3,123 research outputs found

    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach

    Full text link
    Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, Gene Expression Programming is adapted for network's cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82% in CIFAR-10 dataset (which the best model achieved an error of 2.67%) and 18.83% for CIFAR-100 (best model with 18.16%). For ImageNet in the mobile setting, our best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.Comment: Accepted for presentation at the IEEE Congress on Evolutionary Computation (IEEE CEC) 202

    DSpot: Test Amplification for Automatic Assessment of Computational Diversity

    Full text link
    Context: Computational diversity, i.e., the presence of a set of programs that all perform compatible services but that exhibit behavioral differences under certain conditions, is essential for fault tolerance and security. Objective: We aim at proposing an approach for automatically assessing the presence of computational diversity. In this work, computationally diverse variants are defined as (i) sharing the same API, (ii) behaving the same according to an input-output based specification (a test-suite) and (iii) exhibiting observable differences when they run outside the specified input space. Method: Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We quantify computational diversity as the dissimilarity between observations on inputs that are outside the specified domain. Results: We run our experiments on 472 variants of 7 classes from open-source, large and thoroughly tested Java classes. Our test amplification multiplies by ten the number of input points in the test suite and is effective at detecting software diversity. Conclusion: The key insights of this study are: the systematic exploration of the observable output space of a class provides new insights about its degree of encapsulation; the behavioral diversity that we observe originates from areas of the code that are characterized by their flexibility (caching, checking, formatting, etc.).Comment: 12 page

    Textual Membership Queries

    Full text link
    Human labeling of data can be very time-consuming and expensive, yet, in many cases it is critical for the success of the learning process. In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data. It uses a small amount of labeled data as the core set for the synthesis of useful membership queries (MQs) - unlabeled instances generated by an algorithm for human labeling. Our solution uses modification operators, functions that modify instances to some extent. We apply the operators on a small set of instances (core set), creating a set of new membership queries. Using this framework, we look at the instance space as a search space and apply search algorithms in order to generate new examples highly relevant to the learner. We implement this framework in the textual domain and test it on several text classification tasks and show improved classifier performance as more MQs are labeled and incorporated into the training set. To the best of our knowledge, this is the first work on membership queries in the textual domain.Comment: Accepted to IJCAI 2020. Code is available at github.com/jonzarecki/textual-mqs . Additional material is available at tinyurl.com/sup-textualmqs . SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

    Aeronautical Engineering: A special bibliography, supplement 60

    Get PDF
    This bibliography lists 284 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975

    Design opportunities for wearable devices in learning to climb

    Get PDF
    In this paper, we present a field study on the learning of climbing aimed at defining the design space of wearable devices to support beginners. Three main findings have emerged from our study. First, climbing has a strong emotional impact on beginners; therefore, learning to climb requires mastering new motor patterns as well as negative emotions, such as stress and fear. Second, the feeling of danger that climbers often experience can be mitigated by trust in the climbing partner and the perception of her active presence. Finally, a big problem in climbing is the communication difficulty between the climbing partners and between climber and instructor. We conclude the paper presenting four design considerations for the design of wearable devices meant to support the learning of climbing by providing the actors involved with augmented communication. Such augmented communication should address both the physical and the emotional difficulties of this sport

    Large-scale automatic species identification

    Get PDF
    The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control

    Manx: Close air support aircraft preliminary design

    Get PDF
    The Manx is a twin engine, twin tailed, single seat close air support design proposal for the 1991 Team Student Design Competition. It blends advanced technologies into a lightweight, high performance design with the following features: High sensitivity (rugged, easily maintained, with night/adverse weather capability); Highly maneuverable (negative static margin, forward swept wing, canard, and advanced avionics result in enhanced aircraft agility); and Highly versatile (design flexibility allows the Manx to contribute to a truly integrated ground team capable of rapid deployment from forward sites)
    corecore