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Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced...
K-Nearest Neighbor (KNN) is a commonly used fault diagnosis method, which is based on Euclidean distance between samples to carry out fault diagnosis. The differences between the variables have a direct effect on the Euclidean distance, which affects the KNN fault diagnosis effect. After the dimensional normalization, there are also some problems such as the decrease of variable diversity, and the...
We propose the Anchored Regression Network (ARN), a nonlinear regression network which can be seamlessly integrated into various networks or can be used stand-alone when the features have already been fixed. Our ARN is a smoothed relaxation of a piecewise linear regressor through the combination of multiple linear regressors over soft assignments to anchor points. When the anchor points are fixed...
Indonesia is a big country which has many isolated power system networks. Sumatera power system is one of the main power system networks in Indonesia that supplies electricity for more than 45 million inhabitants in Sumatera Island. For the transmission topology and load characteristic, there are some dynamic stability problems in Sumatera power system such as voltage and small signal stability. To...
We propose a method for generating caustic images in real time using a deep/convolutional neural network (CNN). To do so, training images are first rendered using photon mapping, and the CNN learns the correspondences between the depth images and caustic images. After learning, the CNN generates a caustic image from a depth image within 55 milliseconds. In addition, the similarity between the generated...
Event logging is a key source of information on a system state. Reading logs provides insights on its activity, assess its correct state and allows to diagnose problems. However, reading does not scale: with the number of machines increasingly rising, and the complexification of systems, the task of auditing systems' health based on logfiles is becoming overwhelming for system administrators. This...
This lightning talk paper discusses an initial data set that has been gathered to understand the use of software in research, and is intended to spark wider interest in gathering more data. The initial data analyzes three months of articles in the journal Nature for software mentions. The wider activity that we seek is a community effort to analyze a wider set of articles, including both a longer...
This paper reports on three measurement science field exercises for evaluating ground, aerial, and aquatic robots. These events, conducted from February to June 2017, were conducted in close co-ordination with the responder community, standards organizations, manufacturers, and academia. Test data from a wide variety of robot platforms were gathered in a wide variety of standard and prototypical test...
Active shape model is widely used for facial feature localization. Regarding the traditional ASM algorithm can't describe the object shape precisely, an improved ASM algorithm is proposed. At first, we establish shape model and use PCA (Principle Component Analysis) to transform high-dimensional data to lower dimensions. Another work is to establish local texture model giving sample points with different...
We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial...
Video scene parsing is challenging due to the following two reasons: firstly, it is non-trivial to learn meaningful video representations for producing the temporally consistent labeling map; secondly, such a learning process becomes more difficult with insufficient labeled video training data. In this work, we propose a unified framework to address the above two problems, which is to our knowledge...
Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes...
Subspace learning is one of the most foundational tasks in computer vision with applications ranging from dimensionality reduction to data denoising. As geometric objects, subspaces have also been successfully used for efficiently representing certain types of invariant data. However, methods for subspace learning from subspace-valued data have been notably absent due to incompatibilities with standard...
Automatic grading systems, such as WebWork, are becoming much more widely used as they relieve the instructor from needing to grade student work, provide students with automatic feedback, and can allow for immediate resubmission. They have also been shown to improve the effectiveness of teaching and learning. In this paper, we apply Item Response Theory (IRT) to a large WebWork Calculus homework dataset...
Dropout is a technique widely used for preventing overfitting while training deep neural networks. However, applying dropout to a neural network typically increases the training time. This paper proposes a different dropout approach called controlled dropout that improves training speed by dropping units in a column-wise or row-wise manner on the matrices. In controlled dropout, a network is trained...
A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Oversampling Technique (SMOTE) for handling the imbalanced classification problem. The proposed technique uses an optimised membership function to enhance the classification performance and it is compared with three different classifiers. The experiments consisted of four...
This study is focused on the application of the forecast results obtained with the help of a dual-parametric neural network for measuring instruments calibration within a limited range. In this report describing our study we intend to give a theoretical overview of the previous research on the subject. We also consider the learning algorithms for interval neural networks with hidden layer weights...
Online education constitutes an important component in the continuous growth / development of higher education and adult education. Its quality largely depends on the design rules and the evaluation standards of the programs as a whole as well as the study courses as component parts. The need to ensure the quality of online courses is a current issue. The paper proposes a system of standards, criteria...
In this paper, the problem of adaptive beamforming in the presence of direction-of-arrival (DOA) mismatch is investigated. To develop a robust beamformer against such an imperfection, a new approach is devised by formulating an output signal-to-interference-plus-noise ratio (SINR) maximization problem. In particular, the proposed robust beamforming approach consists of two steps. At first, the standard...
A method for scene text localization and recognition is proposed. The novelties include: training of both text detection and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the text and adapts its resolution to the data.,,The proposed method achieves state-of-the-art accuracy in the end-to-end text recognition...
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