test it. The study examines implications and limitations of the model for inclusion of housing preferences considering local conditions and cultural values. The different parts of the model along with data requirements for each part are described. The paper concludes with findings regarding the identification of most preferred attributes by housing users and the use of alternatives methods to incorporate additional value into projects, translated into more appealing profits for developers and the provision of better and more affordable houses for users.
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