Reviewerly/exHarmony huggingface dataset:
ELSA Speak Application as a Supporting Media in Enhancing Students’ Pronunciation Skill

This research aimed at verifying the effectiveness of using ELSA Speak Application in enhancing students’ pronunciation skills. It applied a quantitative method. Specifically, it used a pre-experimental research design. The population was the English study program students of STKIP Muhammadiyah Enrekang. The sample was the first semester students in the academic year 2018/ 2019. It was chosen through purposive sampling technique by considering that the sample took the pronunciation course at that time. The data were collected through pre-test and post-test which was conducted using the recording. The data attained were analyzed statistically. The result shows that the pre-test means the score is 1.96 while the post-test mean score is 5.79 by applying 0.05 level of significance with 29 degrees of freedom (29), the t-test 6.28 was higher than the t-table 1.699. It indicated that the use of ELSA Speak application is effective in enhancing the pronunciation skill of the first semester students of STKIP Muhammadiyah Enrekang.
A Model Driven Approach to Constructing Knowledge Graph from Relational Database

Abstract Relational database is one of the main data sources in the construction of vertical domain knowledge graph. How to establish the mapping relationship between the concepts in relational database and the corresponding concepts in knowledge graph is the key to improve the efficiency of the construction of vertical domain knowledge graph. To solve this problem, this paper proposes a model driven approach to automatically constructing knowledge graph from relational database based on the Model Driven Architecture (MDA). First of all, a model driven framework is proposed to describe the relational database and knowledge graph using standard XML Schema. Then, a model driven approach is proposed, which extracts database models and data to XML, defines the conversion functions, transforms models and data from database to knowledge graph, and builds knowledge graph according to the expression forms. Finally, using a case to validate the approach. This paper uses the idea of model driven to realize the high-quality transformation from relational database to knowledge graph.
ELSIM: End-to-End Learning of Reusable Skills Through Intrinsic Motivation

Taking inspiration from developmental learning, we present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up. This bottom-up approach allows to learn skills that 1 - are transferable across tasks, 2 - improve exploration when rewards are sparse. To do so, we combine a previously defined mutual information objective with a novel curriculum learning algorithm, creating an unlimited and explorable tree of skills. We test our agent on simple gridworld environments to understand and visualize how the agent distinguishes between its skills. Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improves over a baseline both transfer learning and exploration when rewards are sparse.
Frequent Itemset Mining of User’s Multi-attribute Under Local Differential Privacy

Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute. The main technique has bitmap encoding for converting the user’s original data into a binary string. It also includes how to choose the best perturbation algorithm for varying user attributes, and uses the frequent pattern tree (FP-tree) algorithm to mine frequent itemsets. Finally, we incorporate the threshold random response (TRR) algorithm in the framework and compare it with the existing algorithms, and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.
Robust INS/GPS Coupled Navigation Based on Minimum Error Entropy Kalman Filtering

This paper addresses the results showing the expanded use or improvement of the accuracy, availability, and/or integrity performance of multisensory navigation systems. In addition, Processing algorithms and methods for multisensory systems are significantly improved when noises are non-Gaussian. In the literature, different modified linear and nonlinear Kalman filters (KFs) were derived under the Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In order to improve their robustness with respect to impulsive non-Gaussian noises, different algorithms and techniques based on Gaussian sum filtering, Huber based estimators and recently introduced maximum Correntropy criterion (MCC) have recently been used to counter the weakness of the MMSE criterion in developing different versions of robust Kalman filters.
CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

Recently, the need for research on tracking driver gazes is increasing owing to the development of driver convenience systems, such as autonomous driving or intelligent driver monitoring system, to address traffic accidents caused by negligence. A camera is installed in a vehicle to track the driver's gaze in vehicle environment. The accuracy of estimating driver gazes in vehicle environments reduces if a motion blur of the driver occurs, owing to vehicular vibrations during driving. Most past studies on gaze-tracking of a driver in a vehicle did not consider the motion blurs in their experiments. To address this concern, we propose a method for improving the accuracy of gaze estimation by deblurring the blurred images of a driver from the vehicle. This study is the first attempt to calculate a driver's gaze by deblurring a motion blurred image with CycleGAN, whereas simultaneously using the image information from the two cameras in the vehicle. In previous studies, multiple deep CNNs were used for obtaining the images of a driver's eyes and face. In this study, information obtained from the two cameras in the vehicle are integrated into an image with three channels and thereafter deblurred, consequently reducing the time required for training. Whereas in previous studies the gaze position was not calculated for severe blurs by measuring the level of blur from the input image, the gaze position was calculated for all the input images in this study. From the database (Dongguk blurred gaze database (DBGD)) from 26 drivers in actual vehicles and the Columbia gaze dataset (CAVE-DB) that is an open database, the proposed method exhibited greater accuracy than the existing methods.
Research on Gesture Recognition Method Based on Computer Vision Technology

From the directions of the framework model of gesture recognition system, gesture segmentation, gesture modeling and analysis, and gesture recognition, we systematically summarize the current research status of dynamic vision recognition technology in computer vision, analyze its shortcomings. The results show that gesture recognition based on simple wearable devices, gesture recognition based on deep vision sensors, and multi-method cross-fusion gesture recognition will be the development trends in this field in the future.
Data Exploratory Analysis for Classification in Machine Learning Algorithms

Availability of big data transformed the way machine learning works and the way data is used in machine learning. In real time the data gathered from various sources might be unstructured, incomplete, unrealistic and incorrect in nature. Transforming the data with the above-mentioned qualities and making it ready for analysis is a challenging task. As the quality of data have direct impact on the efficiency of the trained model, data exploratory analysis (DEA) plays a major role in understanding the data and forms the quality training dataset for the machine learning algorithms. This paper emphasizes the importance of DEA in the selection of the significant attributes and filling of missing values to form the quality training dataset. The dataset considered for experimentation is a binary classification problem “Survival prediction of Titanic Passengers”. Experimental results show that training the model with the quality dataset has improved the accuracy as compared to the case when the model was trained with a raw data.
A Decoupling and Bidirectional Resampling Method for Multilabel Classification of Imbalanced Data with Label Concurrence

Label imbalance is one of the characteristics of multilabel data, and imbalanced data seriously affects the performance of the classifiers. In multilabel classification, resampling methods are mostly used to deal with imbalanced problems. Existing resampling methods balance the data by either undersampling or oversampling, which causes overfitting and information loss. Resampling has a significant impact on the minority labels. Furthermore, the high concurrency of majority labels and minority labels in many instances also affects the performance of classification. In this study, we proposed a bidirectional resampling method to decouple multilabel datasets. On one hand, the concurrency of labels can be reduced by setting termination conditions for decoupling, and on the other hand, the loss of instance information and overfitting can be alleviated by combining oversampling and undersampling. By measuring the minority labels of the instances, the instances that have less impact on minority labels are selected to resample. The number of resampling is limited to keep the original distribution of the data during the resampling phase. The experiments on seven benchmark multilabel datasets have proved the effectiveness of the algorithm, especially on datasets with high concurrency of majority labels and minority labels.
<i>Research</i> : How Medical Device Instructions for Use Engage Users

Instructions for use (IFUs) often are used as risk control measures for medical devices with the potential to expose users or others to use-related hazards and hazardous situations that are not entirely mitigated by device design. In the authors' extensive experience observing representative users interact with medical devices in simulated-use studies, individuals' engagement with medical device IFUs varies widely. This variance raises questions regarding how various user groups use IFUs and the factors that make an IFU stronger or weaker for its intended users, uses, and use environments. An online survey was conducted to examine (1) first-time use of medical device IFUs, (2) how first-time use strategies vary across typical user groups for medical devices (e.g., patients, lay caregivers, and healthcare professionals), and (3) which design elements promote initial engagement with IFUs. The results showed that IFUs are used in a variety of ways, including as preparation before use, as guides during use, and as troubleshooting resources during use, as well as that IFUs are not used at all. Overall, the user groups tested responded similarly across all of the survey questions. Bullet point organization, figures, and logical flow were reported to be the most engaging design elements. Small font size and poor organization and flow were reported to be the least engaging design elements. IFU designers can use various usability testing methods to assess their assumptions regarding how a product's users will use the IFU and to make the IFU more engaging.
A Modular and Model-Free Trajectory Planning Strategy for Automated Driving

Intelligent transportation systems that promise high level of automation, has gained significant attention during the last years. Movement planning of such vehicles is one of the most critical issues in order to realize autonomous driving. In this paper, a model-free trajectory planning concept that is based on the analytical solution of a given optimization problem is presented. In order to improve the longitudinal performance, an approach that is based on velocity profile partitioning is proposed. The proposed trajectory planning approach is implemented in a very modular way and tested in closed-loop software in the loop test environment. Effectiveness and capabilities of the proposed trajectory planning framework are validated over several case studies.
Finger trees explained anew, and slightly simplified (functional pearl)

We explicitly motivate the subtle intricacies of Hinze and Paterson's Finger Tree datastructure, by step-wise refining a naive implementation. The result is a new explanation of how Finger Trees work and why they have the particular structure they have, and also a small simplification of the original implementation.
TRAINING THE TRAINERS CURRICULUM ON CO-CREATION OF VIRTUAL REALITY REUSABLE E-RESOURCES

S.T. Konstantinidis 1H. Wharrad1M.M. Nikolaidou2P. Antoniou2K. Neokleous3E. Schiza3M. Matsangidou3F. Frangoudis3M. Hatziaros3M. Avraamides3P.D. Bamidis2C.S. Pattichis3
Real-Time Sickle Cell Anemia Diagnosis Based Hardware Accelerator

Sickle cell anemia (SCA) is a blood disease, which causes distortion in the shape of Red Blood Cells (RBCs) and becomes like a crescent. Traditional methodologies of classifying and counting RBCs that have been used by medical analysts are time-consuming, as well as, cost-effective. In addition, it is possible to make errors throughout the classifying and counting stages. We overcome these limitations by proposing a novel convolutional neural network model that classifies the RBCs into three categories: Normal, Abnormal (sickle shape) and other blood content. We enhanced the model’s efficiency using a hardware accelerator called FPGA (Altera DE2 Cyclone II) to take advantage of its parallelism features. We have evaluated the model’s efficiency with different platforms to show the differentiation in terms of time execution and power computation. Based on our knowledge, the results show that our proposed model has achieved the best accuracy (87.15%) and has high efficiency for real-time diagnosis.
Quantum Turing Machines: Computations and Measurements

Contrary to the classical case, the relation between quantum programming languages and quantum Turing Machines (QTM) has not been fully investigated. In particular, there are features of QTMs that have not been exploited, a notable example being the intrinsic infinite nature of any quantum computation. In this paper, we propose a definition of QTM, which extends and unifies the notions of Deutsch and Bernstein &amp; Vazirani. In particular, we allow both arbitrary quantum input, and meaningful superpositions of computations, where some of them are “terminated” with an “output”, while others are not. For some infinite computations an “output” is obtained as a limit of finite portions of the computation. We propose a natural and robust observation protocol for our QTMs, which does not modify the probability of the possible outcomes of the machines. Finally, we use QTMs to define a class of quantum computable functions—any such function is a mapping from a general quantum state to a probability distribution of natural numbers. We expect that our class of functions, when restricted to classical input-output, will not be different from the set of the recursive functions.
Techniques of Displacement and Deformation Measurements in Speckle Metrology

The speckle effect has the rare distinction of being both a nuisance and an asset to the optics community working on the development of laser-based optical methods to monitor displacements and deformations of engineering structures. Long considered to be a bane for holographers, the speckle effect grew out of its complex to shape an important niche for itself in deformation analysis [1–7]. Ever since the appearance of the pioneering works of Leendertz [8] and Archbold et al. [9] in 1970 demonstrating the use of speckles in displacement measurements, the development of speckle-based methods has gone from strength to strength backed by spates of activities and programs in different laboratories around the world. As a result of this investigation a wide range of procedures have been developed, which can be classified into three broad categories: speckle photography, speckle interferometry, and speckle shear interferometry. While the modes of operation of the different categories will open out to readers as they go through the chapter, it will suffice to say at this stage that it would be dubious, if not misleading, in many instances to make an outright distinction between the procedures belonging to the different categories.
An Isolated Data Island Benchmark Suite for Federated Learning.

Federated learning (FL) is a new machine learning paradigm, the goal of which is to build a machine learning model based on data sets distributed on multiple devices--so called Isolated Data Island--while keeping their data secure and private. Most existing work manually splits commonly-used public datasets into partitions to simulate real-world Isolated Data Island while failing to capture the intrinsic characteristics of real-world domain data, like medicine, finance or AIoT. To bridge this huge gap, this paper presents and characterizes an Isolated Data Island benchmark suite, named FLBench, for benchmarking federated learning algorithms. FLBench contains three domains: medical, financial and AIoT. By configuring various domains, FLBench is qualified for evaluating the important research aspects of federated learning, and hence become a promising platform for developing novel federated learning algorithms. Finally, FLBench is fully open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite will be publicly available from this http URL.
Research on obstacle detection and path planning based on visual navigation for mobile robot

Abstract Both obstacle and path planning are of significance for autonomous visual navigation of a mobile robot which is able to realize free movement and obstacle avoidance. In this paper, a scheme of obstacle detection which is based on binocular vision to identify obstacles is constructed and it is combined with improved A* algorithm for obstacle avoidance motion for mobile robot platform. First, a binocular vision system to detect the robot’s perception of the environment is used and the visual information is obtained. Then a Gaussian filter is used to reduce the noise in the image, and the grayscale image is obtained through the binocular vision system. The gray scale map is matched with SAD (Sum of Absolute Differences) in order to obtain the disparity image with comparison. Second, the location information of the obstacle is extracted through the environmental depth information contained in the disparity image, and the improved A* algorithm is used as the path planning algorithm in this paper, which can modify the path of the planning error. Furthermore, the detection experiment of the robot visual obstacle avoidance system was established and the optimal path was obtained. The examples show that, the obstacle avoidance system can complete the task of obstacle detection and give a better path for the mobile robot.
Information Theory for Future Detection System Construction

Radars are distributed in multi-dimensional space including space, time and frequency spaces. How to make these distributed radar nodes organize, cooperate and jointly process information to form system capability is the key problem for future detection system. For this purpose, the conceptual mechanism, engineering implementation methods and basic theory of the future detection system are respectively discussed in this paper. In the system architecture, a new method for the dynamic construction of detection system is proposed. It is supported by resource virtualization, semantic interconnection and time-sensitive network. And the proposed construction method is centered on the theory of capability generation. In theory, based on the incompleteness and complementarity of information acquired by a single radar, the formation mechanism of “1+1>2” of the system detection capability is analyzed. With information theory and control theory, the basic theory of detection system is established. It is about the allocation of detection elements and their control and processing relationship for different detection tasks.
A multi-sensors pure azimuth data fusion method

Abstract Passive detection sensors have the advantages of good concealment, strong viability, strong resistance to electronic interference, wide coverage of airspace, and stronger detection capabilities for low-altitude targets than active detection sensors, and farther detection distances. It has caused more and more research and application attention in various fields. However, passive detection sensors mainly obtain azimuth information, no stable distance information, and stable tracking of only azimuth targets has many uncertainties. In recent years, how to integrate effective and stable passive detection data has become the focus of scholars research and the difficulty of urgent breakthrough. This article proposes a method of fusion of pure azimuth data for systems with multi-source detection sensors, through the filtering of “modified extreme coordinates ”based on the measurement results of each sensor, “based on the Lagrange daily linear interpolation method” time allocation, “m/n logic-based” track association and fusion, obtain more accuracy and track more stable direction measurement results, and improve the comprehensive capabilities of the multi-source detection sensor system.
Adapting COBIT for Quantum Computing Governance

Quantum computing is a new paradigm that uses the properties of quantum mechanics to achieve computers and technologies more powerful. Quantum Technology will solve some types of problems more efficiently than current technology. Every organization that wants to use all the power of quantum computing to be more competitive in its sector, must have a method that allows it to take advantage of all the value that this technology can provide. This article proposes the development of a framework for the Management and Governance of Quantum Computing based on COBIT.
Scalable Integration of Image and Face Based Augmented Reality

In this paper we present a scalable architecture that integrates image based augmented reality (AR) with face recognition and augmentation over a single camera video stream. To achieve the real time performance required and ensure a proper level of scalability, the proposed solution makes use of two different approaches. First, we identify that the main bottleneck of the integrated process is the feature descriptor matching step. Taking into account the particularities of this task in the context of AR, we perform a comparison of different well known Approximate Nearest Neighbour search algorithms. After the empirical evaluation of several performance metrics we conclude that HNSW is the best candidate. The second approach consists on delegating other demanding tasks such as face descriptor computation as asynchronous processes, taking advantage of multi-core processors.