Reviewerly/exHarmony huggingface dataset:
IBLIND Quantum Computing and HASBE for Secure Cloud Data Storage and Accessing

Cloud computing is a universal conventional system.With the exponential development of cloud data and storage room, cloud security has turned out to be one of the intriguing exploration regions of cloud registering servers.Property based encryption is an open key encryption calculation that permits cloud users to anchor their delicate data in the general population cloud servers.Credited based Quantum cryptography will anchor the user data transmission and correspondence through cloud frame programmers.Be that as it may, daze quantum registering will anchor the moment spying or getting to of data handling in cloud from any awful cloud provider or outsider.In this paper we proposed an algorithm based on blind quantum computing for to secure the communication between data owner and CSP.And enhanced the hierarchal attribute-based encryption algorithm using BCQ key sharing which provides user data providing and group accessing of cloud data.The experimental results show that the proposed mechanism is efficient than the existing CP-ABE enhancements.
A modular cloud-based ontology framework for context-aware EHR services

Healthcare providers in a heterogeneous distributed environment depend on many non-functional requirements or cross-cutting concerns that could affect the entire Healthcare Information System (HIS). The implementation of crosscutting concerns could potentially lead to a significant impact on the integrity of Electronic Health Records (EHRs), as well as code quality. Aspect-Oriented Programming (AOP) encapsulates the crosscutting concerns to improve the overall quality of software development by reducing the required code to implement collection modules. In this paper, we propose an AOP-based novel approach to improve the elasticity of cloud-based EHR services. We propose an abstract framework for cloud-based HIS, which provides EHR interoperability through incorporating crosscutting concerns. The proposed framework eliminates the need to change the core code by adding high-level abstractions to complex cloud applications. We implement a prototype of the proposed framework to validate its ability in both separation of concerns and the ability to reuse EHR services.
UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE

In this paper, we propose a novel method for UAV anomaly detection in the distributed artificial intelligence environment by using deep learning models. In the conventional artificial intelligence environment, a lot of computing power is required for anomaly detection, so it is not suitable to the UAV environment based on embedded systems. For UAV anomaly detection, distributed artificial intelligence with DPS (Distributed Problem Solving) and MAS (Multi-Agent System) is applied using LSTM-AE and AE models. The experimental results show that the proposed method performs well for anomaly detection in the UAV environment.
Fall Detection Based on Convolutional Neural Networks Using Smart Insole

In recent years, falls have become one of the leading cause of mortality for elderly people without caregivers at home. To tackle this problem, many autonomous monitoring systems for fall detection have been researched. However, considering the complicated computation during the manual feature extraction, these systems ignore the fact that the feedback to the guardian should be provided in real time. In this paper, we propose to apply one-dimensional (1D) convolutional neural networks (CNN) in automatic fall detection using built-in tri-axial accelerometer and gyroscope sensor in smart insole. By extracting signal features of the raw sensor data automatically, the proposed method aims to improve the fall detection rate with minimal computational complexity. Experimental results are provided to show the performance of the proposed system in fall detection and activity classification: on 800 falls and 1000 activities of daily living (ADL), the overall average accuracy of our method in detection can reach 98.61%. In addition, our system also achieves a high sensitivity and specificity of 97.92% and 99.58%, respectively.
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification

This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification).In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks.These include deep neural nets and heuristics.Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps.Our system achieves F1-Scores of 66.1% and 67.0% on the development sets for Subtasks 1 and 2 respectively.
Large-scale optimal transport map estimation using projection pursuit

This paper studies the estimation of large-scale optimal transport maps (OTM), which is a well known challenging problem owing to the curse of dimensionality. Existing literature approximates the large-scale OTM by a series of one-dimensional OTM problems through iterative random projection. Such methods, however, suffer from slow or none convergence in practice due to the nature of randomly selected projection directions. Instead, we propose an estimation method of large-scale OTM by combining the idea of projection pursuit regression and sufficient dimension reduction. The proposed method, named projection pursuit Monge map (PPMM), adaptively selects the most ``informative'' projection direction in each iteration. We theoretically show the proposed dimension reduction method can consistently estimate the most ``informative'' projection direction in each iteration. Furthermore, the PPMM algorithm weakly convergences to the target large-scale OTM in a reasonable number of steps. Empirically, PPMM is computationally easy and converges fast. We assess its finite sample performance through the applications of Wasserstein distance estimation and generative models.
SiLearn: an intelligent sign vocabulary learning tool

Purpose Vocabulary learning is a difficult task for children without hearing ability. Absence of enough learning centers and effective learning tools aggravate the problem. Modern technology can be utilized fruitfully to find solutions to the learning difficulties experienced by the deaf. The purpose of this paper is to present SiLearn – a novel technology based tool for teaching/learning sign vocabulary. Design/methodology/approach The proposed mobile application can act as a visual dictionary for deaf people. SiLearn is equipped with features that can automatically detect both text and physical objects and convert them to their corresponding signs. For testing the effectiveness of the proposed mobile application quantitative analyses were done. Quantitative analysis is based on testing a class of 28 students belonging to St Clare Oral School for the Deaf, Kerala, India. This group consisted of 17 boys and 11 girls. Analysis was also done through questionnaire. Questionnaires were given to teachers, parents of deaf students learning sign language and other sign language learners. Findings Results indicate that as SiLearn is very effective in sign vocabulary development. It can enhance vocabulary learning rate considerably. Originality/value This is the first time that artificial intelligence (AI) based techniques are used for early stage sign language learning. SiLearn can equally be used by children, parents and teachers for learning sign language.
Computing Linear Restrictions of Neural Networks

A linear restriction of a function is the same function with its domain restricted to points on a given line. This paper addresses the problem of computing a succinct representation for a linear restriction of a piecewise-linear neural network. This primitive, which we call ExactLine, allows us to exactly characterize the result of applying the network to all of the infinitely many points on a line. In particular, ExactLine computes a partitioning of the given input line segment such that the network is affine on each partition. We present an efficient algorithm for computing ExactLine for networks that use ReLU, MaxPool, batch normalization, fully-connected, convolutional, and other layers, along with several applications. First, we show how to exactly determine decision boundaries of an ACAS Xu neural network, providing significantly improved confidence in the results compared to prior work that sampled finitely many points in the input space. Next, we demonstrate how to exactly compute integrated gradients, which are commonly used for neural network attributions, allowing us to show that the prior heuristic-based methods had relative errors of 25-45% and show that a better sampling method can achieve higher accuracy with less computation. Finally, we use ExactLine to empirically falsify the core assumption behind a well-known hypothesis about adversarial examples, and in the process identify interesting properties of adversarially-trained networks.
A Method for Handling Multi-Occlusion in Depth Estimation of Light Field

With the blossom of light field cameras, a number of studies followed. Depth estimation becomes one of the most important problems in light field and numerous algorithms have been proposed to achieve accurate depth map from a light field image. However, few algorithms take occlusions into consideration, especially for multi-occlusion. This makes incorrect depth estimation in object boundaries. In this paper, based on theories of the photo-consistency for Lambertian surfaces and the correspondence between spatial space and angular space, we proposed a method to handle occlusions in depth estimation of light field. The occlusion edges are extracted firstly by the Lucas-Kanade optical flow algorithm. Secondly K-means clustering is applied for each pixel in angular patch adaptively. Thirdly the initial depth map is obtained by aggregating the modified matching costs respectively in the angular patch. Finally the depth map is regularized using Markov Random Field and weighted median filter. Experimental results show that the proposed method achieves more accurate depth map on synthetic datasets, especially in multi-occlusion boundaries.
Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning

This paper presents an online, robust, and model-free motion planning framework for kinodynamic systems. In particular, we employ a Q-learning algorithm for a two player zero-sum dynamic game to account for worst-case disturbances and kinodynamic constraints. We use one critic, and two actor approximators to solve online the finite horizon minimax problem with a form of integral reinforcement learning. We then leverage a terminal state evaluation structure to facilitate the online implementation. A static obstacle augmentation, and a local replanning framework is presented to guarantee safe kinodynamic motion planning. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability, while maintaining robustness and optimality. We finally evaluate the efficacy of the proposed framework with simulations and we provide a qualitative comparison of kinodynamic motion planning techniques.
Kinect-Based Platform for Movement Monitoring and Fall-Detection of Elderly People

The presented article details our platform for movement monitoring and fall-detection of persons based on data acquired from a Microsoft Kinect v2 sensor. The proposed platform is programmed in the C# programming language for more efficient real-time analysis of the obtained spatial data and future modularity - allowing the integration of other data sources (e.g., thermal sensors, accelerometer data or electrocardiogram recordings) to create a sophisticated monitoring platform. The primary intended use of the platform is to monitor elderly people living alone and, in case of fall detection, transmit relevant information to relatives or medical staff and/or perform specific actions (e.g., turn off kitchen appliances).
Planning Swift Maneuvers of Quadcopter Using Motion Primitives Explored by Reinforcement Learning

In this work, we propose a novel, learning-based approach for swift maneuver planning of unmanned aerial vehicles using motion primitives. Our approach is composed of two main stages: learning a set of motion primitives during offline training first, and utilization of them for online planning of fast maneuvers thereafter. We propose a compact disposition of motion primitives which consists of roll, pitch, and yaw motions to build up a simple yet effective representation for learning. Thanks to this compact representation, our method retains an easily transferable, reproducible, and referable knowledge which caters for real-time swift maneuver planning. We compare our approach with the current state-of-the-art methods for planning and control, and show improved navigation time performance up to 25 % in challenging obstacle courses. We validate our approach through software-in-the-loop Gazebo simulations and real flight tests with Diatone FPV250 Quadcopter equipped with PX4 FMU.
A Personalized Recommendation System Based on User Portrait

This paper focus on video recommendation in response to the explosive growth of movies which has made people in the dilemma of information overload for a long time. It is particularly important to push movies accurately. Regarding this situation, this paper studies how to recommend the most similar movies to users based on their interest tags and browsing behavior. Compared with the collaborative filtering algorithm, whose processing effect is not good confronting scoring data sparse problem. This paper makes full use of the movie review data, and the experimental data set is obtained by crawling the high-quality review of the Douban website. The experimental results show that the proposed method can effectively solve the problem of difficulty in calculating the similarity of the traditional recommendation algorithm when the user data set is incomplete, and the recommended effect is satisfactory.
Malaria Detection Using Custom Convolutional Neural Network Model on Blood Smear Slide Images

Malaria is a life-threatening disease and is a concern of global health threat. The standard way of diagnosing the malaria is by visually examining them under microscope and is very lengthy and tedious task. In this paper, the authors has purposed custom Convolutional Neural Network model for detection of malaria on blood smear slide images. The images are available on website of U.S. National Library of Medicine. The proposed model uses various deep learning layers like convolution layer, max pooling layer, batch normalization layer and fully connected layer. The model achieves 99.71% accuracy in training and 98.23% accuracy on the test data. The study purposes a robust CNN models for detecting infected cell. The training and testing were performed on the 27,558 single cell images.
Greedy Incremental Support Vector Regression

Support Vector Regression (SVR) is a powerful supervised machine learning model especially well suited to the normalized or binarized data.However, its quadratic complexity in the number of training examples eliminates it from training on large datasets, especially high dimensional with frequent retraining requirement.We propose a simple two-stage greedy selection of training data for SVR to maximize its validation set accuracy at the minimum number of training examples and illustrate the performance of such strategy in the context of Clash Royale Challenge 2019, concerned with efficient decks' win rate prediction.Hundreds of thousands of labelled data examples were reduced to hundreds, optimized SVR was trained on to maximize the validation R 2 score.The proposed model scored the first place in the Cash Royale 2019 challenge, outperforming over hundred of competitive teams from around the world.
BELID: Boosted Efficient Local Image Descriptor

Efficient matching of local image features is a fundamental task in many computer vision applications. Real-time performance of top matching algorithms is compromised in computationally limited devices, due to the simplicity of hardware and the finite energy supply. In this paper we present BELID, an efficient learned image descriptor. The key for its efficiency is the discriminative selection of a set of image features with very low computational requirements. In our experiments, performed both in a personal computer and a smartphone, BELID has an accuracy similar to SIFT with execution times comparable to ORB, the fastest algorithm in the literature.
An OBDD-Based Technique for the Efficient Synthesis of Garbled Circuits

Secure Multi-party Computation (SMC) protocols are exploited to perform collaborative computation of a function between two or more parties while keeping the privacy of the private inputs and sharing the computed result only. The Garbled Circuit (GC) protocol, proposed by Yao, is one of the possible approaches to solve the SMC problem, based on the evaluation of the Boolean Circuit representing the given function. Recently, the question to improve efficiency in secure multi-party computation has gained much interest. One of the proposed techniques to increase the efficiency of the GC protocol is based on the reduction of the number of non-XOR gates in the Boolean circuit, since the evaluation of XOR gates have no cost for the execution of the whole protocol. The aim of this work is to define a post-processing procedure that, given an optimized GC, decreases the number of non-XOR gates by transforming some parts of the circuit. The strategy is based on the fact that some gates behave as XORs apart from one output and then, if that input never occurs, those gates can be replaced by a XOR without changing the output of the overall network. The technique we propose is based on the analysis of the GC by using Ordered Binary Decision Diagrams (OBDD) representation. We present the application of our technique to some standard circuits to show the effectiveness of our proposal.
Hyperreal Instruments: Bridging VR and Digital Fabrication to Facilitate New Forms of Musical Expression

Virtual Reality (VR) and digital fabrication technologies today are ushering in a new wave of opportunities in instrument design; the marriage of these two domains, seemingly at odds with each other, can bring impossible instruments to life. In this article, the authors first sample such instruments throughout history. The authors also look at how technology has facilitated the materialization of impossible instruments from the twentieth century on. They then discuss the bridging of VR and fabrication as a new frontier in instrument design, where synthetic sounds can be used to condition an equally synthetic sensory scaffolding upon which the time-varying spectra can be interactively anchored: The result is new instruments that can defy our sense of audiovisual reality while satisfying our proprioceptive and haptic expectations. The authors report on their ongoing work as well as their projections of how emerging technologies in VR and fabrication will shape the design of new musical interfaces.
Shuttering methods and the artifacts they produce

When exposure times were measured in minutes, the opening and closing of the shutter was essentially instantaneous. As more sensitive films and brighter optics became available, exposure times decreased, the travel time of the shutter mechanism became increasingly significant, and artifacts became visible. Perhaps the best-known shutter artifacts are the spatio-temporal distortions associated with photographing moving subjects using a focal-plane shutter or sequential electronic sampling of pixels (electronic rolling shutter). However, the shutter mechanism also can cause banding with flickering light sources and strange artifacts in out-of-focus regions (bokeh); it can even impact resolution. This paper experimentally evaluates and discusses the artifacts caused by leaf, focal plane, electronic first curtain, and fully electronic sequential-readout shuttering.
Local Decoding and Update of Compressed Data

In compressing large datasets it is often desirable to guarantee locality properties that allow the efficient decoding and efficient update of short fragments of data. This paper proposes a universal compression scheme for memoryless sources with the following features: 1. the rate can be made arbitrarily close to the entropy of the underlying source, 2. constant-sized (as a function of the blocklength) fragments of the source can be recovered by probing a constant number of codeword bits on average, 3. the update of constant-sized fragments of the source can be achieved by reading and modifying a constant number of codeword symbols on average, and 4. the overall encoding and decoding complexity is quasilinear in the blocklength of the source.
Multiscale Variability of Precipitation and Their Teleconnection with Large-scale Climate Anomalies: A Case Study of Xi'an City, China

Wang, X.; Jiang, R.; Xie, J.; Zhao, Y.; Li, F., and Zhu, J., 2019. Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: A case study of Xi'an city, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 417–426. Coconut Creek (Florida), ISSN 0749-0208.The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Nino-Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot.