Persistent Contextual Values as Inter-Process Layers

Mobile applications today often fail to be context aware when they also need to be customizable and efficient at run-time. Context-oriented programming allows programmers to develop applications that are more context aware. Its central construct, the so-called layer, however, is not customizable. We propose to use novel persistent contextual values for mobile development. Persistent contextual values automatically adapt their value to the context. Furthermore they provide access without overhead. Key-value configuration files contain the specification of contextual values and the persisted contextual values themselves. By modifying the configuration files, the contextual values can easily be customized for every context. From the specification, we generate code to simplify development. Our implementation, called Elektra, permits development in several languages including C++ and Java. In a benchmark we compare layer activations between threads and between applications. In a case study involving a web-server on a mobile embedded device the performance overhead is minimal, even with many context switches.Comment: 8 pages Mobile! 16, October 31, 2016, Amsterdam, Netherland

Reflections on preserving the state of new media art

As part of its work to explore emerging issues associated with characterisation of digital materials, Planets has explored vocabularies and information structures for expressing the properties integral to the value of digital art. Value encompasses those qualities that must be understood and captured in order to ensure that art works’ sensory, emotional, mental and spiritual resonance remain. Facets of interactivity, modularity and temporality associated with digital art present some critical questions that the preservation community must increasingly be equipped to answer. Because digital art materials exhibit fundamental multidimensionality, validating the successful preservation of creative experience demands the explication of more than just file characteristics. Understanding relationships between objects also implies an understanding of their respective functional qualities. This paper presents a Planets’ vocabulary for encapsulating contextual and implicit characteristics of digital art, optimised for preservation planning and validation

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

A call for resilience index for health and social systems in Africa

This repository item contains a single issue of Issues in Brief, a series of policy briefs that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications and symposia exploring African futures in various aspects related to development on continental and regional scales. The views expressed in this paper are strictly those of the author and should not be assumed to represent the views of the Frederick S. Pardee Center for the Study of the Longer-Range Future or of Boston University.This policy brief explores the concept of resilience as it applies to health and social systems in Africa, and suggests that development of a multi-dimensional resilience index may help to understand and formulate policy in settings of complex emergencies. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications and symposia exploring African futures in various aspects related to development on continental and regional scales