Reviewerly/exHarmony huggingface dataset:
Learning-Based Calibration Decision System for Bio-Inertial Motion Application

We developed a learning-based calibration algorithm for a vestibular prosthesis with the long-term goal of reproducing error-free vestibular system dynamic responses. Our approach uses an additional IMU to detect the head acceleration of a patient and to correct the corresponding drift in the vestibular prosthesis. The algorithm includes four major parts. First, we extract features from the shoe-mounted IMU to classify human activities through convolutional neural networks. Second, we fuse data from the head-mounted IMU (vestibular prosthesis). Third, we artificially create additional data samples from a small pool of training data for each classification class. Fourth, we use the classified activities to calibrate the reading from the head-mounted IMU. The results indicate that during daily routine activities the firing rate baseline of a vestibular prosthesis system without calibration fluctuates between 100 pulses/s to 150 pulses/s; in contrast, an appropriate calibration to human activity results in correction of 4 pulses/s in extreme cases, providing a stable baseline firing rate while the head is not moving. In this work, we specifically study the contribution of gyroscope scale factor on the drift of the vestibular prosthesis system and propose a corresponding calibration method.
Gender Inference Based on Indonesian Name and Profile Photo

Social sensing is about building reliable system on top of unreliable data by using recent and top-notch technologies. Demographic understanding in a social sensing system requires several tools such as location, age, or gender inference service. This paper presents an alternative method to infer gender of a person based on the name input and/or the profile photo input. The result shows that the formula improves the standalone text classifier and image classifier. The F1 score of the standalone text (name) classifier reaches 83.71% by using the n-gram Logistic Regression model. On the other hand, the F1 score of the standalone image (profile photo with a facial feature) classifier reaches 98.14% by using the Inception convolutional neural network model. If these two classifiers are combined, the F1 score is improved further, reaching 98.62%.
Subgroup analysis with a nonparametric unimodal symmetric error distribution

In clinical trials and moving toward precision medicine, a very key issue is identifying subgroups of patients with different responses to therapy, namely, testing if there exist subgroups of patients who may benefit the most from the treatment under investigation, e.g., treatment-favorable vs nonfavorable subgroups, and then classifying patients into such subgroups. Existing parametric methods are not robust against subjective assumptions, and the typical classification rules ignore the priority of the treatment-favorable subgroup. To address these issues, we propose a semiparametric model, with the subdensities specified nonparametrically. For semiparametric mixture identifiability, the subdensity is assumed to be symmetric unimodal to find its nonparametric maximum likelihood estimate, and it can be extended without the symmetry assumption for a wider application scope. The Wald statistic is used to test the existence of subgroups, and the Neyman-Pearson rule is used to classify each subject. Basic asymptotic properties of the estimators are derived, and simulation studies are performed to evaluate the finite sample properties of the method. Finally, the method is applied to the analysis of a real clinical trial result.
THE PARSEC MACHINE: A NON-NEWTONIAN SUPRA-LINEAR SUPERCOMPUTER

This paper describes how transfer-learning can turn a Beowulf cluster into a full super-computer with supra-linear qualitative acceleration.Harmonic Analysis is used as a real-world example to show the kind of result that can be achieved with the proposed supercomputer architecture, that locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative space-time parallelization between different machines (loosely coupled MIMD).
Agriculture Big Data Analysis System Based on Korean Market Information

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.
Complementing real datasets with simulated data: a regression-based approach

Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy R2 = 97.03%.
A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems

Although traditional methods for video stabilization are time consuming and prone to failure, more promising Deep Learning based solutions have not been thoroughly studied yet. This is mostly due to the lack of suitable training and testing datasets. To address this problem, this paper introduces a comprehensive dataset for training and assessing techniques for video stabilization. It consists of many shaky video sequences, their stable videos and the respective motion parameters that map each frame of the stable video into the corresponding frame in the unstable video. An important aspect of the dataset is the availability of motion parameters. This critical feature enables better assessment of any video stabilization technique, since it allows for additional comparison of the estimated motion parameters with the provided Ground Truth motion parameters. Using this dataset, an evaluation framework for video stabilization technology is also introduced. To demonstrate the practical use of the introduced dataset and the evaluation framework, we compare the performance of two state of the art techniques for video stabilization. The results of this extensive evaluation are presented and both the database and the evaluation framework are also provided in this paper.
A Spatio-temporal Hybrid Network for Action Recognition

Convolutional Neural Networks (CNNs) are powerful in learning spatial information for static images, while they appear to lose their abilities for action recognition in videos because of the neglecting of long-term motion information. Traditional 3D convolution has high computation complexity and the used Global Average Pooling (GAP) on the bottom of network can also lead to unwanted content loss or distortion. To address above problems, we propose a novel action recognition algorithm by effectively fusing 2D and Pseudo-3D CNN to learn spatio-temporal features of video. First, we use Pseudo-3D CNN with proposed Multi-level pooling module to learn spatio-temporal features. Second, the features output by multi-level pooling module are passed through our proposed processing module to make full use of the rich features. Third, a 2D CNN fed with motion vectors is designed to extract motion patterns, which can be regarded as a supplement of Pseudo-3D CNN to make up for the information lost by RGB images. Fourth, a dependency-based fusion method is proposed to fuse the multi-stream features. Finally, the effectiveness of our proposed action recognition algorithm is demonstrated on public UCF101 and HMDB51 datasets.
Learning from satisfying assignments under continuous distributions

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of [DDS15], which studied the learnability of probability distributions over {0, 1}n defined by the set of satisfying assignments to Boolean functions, to Boolean-valued functions defined over continuous domains. In our learning scenario there is a known D over Rn (such as a known normal distribution or a known log-concave distribution) and the learner is given i.i.d. samples drawn from a target distribution Df, where Df is D restricted to the satisfying assignments of an unknown low-complexity Boolean-valued function f. The problem is to learn an approximation D' of the target distribution Df which has small error as measured in total variation distance. We give a range of efficient algorithms and hardness results for this problem, focusing on the case when f is a low-degree polynomial threshold function (PTF). When the background distribution D is log-concave, we show that this learning problem is efficiently solvable for degree-1 PTFs (i.e., linear threshold functions) but not for degree-2 PTFs. In contrast, when D is a normal distribution, we show that this learning problem is efficiently solvable for degree-2 PTFs but not for degree-4 PTFs. Our hardness results rely on standard assumptions about secure signature schemes.
Self-Induced Curriculum Learning in Neural Machine Translation

Self-supervised neural machine translation (SS-NMT) learns how to extract/select suitable training data from comparable -- rather than parallel -- corpora and how to translate, in a way that the two tasks support each other in a virtuous circle. SS-NMT has been shown to be competitive with state-of-the-art unsupervised NMT. In this study we provide an in-depth analysis of the sampling choices the SS-NMT model takes during training. We show that, without it having been told to do so, the model selects samples of increasing (i) complexity and (ii) task-relevance in combination with (iii) a denoising curriculum. We observe that the dynamics of the mutual-supervision of both system internal representation types is vital for the extraction and hence translation performance. We show that in terms of the human Gunning-Fog Readability index (GF), SS-NMT starts by extracting and learning from Wikipedia data suitable for high school (GF=10--11) and quickly moves towards content suitable for first year undergraduate students (GF=13).
Super-telephoto Drone Tracking Using HFR-video-based Vibration Source Localization

In this paper, we propose a super-telephoto drone surveillance system that can search and track a flying drone at a 1 km distance or more within an area spanning hundreds of square meters. By constructing a mirror-drive high-speed pan-tilt control system with a super-telephoto camera lens, remote drone surveillance becomes possible owing to the acquisition of high spatial resolution images of target objects. We adopted a vibration-based drone localization algorithm that can simultaneously detect fast-rotating drone-propellers in an high-frame-rate video using a pixel-level short-time Fourier transform to achieve robust surveillance under various challenging conditions for super-telephoto video-shooting such as low luminance and heat haze. The effectiveness of the super-telephoto drone tracking system is demonstrated by conducting a drone searching and tracking experiment at a 1 km distance using an 800-mm focal length lens with a mirror-drive video-shooting system.
Improved feature size customized fast correlation-based filter for Naive Bayes text classification

Feature selection is an essential part in the data preprocessing. In the text classification, most of the previous feature selection algorithms rarely consider the redundancy between features. This paper focuses on eliminating redundancy. After modifying the formula of feature correlation of origi nal fast correlation-based filter (FCBF) and updating the algorithm strategy, we propose a new approach named improved feature size customized fast correlation-based filter (IFSC-FCBF). In addition, we combine IFSC-FCBF with Naive Bayes (NB) classifier for text classification, and test it in four typical text corpus data sets. The results demonstrate that with the same feature size, IFSC-FCBF method has the advantages of higher accuracy and shorter running time than other methods.
Energy-Quality Scalable Memory-Frugal Feature Extraction for Always-On Deep Sub-mW Distributed Vision

In this work, an energy-quality (EQ) scalable and memory-frugal architecture for video feature extraction is introduced to reduce circuit complexity, power and silicon area. Leveraging on the inherent resiliency of vision against noise and inaccuracies, the proposed approach introduces properly selected EQ tuning knobs to reduce the energy of feature extraction at graceful quality degradation. As opposed to prior art, the proposed architecture enables the adjustment of such knobs, and adapts its cycle-level timing to reduce the amount of computation per frame at lower quality targets. As further benefit, the approach adds opportunities for energy reduction via aggressive voltage scaling. The proposed architecture mitigates the traditionally dominant area/energy of the on-chip memory by reducing the number of pixels stored on chip, introducing memory access reuse and on-the-fly computation. At the same time, EQ tuning preserves the ability to conventionally operate at maximum quality, when required by the task or the visual context. A 0.55 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> testchip in 40nm exhibits power down to 82μW at 5fps frame rate (i.e., 33X lower than prior art), while assuring successful object detection at VGA resolution. To the best of the authors' knowledge, this is the first feature extractor with sub-mW operation and sub-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area, making the proposed approach well suited for tightly power-constrained and low-cost distributed vision systems (e.g., video sensor nodes).
PoWER-BERT: Accelerating BERT inference for Classification Tasks

We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate encoder outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with <1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with <1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at this https URL.
Tennis stroke detection using inertial data of a smartwatch

To assist individuals in sports activities is one of the emerging areas of wearable applications. Among various kinds of sports, detecting tennis strokes faces unique challenges. In this sport the speed of strokes is high, enforcing wearable sensors to have high sampling rates, high-speed bus (to transfer the data to the processor), and the most importantly adequate size of high-speed memory. The constraints encourage researchers to design a custom made hardware to cope with the challenges. The research question that we are trying to address is to show how accurate a commercial smartwatch can detect tennis strokes by using various techniques in machine learning. In this paper, we propose an approach to detect three tennis strokes by utilizing a smartwatch. In our method, the smartwatch is part of a wireless network in which inertial data file is transferred to a laptop where data prepossessing and classification is performed. The data file contains acceleration and angular velocity data of the 3D accelerometer and gyroscope. We also enhanced our method with data prepossessing techniques to elevate data quality. The evaluation of our devised method shows promising results compared to a similar method.
NLP for the Generation of Training Data Sets for Ontology-Guided Weakly-Supervised Machine Learning in Digital Pathology

The combination of ontologies with machine learning (ML) approaches is a hot topic and not yet extensively investigated but having great future potential. This is due to the general fact that both, ontologies and ML, constitute two indispensable technologies for domain-specific knowledge extraction, actively used in knowledge-based systems. Whilst the primary goal of both these approaches are the same, knowledge discovery, little is yet known about how the two sources of knowledge can be successfully integrated. The main data source in digital pathology are whole slide images. For the effective generation of sufficiently large and high-quality training data we need to extract in addition information from medical reports, containing non-standardized text. Since full annotation on pixel level would be impracticably expensive, a practical solution is in weakly-supervised ML. In the project described in this paper we used ontology-guided natural language processing (NLP) for term extraction and a decision tree built with an expert-curated classification system. This demonstrates the practical value of our solution to analyze and structure training data sets for ML and as a tool for the generation of biobank catalogues.
Localization of characters horizontal bounds in text line images with fully convolutional network

Character segmentation is one of the crucial problems of modern text line recognition methods. In this paper, we propose a per-character segmentation method based on the light weight convolutional neural network (CNN) which is suitable for on-premise applications for various mobile devices. The distinctive feature of our method is that it provides the coordinates of the start and end points of each character, not the coordinates of the “cut” between two characters. It allows us to utilize known geometrical properties of glyphs efficiently. Consequently, the target character images are not flawed because of characters intersections or wide spaces. We present the results measured for text lines with various letter spacing. Results illustrate that the proposed method decreases the segmentation error rate for the majority of test datasets.
Short Text Similarity Measurement Using Context from Bag of Word Pairs and Word Co-occurrence

With the rapid development of social networks, short texts have become a prevalent form of social communications on the Internet. Measuring the similarity between short texts is a fundamental task to many applications, such as social network text querying, short text clustering and geographical event detection for smart city. However, short texts in social media always show limited contextual information and they are sparse, noisy and ambiguous. Hence, effectively measuring the distance between short texts is a challenging task. In this paper, we propose a new heuristic word pair distance measurement (WPDM) technique for short texts, which exploits the corpus level word relations and enriches the context of each short text with bag of word pairs representation. We first adjust Jaccard similarity to measure the distance between words. Then, words are paired up to capture latent semantics in a short text document and thus transfer short text into a bag of word pairs representation. The similarity between short text documents is finally calculated through averaging the distances of the word pairs. Experimental results on a real-world dataset demonstrate that the proposed WPDM is effective and achieves much better performance than state-of-the-art methods.
Dangerous object detection by deep learning of convolutional neural network

In recent years, along with the computer operation speed unending enhancement, the computer is employed to carry on the dangerous cargos the examination and the recognition to obtain the more and more widespread applications. Aiming at the disadvantage of high false detection rate in target classification detection using existing feature training classifiers, the work proposes a detection algorithm for hazardous articles with convolutional neural network on the basis of deep learning. For the image to be checked, sliding windows of different scales are used to determine whether there is an object window. For object detection, a convolutional neural network is trained with a large number of positive and negative samples. In order to better adapt to object detection, the topology of the convolutional neural network is improved. The window of suspected hazardous article is input into the improved convolutional neural network for dangerous object detection, and the false detection rate is reduced while maintaining the original detection rate.
Dense urban scene reconstruction using stereo depth image triangulation

In this paper, we present a novel 3D scene reconstruction framework from a single front-mounted stereo camera on a moving vehicle. We propose image triangulations to efficiently render a 3D scene only from 2D textures, while introducing tube meshes as an effective way to render out-of-frustum points. Furthermore, we derive a 3D extended Kalman filter to fuse stereo estimates temporally between frames and showcase a render pipeline, which exploits OpenGL shaders to offload computational costs from the CPU to the GPU. Our approach is able to increase the stereo accuracy compared to competing approaches on the KITTI visual odometry dataset. We also introduce a challenging view prediction evaluation scenario on the SYNTHIA dataset, in which our approach comes out on top in terms of SSIM, 1-NCC error and completeness.