Reviewerly/exHarmony huggingface dataset:
Aeronautic pilot training and augmented reality

&lt;p class="Abstract"&gt;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, resulting in a poor start-up procedure and possibly a safety hazard. 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. 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 with enough detail of a flight panel of a Cessna150, an aircraft used in training flights at the Santa Catarina Aeroclub. The tests were applied in Brazil and the results showed a good response and acceptance from the users.&lt;/p&gt;
Maping of faults on territory of the North Sakhalin plain by remote sensing data

The article presents the results of the research of the fault zones of earth’s crust within the North Sakhalin plain using the lineament analysis of the SRTM model. The faults be identified in order to increase the information content of the maps of geological content. SRTM data was used as input. The analysis performed using the functionality of the LEFA software package. The use of SRTM data made it possible to assess disjunctive disorders in territory of the north of Sakhalin Island. The images of surface were analyzed using the binary-morphological erosion operation or using Canny mathematical algorithms with further digital processing by means of Hough Transform. The paper reveals the position of linear elements in different parts of the North Sakhalin plain. Excellent test of the quality of performed definitions was the results of earlier studies of the eastern part of the North Sakhalin Plain, which were carried out in connection with the development of hydrocarbon deposits and after the strong Neftegorsk earthquake. The data obtained served as the basis for drawing up a map of disjunctive disturbances of Sakhalin Island at scale of 1:500,000. Depending from scale, spatial resolution, and level of generalization of image, disjunctive disturbances of different lengths (transregional, regional, sub-regional, local) and ranks (1 rank from a few to hundred meters, 2 rank from a hundred meters to a kilometer, 3 rank from 1 km to 35 km, 4 rank from 35 to 100 km, 5 rank more than 100 km). The analysis of the SRTM model with the help of the LEFA toolkit allowed us to identify the zones where the earth’s crust fractures are located. The analysis of the SRTM model with the help of the LEFA toolkit allowed us to identify the zones where the earth’s crust fractures are located. As aresult of the analysis, the West Sakhalin, Central Sakhalin, Hokkaido-Sakhalin (Upper Piltun segment), Piltun, Garomai, Val-Langri, Gyrgylany-Daginsky, South Baikal, Udyl and many other faults of less than rank 2 were identified. The main results of the work will be useful in choosing places for the construction of economic and industrial facilities, as well as in the search and exploration of mineral deposits.
An Efficient Public Verifiable Certificateless Multi-Receiver Signcryption Scheme for IoT Environments

Lightweight cryptographic systems for services delivered by the recently developed Internet of Things (IoT) are being continuously researched. However, existing Public Key Infrastructure (PKI)-based cryptographic algorithms are difficult to apply to IoT services delivered using lightweight devices. Therefore, encryption, authentication, and signature systems based on Certificateless Public Key Cryptography (CL-PKC), which are lightweight because they do not use the certificates of existing PKI-based cryptographic algorithms, are being studied. Of the various public key cryptosystems, signcryption is efficient, and ensures integrity and confidentiality. Recently, CL-based signcryption (CL-SC) schemes have been intensively studied, and a multi-receiver signcryption (MRSC) protocol for environments with multiple receivers, i.e., not involving end-to-end communication, has been proposed. However, when using signcryption, confidentiality and integrity may be violated by public key replacement attacks. In this paper, we develop an efficient CL-based MRSC (CL-MRSC) scheme using CL-PKC for IoT environments. Existing signcryption schemes do not offer public verifiability, which is required if digital signatures are used, because only the receiver can verify the validity of the message; sender authenticity is not guaranteed by a third party. Therefore, we propose a CL-MRSC scheme in which communication participants (such as the gateways through which messages are transmitted) can efficiently and publicly verify the validity of encrypted messages.
Urban Footpath Image Dataset to Assess Pedestrian Mobility

This paper presents an urban footpath image dataset captured through crowdsourcing using the mapillary service (mobile application) and demonstrating its use for data analytics applications by employing object detection and image segmentation. The study was motivated by the unique, individual mobility challenges that many people face in navigating public footpaths, in particular those who use mobility aids such as long cane, guide digs, crutches, wheelchairs, etc., when faced with changes in pavement surface (tactile pavements) or obstacles such as bollards and other street furniture. Existing image datasets are generally captured from an instrumented vehicle and do not provide sufficient or adequate images of the footpaths from the pedestrian perspective. A citizen science project (Crowd4Access) worked with user groups and volunteers to gather a sample image dataset resulting in a set of 39,642 images collected in a range of different conditions. Preliminary studies to detect tactile pavements and perform semantic segmentation using state-of-the-art computer vision models demonstrate the utility of this dataset to enable better understanding of urban mobility issues.
A glimpse of a Nordic model? Policy and practice in the digitalisation of the K-12 school and teacher education in Denmark, Finland, Norway and Sweden: Editorial introduction

(2021). A glimpse of a Nordic model? Policy and practice in the digitalisation of the K-12 school and teacher education in Denmark, Finland, Norway and Sweden: Editorial introduction. Education Inquiry: Vol. 12, Teaching and Teacher Education in the Light of the Digitalized K-12 school - a Nordic Perspective, pp. 311-316.
Imbalanced Class handling and Classification on Educational Dataset

Research that has been done related to EDM using classification methods often researchers are not concerned with the existence of the class imbalance in the dataset. Imbalanced class is a condition where there is a significant difference between the number of instances of the minority class and the number of instances of the majority class. This can cause the performance of the classification algorithm to be not optimal because the majority of classifiers can work properly when the class distribution conditions are relatively balanced in the dataset. Several studies that have been conducted state that handling an imbalanced class on the dataset is a critical step to improving the performance of the classification algorithm. This study intends to show the effect of the imbalanced data problem and find out the better resampling method to be implemented into the machine learning process. The resampling method used in this study are SMOTE, Borderline SMOTE, SMOTE - Tomek. The students’ performance dataset used as the data source and classify using several classifiers namely, Logistic Regression, K-NN, CART, Random Forest, SVM, Stacking ensemble method. The SMOTE-Tomek resampling method works best with Random Forest classification that produced 85.8% accuracy on 10-fold cross-validation and 0.89 Geometric Mean which is the best scores among other models.
Workspace Analysis of Spray Painting Robot with Two Working Modes for Large Ship Blocks in Ship Manufacturing

Abstract Spray painting is crucial in the process of shipbuilding and runs through the whole process of shipbuilding. In order to obtain the good coating quality during the painting process, gantry type painting robot could be used because of its resource-saving and environment-friendly characteristics. Based on the structural parameters of the spray painting manipulator with 3DOF (Degree of freedom) gantry, the kinematic model of the painting robot system can be established. From the perspective of the two commonly working modes, namely 3P3R robot structure and the 3P6R redundant robot structure, their spraying workspace are analyzed. The result shows that the workspace volume of the 3P6R robot is much larger than that of the 3P3R robot. However, the inverse kinematics solution of 3P3R robot has analytical solution, which makes the control accurate and easy to use, and through the simulation, we can give the theoretical basis for the engineering.
A Quantum Circuit to Speed-Up the Cryptanalysis of Code-Based Cryptosystems

The significant interest in cryptographic primitives providing sound security margins when facing attacks with quantum computers is witnessed by the ongoing USA National Institute of Standards and Technology Post-quantum Cryptography Standardization process. Sound and precise evaluation of the amount of computation required to break such cryptographic primitives by means of quantum computers is required to be able to choose the cryptosystem parameters.We present a full description of a quantum circuit to accelerate the computation of the solution of the Information Set Decoding problem , which is currently the best known non-structural attack against code-based cryptosystems. We validate our design running it on small instances of error correction codes, which allowed a complete validation on the AtoS QLM quantum computer simulator. We detail the circuit accelerating the exponential complexity search phase in the Lee and Brickell variant of the ISD solver, and provide its computational complexity for cryptographically relevant parameters taken from the third round candidates in the USA post-quantum standardization process.
A Multilayer Approach to Subgraph Matching in HP-graphs

Visual modeling is widely used nowadays, but the existing modeling platforms cannot meet all the user requirements. Visual languages are usually based on graph models, but the graph types used have significant restrictions. A new graph model, called HP-graph, whose main element is a set of poles, the subsets of which are combined into vertices and edges, has been previously presented to solve the problem of insufficient expressiveness of the existing graph models. Transformations and many other operations on visual models face a problem of subgraph matching, which slows down their execution. A multilayer approach to subgraph matching can be a solution for this problem if a modeling system is based on the HP-graph. In this case, the search is started on the higher level of the graph model, where vertices and hyperedges are compared without revealing their structures, and only when a candidate is found, it moves to the level of poles, where the comparison of the decomposed structures is performed. The description of the idea of the multilayer approach is given. A backtracking algorithm based on this approach is presented. The Ullmann algorithm and VF2 are adapted to this approach and are analyzed for complexity. The proposed approach incrementally decreases the search field of the backtracking algorithm and helps to decrease its overall complexity. The paper proves that the existing subgraph matching algorithms except ones that modify a graph pattern can be successfully adapted to the proposed approach.
A Deep Learning Approach to Recognize Human Activity Using Inertial Sensors and Motion Capture Systems

Human Activity Recognition (HAR) plays an important role in behavior analysis, video surveillance, gestures recognition, gait analysis, and posture recognition. Given the recent progress of Artificial Intelligence (AI) applied to HAR, the inputs that are the data from wearable sensors can be treated as time-series from which movement events can be classified with high accuracy. In this study, a dataset of raw sensor data served as input to four different deep learning networks (DNN, CNN, LSTM, and CNN-LSTM). Differences in accuracy and learning time were then compared and evaluated for each model. An analysis of HAR was made based on an attempt to classify three activities: walking, sit-to-stand, and squatting. We also compared the performance of two different sensor data types: 3-axis linear acceleration measured from two inertial measurement units (IMUs) versus 3D acceleration of two retro-reflective markers from the high-end optoelectronic motion capture system (MOCAP). The dataset created from observations of ten subjects was preprocessed with labelling and sliding windows and then used as input to the four frameworks. The results indicate that, for HAR prediction, linear accelerations estimated using IMUs are as reliable as those measured using the MOCAP system. Also, the use of the hybrid CNN-LSTM framework for both methods resulted in higher accuracy (99%).
Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.
Pedagogy of partnership with social institutions as a significant factor of interaction of participants in the educational process between educational institutions and preschool institutions in the training of future educators

The article considers aspects of pedagogical partnership between participants in the educational process in educational institutions and preschool institutions as an effective factor of interaction. The article analyzes the principles, opportunities and ways of partnership pedagogy as a system of effective interaction between teachers, educators and parents for the purpose of personal development of participants in the educational process. The concepts of "partnership", "partnership pedagogy", "social institutions" in the system of relations between all participants in the educational process (teachers, future educators, employees of preschool educational institutions, parents) are revealed. The paper focuses on the criteria that determine the effectiveness of the idea of developing partnership relations as the most productive system of relations in the context of pedagogical interaction.&#x0D; The article highlights the social and pedagogical partnership as a separate form of social interaction of an educational institution with individuals and the importance of the determining role of a higher educational institution in the implementation of its personal and developmental functions in the training of future educators, which includes family, teachers, and the administration of educational institutions. The article considers the purpose and objectives of social and pedagogical partnership as the establishment of partnership relations between participants in the educational process to determine the value orientations of joint activities and ways to achieve partnership. The issues of organization and social and pedagogical partnership as the most effective form of involving parents in the structure of technology, designing a model of partnership interaction between educational institutions and family in the training of future educators are outlined and justified.
Constraint Loss for Rotated Object Detection in Remote Sensing Images

Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth L1 loss is used as the regression loss function. However, we argue that smooth L1 loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.
Scheduling Hardware-Accelerated Cloud Functions

Abstract This paper presents a Function-as-a-Service (FaaS) approach for deploying managed cloud functions onto heterogeneous cloud infrastructures. Current FaaS systems, such as AWS Lambda, allow domain-specific functionality, such as AI, HPC and image processing, to be deployed in the cloud while abstracting users from infrastructure and platform concerns. Existing approaches, however, use a single type of resource configuration to execute all function requests. In this paper, we present a novel FaaS approach that allows cloud functions to be effectively executed across heterogeneous compute resources, including hardware accelerators such as GPUs and FPGAs. We implement heterogeneous scheduling to tailor resource selection to each request, taking into account performance and cost concerns. In this way, our approach makes use of different processor types and quantities (e.g. 2 CPU cores), uniquely suited to handle different types of workload, potentially providing improved performance at a reduced cost. We validate our approach in three application domains: machine learning, bio-informatics, and physics, and target a hardware platform with a combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to traditional FaaS, our approach achieves a cost improvement for non-uniform traffic of up to 8.9 times, while maintaining performance objectives.
ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

Images and videos with text content are a direct source of information. Today, there is a high need for image and video data that can be intelligently analyzed. A growing number of researchers are focusing on text identification, making it a hot issue in machine vision research. Since this opens the way, several real-time-based applications such as text detection, localization, and tracking have become more prevalent in text analysis systems. To find out more about how text information may be extracted, have a look at our survey. This study presents a trustworthy dataset for text identification in images and videos at first. The second part of the article details the numerous text formats, both in images and video. Third, the process flow for extracting information from the text and the existing machine learning and deep learning techniques used to train the model was described. Fourth, explain assessment measures that are used to validate the model. Finally, it integrates the uses and difficulties of text extraction across a wide range of fields. Difficulties focus on the most frequent challenges faced in the actual world, such as capturing techniques, lightning, and environmental conditions. Images and videos have evolved into valuable sources of data. The text inside the images and video provides a massive quantity of facts and statistics. However, such data is not easy to access. This exploratory view provides easier and more accurate mathematical modeling and evaluation techniques to retrieve the text in image and video into an accessible form.
Dialogue Response Generation via Contrastive Latent Representation Learning

Large-scale auto-regressive models have achieved great success in dialogue response generation, with the help of Transformer layers.However, these models do not learn a representative latent space of the sentence distribution, making it hard to control the generation.Recent works have tried to learn sentence representations using Transformerbased framework, but do not model the context-response relationship embedded in the dialogue datasets.In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.An utterance-level contrastive learning is proposed, encoding predictive information in each context representation for its corresponding response.Extensive experiments are conducted to verify the robustness of the proposed representation learning mechanism.By using both referencebased and reference-free evaluation metrics, we provide detailed analysis on the generated sentences, demonstrating the effectiveness of our proposed model.