Reviewerly/exHarmony huggingface dataset:
USING A DIDACTIC TOOL WITH IT-SUPPORT FOR TEACHING GEOGRAPHY IN A SPECIAL SCHOOL IN SLOVAKIA

The article tells about the peculiarities of using electronic didactic tools in teaching special school students in Slovakia. The staff of the Department of Special Pedagogy, Catholic University in Ruzomberk (Slovakia), together with teachers of special schools prepared a didactic manual with IT support considering the modern requirements for the level of knowledge and skills of students with intellectual disabilities. The purpose of the manual was to increase the level of success in learning geography. The content of the didactic guide is based on the typical curriculum for 9th grade students of a special school approved by the Ministry of Education and Science of Slovakia. As an example, the article briefly highlights the essence of the preparatory stage and the specific sequence of the teacher's work when studying one section «Slovakia». In the course of preparing the didactic guide, the peculiarities of psychophysical development of children in special schools, the problems of perception and understanding of new educational information were taken into account. To showcase the effectiveness of our innovative approach, we conducted an experimental test using the developed electronic textbook with IT support in one of the special schools in Slovakia. During this study, we analyzed and compared the level of geography knowledge among students over a span of 6 academic years (2016 – 2018 – traditional education, 2019 – 2021 – education using e-Learning). Based on the statistical data, a noteworthy improvement in academic performance was observed. To validate these results, considering the limited research sample due to the unique nature of special schools' educational process, we employed nonparametric tests such as the Kruskal-Wallis and Dunn's tests for multiple comparisons. These tests were utilized to ensure the reliability and significance of the findings. The results of the mathematical processing of the materials convincingly confirmed a significant increase in the level of academic achievement over the six years of the educational process in a special school. Thus, the electronic didactic guide for studying geography by 9th grade students of special schools proved to be quite effective. The idea of preparing and using such a didactic guide can be used in practice by teachers of other social studies subjects in special schools.
Google Cloud Platform (GCP) Lab

In this Google Cloud lab, readers will take, an in-browser UI that enables them to manage and access Google Cloud services. The main characteristics of Google Cloud will be identified, and the specifics of the lab environment will be covered. Google Cloud projects, fundamental roles, rules, and GCP IAM are covered. It includes detailed instructions with snapshots for setting up a VPC, firewall rules, Cloud NAT, Compute Instance Group, web servers, Autoscaling, Load Balancers, SQL database, and Storage bucket. Your infrastructure will fully operational on the AWS global infrastructure.
Amazon Web Services (AWS) Lab

This chapter contains step-by-step instructions with snapshots of an AWS three-tier web architecture lab utilizing the Free Tier service provided by AWS. You will create and configure an AWS VPC with public and private subnets, a NAT gateway, route tables, a EC2 compute service, an autoscaling group, an Internet-facing load balancer, an Aurora database, and an S3 storage bucket. Your infrastructure will be fully operational on the AWS global infrastructure. By completing this lab, you will have experienced the application of the AWS theoretical content previously presented.
Bringing It All Together

This chapter summarizes the creation of a virtual private cloud (VPC) which is your small part of a public cloud provider’s platform. It demonstrates, step-by-step, the creation of a typical AWS VPC with related supporting services and functions.
Weighing demonstration experiment based on light spot deflection method

Abstract The cantilever beam is a well-known mechanical model among science and engineering students. In general physics experiments, the light spot deflection method serves as an optical amplification technique for measuring small rotations. In this study, a method for measuring the angular deformation of a cantilever beam using the light spot deflection method is proposed and applied to weight measurement. Furthermore, we replaced the scaleplate in the traditional light spot deflection method with charge-coupled device (CCD). By employing the linear arrangement of CCD pixels and the digital speckle algorithm, the convenience and accuracy of the spot movement measurement have been greatly improved.
A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm’s superiority in real-time processing, resource utilization, preservation of the image’s details, and visual effects.
Mathematical Modelling Ability in Outdoor Learning with Mobile Math Trails

The aim of this study is to describe how outdoor learning with mobile math trails can support students' mathematical modelling ability. The method used is design research with three phases, namely preparation and design, teaching experiment, and retrospective analysis. This study was conducted at junior high school involving 32 students. The Hypothetical Learning Trajectory (HLT) was designed and learning activities that include several math trails tasks were created, then students run the activity. Data were collected by observation, interviews, tests, and worksheets. The findings indicate that the learning activity that has been implemented can support students’ mathematical modelling ability. The learning activity can provide opportunities for students to engage in meaningful mathematical modelling activities in accordance with mathematical modelling indicators. The study identified a link between HLT as learning design and mathematical modelling activities in learning process. In addition, it was found that students' mathematical modelling ability had improved.
Automatic Analysis of Lexical Complexity: Evaluating The Linguistic Performance of The National and International Publication

&lt;p&gt;This research is a kind of a comparison study that deals with lexical complexity in the Indonesian scholars’ journal articles. The analysis covers 98 journal articles from the national and international publication using 25 lexical complexity indices. Results of the research showed that the journal articles of international journals have higher mean scores of lexical complexity indices than the national, but significant differences only occurred in the indices for the lexical sophistication and lexical variation, particularly the type-token ratio and verb variation. This may show the performance gap between the two publications from Indonesian academics as to share the lexical characteristic of the international publication surpass the national.&lt;/p&gt;&lt;p&gt;&lt;em&gt;Studi komparatif ini membahas lexical complexity dari artikel-artikel jurnal yang dihasilkan para penulis Indonesia. Analisis dilakukan terhadap 98 artikel jurnal terbitan nasional dan internasional dengan memakai 25 indeks lexical complexity. Hasil penelitian memperlihatkan bahwa artikel-artikel jurnal terbitan internasional memiliki rerata indeks lexical complexity lebih tinggi dibanding terbitan nasional, namun perbedaan signifikan hanya terdapat pada indeks-indeks lexical sophistication dan lexical variation, terutama pada rasio type-token dan verb variation. Hal ini bisa jadi merefleksikan adanya performance gap antara kedua kelompok publikasi yang dihasilkan para akademis Indonesia, sekaligus memperlihatkan bahwa karakteristik leksikal dari publikasi internasional lebih tinggi dibanding publikasi nasional&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Enhancing Drug Recommendations via Heterogeneous Graph Representation Learning in EHR Networks

Electronic health records (EHRs) contain vast medical information like diagnosis, medication, and procedures, enabling personalized drug recommendations and treatment adjustments. However, current drug recommendation methods only model patients' health conditions from EHR data, neglecting the rich relationships within the data. This paper seeks to utilize a heterogeneous information network (HIN) to represent EHR and develop a graph representation learning method for medication recommendation. However, three critical issues need to be investigated: (1) co-occurrence of diagnosis and drug for the same patient does not imply their relevance; (2) patients' directly associated information may not be sufficient to reflect their health conditions; and (3) the cold start problem exists when patients have no historical EHRs. To tackle these challenges, we develop a bi-channel heterogeneous local structural encoder to decouple and extract the diverse information in HIN. Additionally, a global information capture and fusion module, aggregating meta-paths to form a global representation, is introduced to fill the information gaps in records. A longitudinal model using rich structural information available in EHR data is proposed for drug recommendations to new patients. Experimental results on real-world EHR data demonstrate significant improvements over existing approaches.
A Task-Parallel Approach for Localized Topological Data Structures

Unstructured meshes are characterized by data points irregularly distributed in the Euclidian space. Due to the irregular nature of these data, computing connectivity information between the mesh elements requires much more time and memory than on uniformly distributed data. To lower storage costs, dynamic data structures have been proposed. These data structures compute connectivity information on the fly and discard them when no longer needed. However, on-the-fly computation slows down algorithms and results in a negative impact on the time performance. To address this issue, we propose a new task-parallel approach to proactively compute mesh connectivity. Unlike previous approaches implementing data-parallel models, where all threads run the same type of instructions, our task-parallel approach allows threads to run different functions. Specifically, some threads run the algorithm of choice while other threads compute connectivity information before they are actually needed. The approach was implemented in the new Accelerated Clustered TOPOlogical (ACTOPO) data structure, which can support any processing algorithm requiring mesh connectivity information. Our experiments show that ACTOPO combines the benefits of state-of-the-art memory-efficient (TTK CompactTriangulation) and time-efficient (TTK ExplicitTriangulation) topological data structures. It occupies a similar amount of memory as TTK CompactTriangulation while providing up to 5x speedup. Moreover, it achieves comparable time performance as TTK ExplicitTriangulation while using only half of the memory space.
Multi-Scene Mask Detection Based on Multi-Scale Residual and Complementary Attention Mechanism

Vast amounts of monitoring data can be obtained through various optical sensors, and mask detection based on deep learning integrates neural science into a variety of applications in everyday life. However, mask detection poses technical challenges such as small targets, complex scenes, and occlusions, which necessitate high accuracy and robustness in multi-scene target detection networks. Considering that multi-scale features can increase the receptive field and attention mechanism can improve the detection effect of small targets, we propose the YOLO-MSM network based on the multi-scale residual (MSR) block, multi-scale residual cascaded channel-spatial attention (MSR-CCSA) block, enhanced residual CCSA (ER-CCSA) block, and enhanced residual PCSA (ER-PCSA) block. Considering the performance and parameters, we use YOLOv5 as the baseline network. Firstly, for the MSR block, we construct hierarchical residual connections in the residual blocks to extract multi-scale features and obtain finer features. Secondly, to realize the joint attention function of channel and space, both the CCSA block and PCSA block are adopted. In addition, we construct a new dataset named Multi-Scene-Mask, which contains various scenes, crowd densities, and mask types. Experiments on the dataset show that YOLO-MSM achieves an average precision of 97.51%, showing better performance than other detection networks. Compared with the baseline network, the mAP value of YOLO-MSM is increased by 3.46%. Moreover, we propose a module generalization improvement strategy (GIS) by training YOLO-MSM on the dataset augmented with white Gaussian addition noise to improve the generalization ability of the network. The test results verify that GIS can greatly improve the generalization of the network and YOLO-MSM has stronger generalization ability than the baseline.
Home2edu Education at Your Fingertips: An International Web Platform with Educational Resources for Teachers

Teachers and students faced a huge challenge when the first confinements were put in place. COVID-19 created a difficult situation, both economically and socially. The isolation of the children at home had a negative impact on their development. Compared to other pandemics, today we have technology that allows us to teach at a distance. However, not all children and teachers were prepared. They lacked digital skills, equipment, and Internet access. It is in this context that the project HOME 2.0 Education at your fingertips is developed. This way, for two years, there were meetings between teachers who collaborated in the project, sharing knowledge and good practices. In the end, a teacher-to-teacher sharing platform was developed. Its main objective is to share resources to overcome difficulties caused by not having access to school. This paper presents the implementation process of the platform, for which it was decided to perform an Analysis for Requirements with the project members. Through the platform it is possible to submit apps and documents considered relevant by teachers. For those who are looking for these resources, the platform provides a simple way to search and find an app or document that can help in their teaching practice. It was evaluated by teachers from several countries associated with the project.
EFL TEACHERS’ PROBLEMS IN ONLINE LEARNING DURING COVID-19 PANDEMIC AND THEIR SOLUTION

The spread of COVID-19 in Indonesia has had a major impact on the education sector, requiring schools to close and change the learning system, which becomes an online learning system. The purpose of this study was to find out the problems by English junior high school teachers and their solutions. The researcher used descriptive qualitative method. The research subject were six English teachers from two English teachers in MTs Salafiyah Syafi’iyah Tebuireng, MTsN 9 Jombang, and SMPN 2 Diwek. The researcher used data collection techniques with interview guidlines. The results from this study describe that English teachers faced several problems in the online teaching process, especially in teaching reading, and speaking. The problem was due to the absence of direct interaction between the English teacher and students and students still had difficulty reading English texts, the absence of direct interaction between the English teacher and students, the lack of motivation of students, the difficulty of students in accessing online learning, having an electronic device, and the lack of parental guidance of students. As an educator, the English teacher provides solutions to the problems mentioned above in order to anticipate the course of online learning. The solutions for online learning problems due to providing material and assignments using learning applications, and include providing learning materials, preparing online teaching, approaching students, and communicating with parents of students. Hopefully, this study can provide benefits from the results of the problems faced by English junior high school teachers and their solutions in online teaching. So, they can improve their ability in teaching English lessons.
Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication

This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices.
Software-Based Security Approach for Networked Embedded Devices

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution environments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture consists of two parts: the Agent, which is designed to work with low-cost, low-end devices without requiring modifications to the underlying hardware, and the Computing Module, which is designed for slightly more computationally powerful devices. The Computing Module enables devices to write data in protected memory and continuously verifies its integrity to provide protection. Additionally, it utilizes the Agents located on the device to safeguard device applications against attacks by requesting the Agent to generate an application code signature and validating it. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT.
Active RIS-Empowered Signal Cancellation Attacks

When reconfigurable intelligent surface (RIS) is used to initiate attacks without actively generating sources of jamming signals, signal cancellation attacks (SCAs) are the main methods. However, a passive RIS (PRIS) requires a lot of reflecting elements in order to obtain considerable SCAs when attacked systems limit signal leakage to non-target receivers. This puts the PRIS at high risk of being tracked and monitored by enemies. To increase the flexibility of RIS deployment while maintaining a significant attack, this correspondence proposes active RIS (ARIS) to implement SCAs, which is exact opposite to the original intention of proposing ARIS to improve transmission quality. Specifically, with constraints on maximum reflecting amplitude and maximum amplification power, we derive a signal-to-interference-plus-noise ratio (SINR) minimization problem. In order to achieve a fast attack, an algorithm with low complexity is proposed to resolve the non-convex problem. Numerical results show that ARIS can perform a stronger SCA than PRIS under more stringent conditions.
Toward a New Definition of Augmented Reality

In 1997 Ronald T. Azuma introduced a definition for augmented reality. The definition can be considered slightly outdated because of developments in augmented reality and ubiquitous computing. Extended reality environments do not only allow interactive virtual objects superimposed on reality and aligned with reality, but also static, dynamic, and autonomous virtual content that is not under the control of the user of the environment. One aim of AR research is to superimpose (multisensorial) virtual objects on reality that cannot necessarily be distinguished from real objects that are perceived and experienced by the inhabitants of the environment. In this paper, we take it a step further. Especially if we are no longer able to distinguish between virtual and real objects, shouldn't we look for a definition of AR that is more based on experiencing (not necessarily technology-enhanced) reality than on technology? We do this by focusing on multisensorial experiences that augment our world, rather than on the technology, present or not, that enables these experiences and distinguishes our experiences from those of others. That such a viewpoint has not taken shape before is mainly due to the vision-biased view of what AR research should entail.