Reviewerly/exHarmony huggingface dataset:
NADAQ: Natural Language Database Querying Based on Deep Learning

The high complexity behind SQL language and database schemas has made database querying a challenging task to human programmers. In this paper, we present our new natural language database querying (NADAQ) system as an alternative solution, by designing new translation models smoothly fusing deep learning and traditional database parsing techniques. On top of the popular encoder-decoder model for machine translation, NADAQ injects new dimensions of schema-aware bits associated with the input words into encoder phase and adds new hidden memory neurons controlled by the finite state machine for grammatical state tracking into the decoder phase. We further develop new techniques to enable the augmented neural network to reject queries irrelevant to the contents of the target database and recommend candidate queries reversely transformed into natural language. NADAQ performs well on real-world database systems over human labeled workload, returning query results at 90% accuracy.
Calibration-free single camera stereo-digital image correlation for small-scale underwater deformation measurement

Stereo-digital image correlation (stereo-DIC) has been routinely used as a practical and powerful optical technique for surface 3D full-field shape and deformation measurements in various scenarios. However, it is challenging to perform accurate stereo-DIC measurements for submerged objects due to the significant refraction presented at the interfaces of air and water. In this paper, a novel underwater full-field 3D profile and deformation measurements method using the single camera stereo-DIC technique that combines single bilateral telecentric lens imaging and bi-prism-assisted pseudo stereovision is proposed. In using this technique, an immersed surface projects through the (semi-) submerged bi-prism and the bilateral telecentric lens, forming two virtual images on left and right parts of the camera sensor. Matching the virtual left and right images using DIC and substituting the matched image points into a set of newly derived linear equations, accurate 3D profiles and further 3D deformation fields can be readily obtained. The effectiveness and accuracy of the proposed method are successfully validated by a set of real experiments including underwater 3D shape reconstruction, in-plane and out-of-plane translation, and membrane inflation experiments. Because of the distinctive advantages of simple and compact optical configuration, without the need of stereo calibration, and strong robustness against water fluctuation and ambient light variation, the proposed method is expected to be a simple yet effective method for many underwater applications like in vitro biological tissues deformation measurements and submerged materials characterization.
ACRN: a big little hypervisor for IoT development

With the rapid growth of Internet of Things (IoT) and the new emerging IoT computing paradigm such as edge computing, it is prevalent to see that today’s real-time and functional safety devices, particularly in industrial IoT and automotive scenarios, are getting multi-functional by combining multiple platforms into single product. The new trend potentially prompts embedded virtualization as a promising solution in terms of workload consolidation, separation, and cost- effective. However, hypervisors, such as KVM and XEN, are designed to run on a server and can not be easily restructured to fulfill the requirements such as real-time constrains from IoT products. Meanwhile, existing embedded virtualization solutions are normally tailored towards specific IoT scenarios, which makes them hard to extend towards various scenarios. In addition, most commercial solutions are mature and appealing but expensive and closed-source. This paper presents ACRN, a flexible, lightweight, scalable, and open source embedded hypervisor for IoT development. By focusing on CPU and memory partitioning, and mean- while optionally offloading embedded I/O virtualization to a tiny user space device model, ACRN presents a consolidated system satisfying real-time and general-purpose needs simultaneously. By adopting customer-friendly permissive BSD license, ACRN provides a practical industry-grade solution with immediate readiness. In this paper we will de- scribe the design and implementation of ACRN, and conduct thorough evaluations to demonstrate its feasibility and effectiveness. The source code of ACRN has been released at https://github.com/projectacrn/acrn-hypervisor.
Youtube spam detection framework using naïve bayes and logistic regression

YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.
A Calibration Method for a Laser Triangulation Scanner Mounted on a Robot Arm for Surface Mapping

This paper presents and discusses a method to calibrate a specially built laser triangulation sensor to scan and map the surface of hydraulic turbine blades and to assign 3D coordinates to a dedicated robot to repair, by welding in layers, the damage on blades eroded by cavitation pitting and/or cracks produced by cyclic loading. Due to the large nonlinearities present in a camera and laser diodes, large range distances become difficult to measure with high precision. Aiming to improve the precision and accuracy of the range measurement sensor based on laser triangulation, a calibration model is proposed that involves the parameters of the camera, lens, laser positions, and sensor position on the robot arm related to the robot base to find the best accuracy in the distance range of the application. The developed sensor is composed of a CMOS camera and two laser diodes that project light lines onto the blade surface and needs image processing to find the 3D coordinates. The distances vary from 250 to 650 mm and the accuracy obtained within the distance range is below 1 mm. The calibration process needs a previous camera calibration and special calibration boards to calculate the correct distance between the laser diodes and the camera. The sensor position fixed on the robot arm is found by moving the robot to selected positions. The experimental procedures show the success of the calibration scheme.
The Effect of Using Mobile Applications (GeoGebra and Sketchpad) on the Students’ Achievement

Mobile devices are characterized by attractive advantages, and provide broad benefits in the university students’ math teaching. Our study proposes integrating the Mobile Applications in teaching geometry. Findings of the researches showed that students face difficulties in learning geometry. The material and virtual maneuvers may assist the university students in learning and understanding geometry easily. In this research, we investigated the effects of Mobile Applications (GeoGebra and Sketchpad) on the Students’ Achievement. In this concern, we prepared the educational materials, achievement exams and worksheets according to the university curriculum. And they were applied on the students. This quasi-experimental study was applied on 105 students. The results showed that the use of Sketchpad and GeoGebra facilitated the students’ understanding of the geometry concepts. Furthermore, the results showed that GeoGebra had more effect than Sketchpad on the students’ learning.
Quantum anomaly detection with density estimation and multivariate Gaussian distribution

We study quantum anomaly detection with density estimation and multivariate Gaussian distribution. Both algorithms are constructed using the standard gate-based model of quantum computing. Compared with the corresponding classical algorithms, the resource complexities of our quantum algorithm are logarithmic in the dimensionality of quantum states and the number of training quantum states. We also present a quantum procedure for efficiently estimating the determinant of any Hermitian operators $\mathcal{A}\ensuremath{\in}{\mathcal{R}}^{N\ifmmode\times\else\texttimes\fi{}N}$ with time complexity $O(polylogN)$, which forms an important subroutine in our quantum anomaly detection with multivariate Gaussian distribution. Finally, our results also include the modified quantum kernel principal component analysis (PCA) and the quantum one-class support vector machine (SVM) for detecting classical data.
Stereo Matching With Fusing Adaptive Support Weights

Stereo matching has been widely used in various computer vision applications and it is still a challenging problem. Adaptive support weights (ASW) methods represent the state of the art in stereo matching and have achieved outstanding performance. However, the local ASW methods fail to resolve the matching ambiguity in low texture areas because their cost aggregation is limited within local fixed or adaptive support windows. On the other hand, the non-local ASW methods perform cost aggregation along a special tree, so that these methods are often sensitive to high texture areas since some useful connectivity constrains between adjacent pixels are broken during constructing the special tree. To solve these problems, in this paper, a novel and generic fusing ASW framework are proposed for stereo matching. In this framework, we establish dual support windows for each pixel, i.e., a local window and the whole image window. As such, the primitive connectivity between each pixel and its neighboring pixels in the local window can be maintained, and then each pixel not only gets appropriate supports from neighboring pixels within its local support window but also receives more adaptive supports from the other pixels outside the local window. Furthermore, a local edge-aware filter and a non-local edge-aware filter, whose kernel windows correspond to the dual support windows, are merged in order to achieve collaborative filtering of the cost volume. The performance evaluation on the Middlebury and KITTI datasets shows that the proposed stereo matching method outperforms the current state-of-the-art methods.
PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation.

Densely estimating the depth of a scene from a single image is an ill-posedinverse problem that is seeing exciting progress with self-supervision fromstrong geometric cues, in particular from training using stereo imagery. Inthis work, we investigate the more challenging structure-from-motion (SfM)setting, learning purely from monocular videos. We propose PackNet - a noveldeep architecture that leverages new 3D packing and unpacking blocks toeffectively capture fine details in monocular depth map predictions.Additionally, we propose a novel velocity supervision loss that allows ourmodel to predict metrically accurate depths, thus alleviating the need fortest-time ground-truth scaling. We show that our proposed scale-awarearchitecture achieves state-of-the-art results on the KITTI benchmark,significantly improving upon any approach trained on monocular video, and evenachieves competitive performance to stereo-trained methods.
Large-scale implementation of electronic Integrated Management of Childhood Illness (eIMCI) at the primary care level in Burkina Faso: a qualitative study on health worker perception of its medical content, usability and impact on antibiotic prescription and resistance

Electronic clinical decision algorithms (eCDAs) that guide clinicians during patient management are being deployed in resource-limited settings to improve the quality of care and rational use of medicines (especially antimicrobials). Little is known on how local clinicians perceive the use and impact of these tools in their daily practice. This study investigates clinician insights on an eIMCI tool. Specifically, we report their views on its medical content, assess their knowledge on microbes, antimicrobials and the development of resistance.This qualitative study was conducted in the frame of a large-scale implementation in Burkina Faso of an eIMCI tool developed by the Swiss NGO Terre des hommes. Twelve in-depth interviews and 2 focus-group discussions were conducted including 21 health workers from 10 primary care facilities. Emerging themes were identified using qualitative data analysis software.eIMCI users expressed a high level of satisfaction, slowness of the tablet was perceived as the major inconvenience limiting uptake. Several frequent illnesses were identified as missing in the algorithm along with guidance for fever without focus. When asked about existing types of microbes, 9 and 4 out of 21 participants could mention bacteria and virus respectively; only 5 correctly answered that antibiotics had no action on viral disease and 6 mentioned the risk of antibiotic resistance. Level of knowledge was higher in nurses than in less trained health workers. The tool was perceived as improving patient management and the rational use of antibiotics. Positive changes in health facility organisation were reported, such as task shifting and improved triage. eIMCI was also perceived as a learning tool, and users expressed a strong desire to expand the geographic and temporal scope of the intervention.The use of eICMI was widely accepted and perceived as a powerful tool guiding daily practice. Findings suggest that it has positive effects on the health care system beyond the quality of consultation. To support large uptake and sustainability, better training of health workers in infectiology is essential and the medical content of eIMCI should be optimized to include frequent diseases and, for each of them, the appropriate management plan.
Self-Embedding Authentication Watermarking with Effective Tampered Location Detection and High-Quality Image Recovery

Recently, sensor networks have emerged as a high-impact research area, and a number of high profile applications have been proposed. Although significant progress has already been made on securing basic network protocols, additional research is needed to produce techniques and methods for protecting canonical tasks in wireless sensor networks. In this paper, we propose an effective self-embedding authentication watermarking method for tampered location detection and image recovery. The proposed detection method is classified into block-wise and pixel-wise. In block-wise detection, if the size of the block is small, the false positive rate (FPR) will be low. In pixel-wise detection, when the tampered pixels are detected, only the corresponding pixel area is marked. Therefore, the FPR will be lower than that of the block-wise detection. The experimental results demonstrate that the proposed method was effective, and accurate tamper detection and high-quality recovery can be realized even in highly tampered images.
Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms

Accurate microcalcification (μC) detection is of great importance due to its high proportion in early breast cancers. Most of the previous μC detection methods belong to discriminative models, where classifiers are exploited to distinguish μCs from other backgrounds. However, it is still challenging for these methods to tell the μCs from amounts of normal tissues because they are too tiny (at most 14 pixels). Generative methods can precisely model the normal tissues and regard the abnormal ones as outliers, while they fail to further distinguish the μCs from other anomalies, i.e. vessel calcifications. In this paper, we propose a hybrid approach by taking advantages of both generative and discriminative models. Firstly, a generative model named Anomaly Separation Network (ASN) is used to generate candidate μCs. ASN contains two major components. A deep convolutional encoder-decoder network is built to learn the image reconstruction mapping and a t-test loss function is designed to separate the distributions of the reconstruction residuals of μCs from normal tissues. Secondly, a discriminative model is cascaded to tell the μCs from the false positives. Finally, to verify the effectiveness of our method, we conduct experiments on both public and in-house datasets, which demonstrates that our approach outperforms previous state-of-the-art methods.
LITHOLOGICAL MAPPING USING LANDSAT 8 OLI AND ASTER MULTISPECTRAL DATA IN IMINI-OUNILLA DISTRICT SOUTH HIGH ATLAS OF MARRAKECH

Abstract. This study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units and structural map in the south High Atlas of Marrakech. The method of analysis was used by principal component analysis (PCA), band ratios (BR), Minimum noise fraction (MNF) transformation. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination. The application of the SVM methods on ASTER and Landsat satellite data show that these can be used as a powerful tool to explore and improve lithological mapping in mountainous semi-arid, the overall classification accuracy of Landsat8 OLI data is 97.28% and the Kappa Coefficient is 0.97. The overall classification accuracy of ASTER using nine bands (VNIR-SWIR) is 74.88% and the Kappa Coefficient is 0.71.
Variational Autoencoder with Implicit Optimal Priors

The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence. Although the standard Gaussian distribution is usually used for the prior, this simple prior incurs over-regularization. As a sophisticated prior, the aggregated posterior has been introduced, which is the expectation of the posterior over the data distribution. This prior is optimal for the VAE in terms of maximizing the training objective function. However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior. With the proposed method, we introduce the density ratio trick to estimate this KL divergence without modeling the aggregated posterior explicitly. Since the density ratio trick does not work well in high dimensions, we rewrite this KL divergence that contains the high-dimensional density ratio into the sum of the analytically calculable term and the lowdimensional density ratio term, to which the density ratio trick is applied. Experiments on various datasets show that the VAE with this implicit optimal prior achieves high density estimation performance.
Multi-Relational Script Learning for Discourse Relations

Modeling script knowledge can be useful for a wide range of NLP tasks.Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding.While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks.In this paper, we suggest to view learning event embedding as a multi-relational problem, which allows us to capture different aspects of event pairs.We model a rich set of event relations, such as Cause and Contrast, derived from the Penn Discourse Tree Bank.We evaluate our model on three types of tasks, the popular Mutli-Choice Narrative Cloze and its variants, several multi-relational prediction tasks, and a related downstream task-implicit discourse sense classification.
ElasticPipe

Traditional deep neural network (DNN) training is executed with data parallelism, which suffers from significant communication overheads and GPU memory consumption. Considering this, recent pioneering works have attempted to train DNN with model parallelism. However, model partition remains as a major concern and a static partition fails to adapt to the ever-changing computation environment of the cloud cluster. This paper proposes ElasticPipe, which trains the neural network based on pipe-based model parallelism. Unlike data-parallel solutions, each node in ElasticPipe only holds part of the whole model, leading to much lower cost of communication and GPU memory. More importantly, ElasticPipe is able to dynamically tune the workload distribution among different nodes, so that it can mitigate the common straggler effect in cloud environment. Our primary experiment shows, compared to the data-parallel baselines, ElasticPipe can reduce the training time by up to 89.03% without considering straggler effect, and by up to 76.72% with the existence of stragglers. Besides, ElasticPipe also outperforms its static counterpart by up to 28.81% in training performance when stragglers are involved.
Payload-Based Traffic Classification Using Multi-Layer LSTM in Software Defined Networks

Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification.