Reviewerly/exHarmony huggingface dataset:
Augmented Reality life-size flight panel for checklist training

A pre-flight checklist requires in-depth technical knowledge of the aircraft and its dashboard, avionics, instruments, functions, and cabin layout. The student in training to be a pilot or advanced pilot, to obtain an updated certification, must know very thoroughly each instrument and its position on the flight panel. Every second spent searching for the location of an instrument, switch or indicator can waste time completing the checklist, resulting in a poor start-up procedure and possibly a safety hazard. This paper reports preliminary findings of using augmented reality (AR) technology in a training setting. The objective of this research was to obtain preliminary data to determine if the use of AR as a human interface for training can help pilots improve their skills and help them learn new flight panel layouts of different aircraft more efficiently and effectively than traditional training methods. For this research, the methodology used was Human-Centered Design (HCD) which is a multidisciplinary process that involves many actors who collaborate on design skills, including people who belong to this process such as flight instructors, students, and pilots. A mobile / tablet application prototype was created for the user to have in their pocket, a rich experience with enough detail of a flight panel of a Cessna150, an aircraft used in training flights at the Santa Catarina Aero club. The tests were applied in Brazil and the results showed a good response and acceptance from the users from the users from the users.
Visual Analytics for Extracting Trends from Spatio-temporal Data

Visual analytics combines advanced visualisation methods with intelligent analysis techniques in order to explore large data sets whose complexity, underlying structure and inherent dynamics are beyond what traditional visualisation techniques can handle. The ultimate goal is to expose relevant patterns and relationships from the data, since not everything can be exposed easily through intelligent analysis techniques. On the contrary, the human eye can outperform algorithms in grasping and interpreting subtle patterns, provided it is supported by intelligent visualisations. In this paper, we propose three novel visual analytics techniques for analysing spatio-temporal data. First, we present a fingerprinting technique for discovering and rapidly interpreting temporal and recurring patterns by use of circular heat maps. Next, we present a technique supporting comparisons in time or space by use of circular heat map subtraction. Finally, we propose a technique enabling to characterise and get insights of the temporal behaviour of the phenomenon under study by use of label maps. The potential of the proposed approach to reveal interesting patterns is demonstrated in a case study using traffic data, originating from multiple inductive loops in the Brussels-Capital Region, Belgium.
Fast Replica of Polyglot Persistence in Microservice Architectures for Fog Computing

Aiming to break the software monoliths that traditional approaches usually produce as artifacts, solutions that are based on microservices consist of heterogeneous and independent software platforms to manage applications and data. In this scenario, the term polyglot persistence has been introduced to characterize software solutions where the involved microservices rely on different data storage technologies. Especially in Fog Computing where data are expected to efficiently flow among nodes – usually from the edge to the cloud – the polyglot persistence could have a negative impact since a combination of data replication and transformation is required. The goal of this paper is to study the challenges in data management in Fog Computing when microservices are adopted, and to present a solution which combines the advantages of the physical copy approach performed by network file systems to provide a fast data movement and the ability of the logical copy approach to transform the data. The resulting mix is demonstrated to reduce the time of creating the replica up to 70%.
Model-Free Adaptive Control Based on Neural Network Observer for the Chaotic Power Supply System

In this paper, we considered a type of chaotic power supply system and presented a neural network adaptive method with Neural Network Observer (NNO). First, the mathematical model of the chaotic power supply system is summarized. Then aiming for the unknown model of n-order nonlinear system, the controller is designed by the neural network adaptive method. There is no need to know the accurate mathematical model and state information of the controlled object. We estimate the state information and model information of the controlled object through the input and output data of the object first, and use the obtained estimation results to implement the controller, and give the corresponding theoretical analysis. Finally, the effectiveness of the designed controller is verified by simulation of a power system with chaotic motion.
Implementation of a Real-Time Communication Library Between Smart TV Devices and Android Devices Based on WebSocket for the Development of Applications

The smart device industry has grown in recent years, so the vast majority of people have a smart TV and at least one smartphone in their homes. The transformation of the digital era seeks to improve the user experience and facilitate the use of different smart devices. Due to this, it is proposed to develop a communication library that allows multiple Android devices to be connected to an LG WebOS Smart TV and thus promote the development of applications for Smart TVs, avoiding implementation from scratch and speeding up the deployment of new applications that generate great value for users. In this article, a comparison was made of the main communication protocols existing and compatible with the devices involved. During the development stage, the communication library based on the WebSocket protocol was implemented. Also, a retro videogame was implemented that used the communication library to connect the LG WebOS smart TV with the Android Smartphone. Finally, the results obtained and the opportunities for improvements found for the development of future applications will be shown.
Aggressive Motion Planning for a Quadrotor System with Slung Load Based on RRT

Recent advances in technologies related to Un-manned Aerial Vehicles have led to applications in challenging tasks such as carrying a payload through a small window. Common approaches to solve this task are based on Reinforcement Learning, Quadratic Programming, and Model Predictive Control. Although they provide optimal motion planning, they rely on the necessity of a quadratic cost function or linear constraints for the obstacles. We propose to use a variant of the Rapidly-exploring Random Tree algorithm to solve the task without relying on convex constraints, tuning weights for cost functions or linear constraints. We show that our approach was able to solve the task of crossing a small window with a slung load, and we suggest future modifications to accelerate the convergence rate of the algorithm. The main advantage of the proposed approach is the capability finding a collision-free trajectory for the whole system that is not restricted to linear constraints, and does not depend on cost functions.
Intelligent Control Algorithm for an Automated Autonomous Power Supply System

The paper proposes an algorithm that synchronizes the operation of modules, an intelligent control system for an automated autonomous power supply system (solar system) operating with an n-number of thermal radiation sources located in an environment with a complex infrastructure. An extended block diagram in SDL notation is proposed, which displays the sequence of operations for processing the values of the setting action parameters received from external entities (the user and information-sensor modules registering the effects of the super-system objects), and the synthesis of parameters for adjusting the operating mode of the control object subsystem elements directed to optimize the process of converting thermal energy. A decomposition of complex operations is also proposed, including fuzzy inference systems aimed at aggregating an array of input parameter values (synthesized during the execution of the algorithm or obtained from external entities) into the assessment of the reference influences, the assessment of energy sources, the assessment of energy costs for reorienting the working surface of the converter and transducer position status.
A Simultaneous Object Detection and Component Segmentation Approach Based on Mask R-CNN

Instance segmentation is considered to be one of the critical tasks in image understanding. Recently, Mask R-CNN has established itself as a simple, flexible and effective model in image instance segmentation by demonstrating outstanding performance. However, under specific situations, we not only need to segment the objects, but also need its precise components information and existing instance segmentation architectures including Mask R-CNN are unable to achieve this task. In this paper, we propose a simultaneous object detection and component segmentation approach based on Mask R-CNN. Our architecture contains two main branches, in which the object path is to output the object bounding boxes and the component path is to obtain the component bounding boxes as well as the component mask. For the overlapping problem, we defined an Overlapping Area Ratio(OAR) between object bounding box and component bounding box to confirm their relationships. The result shows that our approach is a practicable way for object and component segmentation, and it's the beginning of object detection and component segmentation.
Customization of self-regulation in the use of ICT for the learning process with continuous improvement: Catholic University of Temuco, Chile

The customization of students' self-regulation regarding the use of information and communication technologies (ICT), is generally aimed as a process of dynamic transitions in their formative or non-formative process. The self-regulation process occurs in a context structured by the convergence theory (ICT and life environment). The objective of this work provides a framework for continuous improvement between the interrelation of activation of the use or adoption of ICT by students in a context of competencies. It was performed with a sample of students. An experimental random sampling design was used. The results demonstrate the manner of application of ICT used by the student in their self-learning. It is observed in the treatment of variability and uncertainty in the environment, it helps trainers to estimate times in training activities in an efficient and fat free way.
Part-Weighted Deep Representation Learning for Person Re-Identification

Person Re-identification (ReID) is to identify the same person across different cameras. It is challenging due to various appearance presented by person images, especially when some person body parts are missing caused by detectors or occlusions. To overcome these challenges, in this paper we propose a Part-Weighted Deep (PWD) model. Our deep multi branch network jointly learns complementary discriminative features from aligned whole body image and body part regions, which are acquired by a pose estimation algorithm. To estimate the reliability of features extracted from local body parts, a Feature Fusion sub-Net (FFN) is designed to learn the weight of each body part, which is used to fuse representation in training stage and aggregate part similarity scores between two person images in testing stage. Extensive experiments demonstrate the significant performance improvements of our PWD model over the state-of-the-art methods on two benchmarks (Market-1501, Duke).
Influence of the structure of educational and professional motivation on the level of aspirations of psychology students of higher educational institutions

Substantiation of the research objectives: The current situation on the labor market imposes new requirements on university graduates, who at the time of graduation from higher educational institutions must have competencies that ensure their competitiveness. Some inconsistency between professional and educational standards gives rise to a contradiction that modern young professionals are forced to solve when entering an independent working life. These risks can be significantly reduced if, during the period of study, students form a harmonious hierarchy of motives in the structure of professional motivation, a realistic level of aspirations and a professionally determined life position. All this determines the purpose of the study : to reveal the features of the hierarchy of motives in the structure of educational and professional motivation of graduates-psychologists of higher educational institutions and the level of their aspirations for future activities. Research methods: method of theoretical analysis and systematization of scientific ideas; ascertaining experiment; methodology for determining the level of personality aspirations (V.K. Gerbachevsky); method of mathematical statistics - Student's t-test. Research results. The study made it possible to establish that the group of leading and accompanying motives of educational and professional activity of psychology students includes: a cognitive motive, a motive for the regularity of the intended results, a motive for self-esteem of personal potential, a motive for self-mobilization. The average level of formation of personality aspirations in future activities was revealed. A correlation relationship has been established between the level of students' aspirations and the leading motives: the cognitive motive and the motive of the regularity of the intended results. Key findings and their significance. In the course of the study, the features in the structure of the hierarchy of educational and professional motivation of psychology students were revealed, the level of their aspirations was determined. This is consistent with the specifics of psychological and pedagogical activity, since it reflects the personal meaning and determines the life-meaning orientations of this activity for future psychologists in the field of education. The data of the conducted study can be useful in the implementation of effective psychological and pedagogical support of students receiving a profession of a humanistic orientation.
Operator Use of Multi-Sensor Data Fusion for Airborne Picture Compilation

A study was conducted to examine the operational use of multi-sensor data fusion for military airborne intelligence, surveillance and reconnaissance. Participants performed a simulated picture compilation task wherein they had to identify all ships and planes in their area of operation using various sensors. One group performed the task using only native sensor data. The second group had imperfect data fusion to help them resolve the kinematic information, but they still had to identify each contact. The results indicated that data fusion automation improved the identification of ships and planes over the native sensor group. However, over time, map clutter continually increased for the group with fusion automation, surpassing the clutter for the native sensor group. The results suggest that while multi-sensor data fusion has benefits for picture compilation, dealing with plot clutter from false and spurious tracks is a key concern. Interface suggestions are provided to mitigate the effects.
Using semi-supervised cluster method to correct the mislabeled training samples of ECG signals

The classification accuracy of electrocardiogram(ECG) signals will decrease when the labels of some samples in the training set are incorrect. To mitigate this negative impact, the semi-supervised method is introduced to correct the mislabeled samples. The proposed method is based on the basic principle that the characteristics of samples of the same category are more similar than those of samples of different categories, so in the feature space,the number of samples of the same category around a sample is more than that of different categories. Cross validation is used to divide the training set into sub training set and validation set, and the samples in the validation set are regarded as unlabeled, k nearest neighbour(KNN) classifier label the samples in the validation set according to the samples in the sub training set. Because there are mislabeled samples in the sub training set, it is difficult for KNN classifier to label all samples in the validation set correctly at one time. So we need to use the above method iteratively. Thus, the mislabeled samples in the training set is basically corrected. Experients on the ECG signal corrected from the MIT-BIH arrhythmia database show the eectiveness of the proposed method.
Game Semantics for Interface Middleweight Java

We consider an object calculus in which open terms interact with the environment through interfaces. The calculus is intended to capture the essence of contextual interactions of Middleweight Java code. Using game semantics, we provide fully abstract models for the induced notions of contextual approximation and equivalence. These are the first denotational models of this kind.
Enhancement of Weakly Illuminated Images Using CNN and Retinex Theory

Images captured in low light environments are more susceptible to loss of information in dark regions, making details of the scene not noticeable to humans. These dark images can also make difficult the use of automatic computer vision algorithms in applications as segmentation, object detection and recognition, or tracking. This paper proposes a Convolutional Neural Network (CNN) based method for the enhancement of weakly illuminated images. The network architecture estimates the illumination of the scene which is further used to enhance the images using the Retinex model. The experiments conducted in datasets with synthetic and natural images proved that our method surpassed other state of the art approaches (quantitatively and qualitatively), creating images with less color distortion.
SEEF-ALDR: A Speaker Embedding Enhancement Framework via Adversarial Learning based Disentangled Representation

Speaker verification, as a biometric authentication mechanism, has been widely used due to the pervasiveness of voice control on smart devices. However, the task of "in-the-wild" speaker verification is still challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without taking into account the elimination of the impact from the identity-unrelated information. To solve the above problem, we propose SEEF-ALDR, a novel Speaker Embedding Enhancement Framework via Adversarial Learning based Disentangled Representation, to reinforce the performance of existing models on speaker verification. The key idea is to retrieve as much speaker identity information as possible from the original speech, thus minimizing the impact of identity-unrelated information on the speaker verification task by using adversarial learning. Experimental results demonstrate that the proposed framework can significantly improve the performance of speaker verification by 20.3% and 23.8% on average over 13 tested baselines on dataset Voxceleb1 and 8 tested baselines on dataset Voxceleb2 respectively, without adjusting the structure or hyper-parameters of them. Furthermore, the ablation study was conducted to evaluate the contribution of each module in SEEF-ALDR. Finally, porting an existing model into the proposed framework is straightforward and cost-efficient, with very little effort from the model owners due to the modular design of the framework.
Why educating for clinical machine learning still requires attention to history: a rejoinder to Gauld <i>et al</i>

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Discerning User Activity in Extended Reality Through Side-Channel Accelerometer Observations

Extended reality technologies such as virtual reality are becoming increasingly common for enterprise applications. They have the potential to create secure multi-user environment in previously less-secure spaces, without the need for privacy filters or secure rooms. In this pilot paper we explore how malicious actors may be able to eavesdrop on a virtual reality session, by tracking the physical movements of a user. This is carried out using a third-party accelerometer, attached to the user. Through initial experimentation, we observe that specific actions and session types can be identified through visual analysis of the accelerometer. We posit there is substantial potential for sophisticated and automatic classification of user activity in VR. We discuss how this may enable eavesdropping by malicious actors, or could serve as a mechanism for improved security.
Deep learning for environmentally robust speech recognition

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy
A Decision Support System for Inexperienced Volunteer Guides to Assist Increased Inbound Tourists in Japan

In recent years, Japan is experiencing a drastic increase of inbound tourists. In order to improve the environment for accepting foreign tourists visiting Japan, the Licensed Guide Interpreters Act was partially revised in 2018, which allows anyone to work as a compensated guide interpreter without the national license. Because inbound tourists have a wide variety of preferences on sightseeing, guides are needed to manage those diversified or sometimes complex requests flexibly. Thus, it is an important issue to ensure a quality of the interpreted guide services, as such uncertified guides may include inexperienced volunteers. This paper proposes a decision support system for inexperienced guides who is developing sightseeing plans for foreign visitors. First, inbound tourist behavior patterns in a city in Japan are extracted from a guide log data, which are recorded by some volunteer guides. By applying the collaborative filtering, the support system is constructed that predict and recommend sightseeing spots and activities by inputting new visitors' interested spots and/or activities. Experimental test demonstrates that the proposed system holds a certain level of accuracy.