Reviewerly/exHarmony huggingface dataset:
Re-considering Teacher Professional Development in the Pandemic Era: The Uruguayan Case

Abstract For more than a decade, Uruguay has maintained a considerable investment in digital technologies in primary and secondary education, as well as teacher training centers, through the implementation of the Ceibal Plan (Basic IT Computer Science Educational Connectivity Plan for Online Learning). When the COVID-19 pandemic arrived in Uruguay in March 2020, the government ordered the suspension of all face-to-face classes until June 2020. Then, a progressive return to school was introduced. Fortunately, the country already had a well-established technological infrastructure to rely on. Teachers across all levels of education had to quickly adapt to virtual teaching by adopting strategies that presented significant challenges, and which will make a lasting impact on teacher training and professional development. This article examines Uruguayan standards for the professional development of teachers, with a focus on digital teaching skills for online instruction. Practical examples of Ceibal initiatives are provided to promote the pedagogical use of technologies by teachers. We argue that the emergency that Latin America and Uruguay faced in 2020 led to a fundamental rethink and redesign of teacher professional development courses and institutional support. The COVID-19 crisis has presented an enormous shock to the traditional model of education delivery, and to teacher training programs. Digital technologies are not merely devices for conveying educational content, but rather should be seen as tools for maximizing meaningful learning for students. For this purpose, adequate training and support are essential for teachers to be prepared for future scenarios of even greater uncertainty. Keywords: Teacher Professional Development, Uruguay, Emergency Remote Teaching; Educational Technology; Technology Uses in Education; Compulsory Education; Educational Practices.
Pelatihan Manajemen Pertunjukan Virtual Seni Anak Usia Dini Berbasis Budaya Lokal

Early childhood art performance management training is given to kindergarten teachers to make it easier for teachers to manage the need to organize virtual art performances for early childhood, and while still paying attention to the psychological aspects of early childhood. The participants of this local culture-based early childhood virtual art performance management training were representatives of the 12 people of Pokja 02 Kediri City Kindergarten teachers, and the implementation took place at Santo Yoseph II Kindergarten, Kediri City. The methods used in this training are lectures, question and answer discussions and demonstrations, and mentoring. The main resource persons for this training were the chairperson and members of the team proposing the application of science and technology for the community, namely 4 people, assisted by 1 student. The results of this local culture-based early childhood virtual art performance management training are very useful for teachers. It is felt that most teachers still lack the knowledge and skills to design virtual art performances for early childhood, and the training organized is very helpful for teachers in designing ideas for virtual art performances for early childhood.
An Efficient Multidocument Blind Signcryption Scheme for Smart Grid-Enabled Industrial Internet of Things

The smart grid-enabled industrial Internet of Things (SG-IIoT) is a hybrid data communication network connected with the power grid that collects and analyzes data from transmission lines, distribution substations, and consumers. In the IIoT setting, SG provides predictive information to its supplier and customers on how to effectively manage the power based on this aggregated data. To achieve this goal, every virtual or physical entity in the SG-IIoT must be linked and accessible over the Internet, which can be susceptible to numerous cyberattacks. In this paper, we propose a multidocument blind signcryption scheme to simultaneously resolve the security and efficiency issues. The proposed scheme performs the blind signature and encryption operation on multiple digital documents in one step because SG-IIoT outputs a large amount of data that needs to be blind signed and encrypted in a batch. The proposed scheme employs the concept of hyperelliptic curve cryptography (HECC), which is lightweight owing to the smaller key size. The comparative analysis in both security and efficiency with the relevant existing scheme authenticates the viability of the proposed scheme.
Corporate social responsibility and employee loyalty: Role of pride and commitment

Recently, corporate social responsibility (CSR) is one of the most important issues that have received the attention of our whole society in all sectors.The current research attempts to examine the influence mechanism of corporate social responsibility on the loyalty of employees who work in the firms.This research also clarifies the mediating effects of employees' pride perception and commitment in the relationship between CSR performance of the firms and employees' loyalty intention.This research collected data through questionnaire survey using a convenient sampling method with the respondents who are employees in small and medium firms in Hanoi city, capital of Vietnam.Finally, 210 valid questionnaires collected that were utilized to test the hypothesis applying PLS-SEM approach with SmartPLS software.The results showed that CSR does not directly affect employees loyalty behavior, but via the mediating effects of employees' pride perception and commitment with the firms.Moreover, perceived pride of employees has no direct effect on employees' loyalty behaviors with the firms.
Analysis of Music Wisdom Education Mode under the Background of Internet of Things

With the rapid progress of information technology, more and more things can be transmitted and analyzed in the form of data through sensors. The idea of interconnection of all things is gradually becoming a reality. With its blessing, many areas of people’s life are quietly changing, such as health care and education. In particular, the concept of smart classroom makes music, geography, and other traditional subjects, which are in a weak position and glow with a new era glory. This paper takes the music wisdom education mode under the background of Internet of things as the research object, analyzes the contemporary practice of music wisdom classroom by using the methods of data analysis and vertical comparison, and uses the methods of data analysis and longitudinal comparison to analyze the contemporary practice of music wisdom classroom, the practical choices it provides in the music teaching process, and the innovative music teaching design form. The strengths that enhance students’ cultural experience in the music classroom and other areas can be elaborated upon. And its future development trend is expected. Looking forward, it will make remarkable achievements in intelligent attendance management, the connotation and extension of innovative music wisdom education in compound classroom interaction, and so on.
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity

In this work, we introduce LEAD, an approach to dis-cover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner. While there have been advances in self-supervised learning of image features for instance-level tasks like classification, these methods do not ensure dense equivariant representations. The property of equivariance is of interest for dense prediction tasks like landmark estimation. In this work, we introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We follow a two-stage training approach: first, we train a network using the BYOL [13] objective which operates at an instance level. The correspondences obtained through this network are further used to train a dense and compact representation of the image using a lightweight network. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations while also improving generalization across scale variations.
Mission Planning for Underwater Survey with Autonomous Marine Vehicles

With the advancement of intelligent vehicles and unmanned systems, there is a growing interest in underwater surveys using autonomous marine vehicles (AMVs). This study presents an automated planning strategy for a long-term survey mission using a fleet of AMVs consisting of autonomous surface vehicles and autonomous underwater vehicles. Due to the complex nature of the mission, the actions of the vehicle must be of high-level abstraction, which means that the actions indicate not only motion of the vehicle but also symbols and semantics, such as those corresponding to deploy, charge, and survey. For automated planning, the planning domain definition language (PDDL) was employed to construct a mission planner for realizing a powerful and flexible planning system. Despite being able to handle abstract actions, such high-level planners have difficulty in efficiently optimizing numerical objectives such as obtaining the shortest route given multiple destinations. To alleviate this issue, a widely known technique in operations research was additionally employed, which limited the solution space so that the high-level planner could devise efficient plans. For a comprehensive evaluation of the proposed method, various PDDL-based planners with different parameter settings were implemented, and their performances were compared through simulation. The simulation result shows that the proposed method outperformed the baseline solutions by yielding plans that completed the missions more quickly, thereby demonstrating the efficacy of the proposed methodology.
Analytical nanoscience

Russ Algar, Tim Albrecht, Karen Faulds and Jun-Jie Zhu introduce the Analyst themed collection on analytical nanoscience.
DIOR: DIstill Observations to Representations for Multi-Object Tracking and Segmentation

Multi-object tracking (MOT) has long been a crucial topic in the field of autonomous driving and security monitoring. With the saturation of the bounding-box-based MOT algorithms in recent years, a new task to track objects with instance segmentation, called multi-object tracking and segmentation (MOTS), provides a finer level of scene understanding and introduces potential improvements in tracking accuracy. In this paper, we introduce a video-based MOTS framework, named DI still Observations to Representations (DIOR). A feature distiller is designed to extract and balance the comprehensive object representations: 1) the temporal distiller aggregates context information for consistency of features and smoothness of prediction longitudinally; 2) the spatial distiller on the target of interest within each bounding box removes ambiguity and irrelevance of background in the learned features. The subsequent tracking steps start with Hungarian matching based on feature similarity and masks continuity, which is efficient and straightforward. In addition, we propose short-term retrieval (STR) and long-term re-identification (re-ID) modules to avoid missing associations due to failures in detection or possible occlusion. Our method achieves state-of-the-art performance in both MOTS20 and KITTI-MOTS benchmarks.
Networking Feasibility of Quantum Key Distribution Constellation Networks

Quantum key distribution constellation is the key to achieve global quantum networking. However, the networking feasibility of quantum constellation that combines satellite-to-ground accesses selection and inter-satellite routing is faced with a lack of research. In this paper, satellite-to-ground accesses selection is modeled as problems to find the longest paths in directed acyclic graphs. The inter-satellite routing is interpreted as problems to find a maximum flow in graph theory. As far as we know, the above problems are initially understood from the perspective of graph theory. Corresponding algorithms to solve the problems are provided. Although the classical discrete variable quantum key distribution protocol, i.e., BB84 protocol, is applied in simulation, the methods proposed in our paper can also be used to solve other secure key distributions. The simulation results of a low-Earth-orbit constellation scenario show that the Sun is the leading factor in restricting the networking. Due to the solar influence, inter-planar links block the network periodically and, thus, the inter-continental delivery of keys is restricted significantly.
A Principled Design of Intelligent Agent for the SLA negotiation process in cloud computing

Cloud Service Providers (CSPs) are in massive demand with the growing field of information technology. CSP produces enormous solutions and support for cloud, IoT, Fog, and Edge computing. In the current era of the competitive cloud market, consumer satisfaction plays an important role. Quality of Services (QoS) fulfilment for service level agreement (SLA) for CSP and consumers is always given more attention. Consumers face problems over resource delivery after signing SLA and CSP issues to provide over-demanded resources. In the dynamic market of cloud and heterogeneous environments, the existing system needs an intelligent agent-based negotiation system (IANS) to handle responsibilities. IANS will perform the negotiation process and measure the key factors to make fast decisions with maximum utility value at an affordable price. IANS uses a behavioural learning analysis algorithm between consumers and CSP to select a reliable, secure server at a minimum cost. The negotiation process will be using metrics, performance factors, evaluation measures, and behaviour learning for decision making.
An Intelligent Vehicle Alarm User Terminal System Based on Emotional Identification Technology

Negative emotions could increase the risks of traffic accidents. However, the driver’s emotional identification is rarely considered in the current design of intelligent vehicle alarm user terminals (IVAUTs). To solve the problem, this paper tries to design an IVAUT system based on emotional identification technology. Firstly, the transformer network was combined with a convolutional neural network (CNN) into a voice emotional identification system for intelligent vehicle alarm, and an emotional labeling approach was provided. Next, a bimodal fusion model was developed based on decompose-CNNs, which includes an appearance module, an optical flow module, and a bimodal fusion module. The proposed emotional identification approach was proved effective through experiments.
The Influence of SQ3R Technique and Students’ Reading Interest towards Students’ Reading Comprehension Achievement

This study was experimental research. The writer conducted this study at senior high school of SMK Negeri 3 Palembang. This study investigated to find out whether or not : (1) there is significant difference in reading comprehension achievement between the students who both have high reading interest taught by using SQ3R technique and those who are taught by using conventional technique, (2) there is significant difference in reading comprehension achievement between the students who both have low reading interest taught by using SQ3R technique and those who are taught by using conventional technique, (3) there is significant difference in reading comprehension achievement between the students who have high reading interest and low reading interest taught by using SQ3R technique, (4) there is significant difference in reading comprehension achievement between students who have high reading interest and low reading interest taught by using conventional strategy, (5) there is significant interaction effect of SQ3R strategy and reading interest towards the students’ reading comprehension achievement. From the finding it was found that SQ3R contributed to improve students reading comprehension achievement.
Metalearning in Ensemble Methods

Abstract This chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble learning and the resulting ensemble. In this chapter we discuss various lines of research that were followed. Some approaches seek an ensemble-based solution for the whole dataset, others for individual instances. Regarding the first group, we focus on metalearning in the construction, pruning and integration phase. Modeling the interdependence of models plays an important part in this process. In the second group, the dynamic selection of models is carried out for each instance. A separate section is dedicated to hierarchical ensembles and some methods used in their design. As this area involves potentially very large configuration spaces, recourse to advanced methods, including metalearning, is advantageous. It can be exploited to define the competence regions of different models and the dependencies between them.
Visual Communication Design and Wireless Data Transmission Technology for Blockchain Big Data Information Presentation

The present work is aimed at solving the difficulty of BC big data information analysis and the defects of traditional BC platform visual interface (VI), such as nonstandard layout, unreasonable color use, unclear guidance, and increased user learning cost. Firstly, this paper expounds on BC technology, the related theory of information visualization (IV), and the IV design method of BC-generated big data. Secondly, by formulating the user experience design strategy, a big data visual information sharing platform (ISP) based on behavior experience (BE) is designed. Finally, the system performance is tested. The results show that (i) the proposed BE-based big data visual ISP has the basic functions of information query and module jump. The overall interface of the platform is simple and tidy, the information layout is reasonable, the presentation method is more intuitive, and the visual effect is better. (ii) The host throughput of each system module when processing business is greater than 100 times/s, and the success rate (SR) of event handling is greater than 99%. The average response time (RT) of terminal processing is less than 0.3 s, and the average RT of the terminal side is less than 0.4 s. The system’s central processing unit (CPU) occupancy rate (OR) shall be controlled below 30%. The memory OR shall be below 30%, both of which are lower than the standard value, and the system performance meets the standard. To sum up, the proposed ISP has basic functions and ensures good operation performance. It is suitable for the IV of BC-generated big data. The purpose is to provide important technical support for the IV of BC-generated big data and improve the efficiency of users’ data information acquisition and analysis.
Sensors and System for Vehicle Navigation

In recent years, vehicle navigation, in particular autonomous navigation, has been at the center of several major developments, both in civilian and defense applications [...]
CNN based sensor fusion method for real-time autonomous robotics systems

Autonomous robotic systems (ARS) serve in many areas of daily life.The sensors have critical importance for these systems.The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot.Since sensors have advantages and disadvantages over each other they should be used together to reduce errors.In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving.In a real-time application, a camera and LIDAR sensor were tested with these networks.The novelty of this work is that the uniquely collected data set was trained in a new CNN network and sensor fusion was performed between CNN layers.The results showed that CNN based sensor fusion process was more effective than the individual usage of the sensors on the ARS.
A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning

INTRODUCTION: Although accurate segmentation of the prostatic subregions is a crucial step for prostate cancer diagnosis, it remains a challenge. OBJECTIVES: To propose a deep learning (DL)-based cropping pipeline to improve the performance of DL networks for segmenting the prostate’s periphe
Pattern Matching in File System

String matching algorithms have had a significant impact on computer science and are used to solve a variety of real-world challenges.It aids in the completion of time-saving tasks in a variety of fields.These techniques can be handy when looking for a string within another string.Database schema and network systems both employ string matching.We have designed and used several pattern matching algorithms like Naive, KMP(Knuth Morris Pratt) and Automation Matcher algorithm to find patterns in the file system.The proposed system goes through three main steps; Take the names of the files present in the current directory; Take the pattern to search and type of algorithm from the user; And display the pattern if found in the directory.The program is able to accurately detect the patterns in the file system using the mentioned algorithms successfully.
Traceable Scheme of Public Key Encryption with Equality Test

Public key encryption supporting equality test (PKEwET) schemes, because of their special function, have good applications in many fields, such as in cloud computing services, blockchain, and the Internet of Things. The original PKEwET has no authorization function. Subsequently, many PKEwET schemes have been proposed with the ability to perform authorization against various application scenarios. However, these schemes are incapable of traceability to the ciphertexts. In this paper, the ability of tracing to the ciphertexts is introduced into a PKEwET scheme. For the ciphertexts, the presented scheme supports not only the equality test, but also has the function of traceability. Meanwhile, the security of the proposed scheme is revealed by a game between an adversary and a simulator, and it achieves a desirable level of security. Depending on the attacker's privileges, it can resist OW-CCA security against an adversary with a trapdoor, and can resist IND-CCA security against an adversary without a trapdoor. Finally, the performance of the presented scheme is discussed.
HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN

Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, HSVNet, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.