Reviewerly/exHarmony huggingface dataset:
Enhancing Cognitive and Readiness Skills: An Integrated Assessment of Perceptual and Memory Abilities for SLD students

Abstract: This study explores how cognitive and readiness skills in students with Specific Learning Disabilities (SLD) can be improved through an integrated assessment of their perceptual and memory abilities. SLD students often struggle with processing information, retaining knowledge, and preparing for tasks in academic environments. Recognizing the crucial link between perceptual and memory abilities is essential for tailoring interventions to support these students effectively. Utilizing a comprehensive assessment framework, the research aims to elucidate how perceptual and memory abilities influence cognitive processing and readiness skills in SLD students. It employs a variety of methodologies, including traditional psychometric measures and innovative approaches such as virtual reality simulations and cognitive task analyses, to offer a holistic perspective By integrating multiple methodologies, the study seeks to uncover the intricate interplay between perceptual and memory abilities and their impact on cognitive readiness among SLD students. The findings are expected to guide the development of targeted interventions and educational practices tailored to their unique needs. The implications of this research extend beyond academia, potentially informing the design of training programs and cognitive enhancement interventions for SLD students. By addressing their specific challenges, the study aims to foster inclusivity and equity in education. This research highlights the importance of assessing perceptual and memory abilities in SLD students to enhance their cognitive readiness and academic outcomes. By identifying factors contributing to their cognitive challenges, educators and practitioners can devise more effective strategies to support their learning and development.
Assistance in Preparing Final Reports for Educational Internship Students at SMA Negeri 01 Buay Madang

Assistance in making final reports for educational internship students at SMA Negeri 01 Buay Madang includes several key points. Students who undertake educational internships at these schools have valuable experience in observing, interacting, and contributing to the educational environment. The final report reflects students' in-depth understanding of the learning process, school management, and the dynamics of teacher-student relationships at the high school. In the report, students present an evaluation of learning methods, challenges faced, and recommendations for improvement. Apart from that, student assistance also includes understanding the school curriculum and student involvement in extracurricular activities. In conclusion, the intern students succeeded in integrating educational theory with practice in the field, proving their professional growth and contribution to educational development at SMA Negeri 01 Buay Madang.
Impact of Motivation on Employee Performance of Selected Real Estate Firms in Abuja

This study examined how motivation affected worker performance in selected Abuja real estate companies. The research design employed in the study is a survey and data were collected through the primary source where 66 structured questionnaires were distributed among the staff of five real estate companies in Abuja which include Osakwe &amp; Associates and Wale Adewusi Associate. Data collected were analyzed using multiple regression analysis. The findings showed that employee motivation has a favorable and significant impact on performance in terms of job efficiency in Abuja real estate. Other findings included the fact that job security has a favorable yet negligible effect on the performance in terms of work efficiency in Abuja real estate. Salary and wages also have a positive and large impact on performance in terms of work efficiency in Abuja real estate, as well as rewards, which have a favorable and big impact on both. According to the report, real estate companies in Abuja should keep up their efforts to motivate staff members by guaranteeing their jobs, paying them a living wage, and rewarding them because doing so improves productivity.
Adaptive and Robust Following of 3D Paths by Holonomic Manipulator

This paper addresses the problem of following three‐ dimensional path by holonomic manipulator with para‐ metric or structural uncertainty in the dynamics. De‐ scription of the manipulator relative to a desired three‐ dimensional path was presented. The path is parame‐ terized orthogonally to the Serret‐Frenet frame which is moving along the curve. The adaptive and robust control laws for stationary manipulator which ensures realiza‐ tion of the task were specified. Theoretical considerations are supported by the results of computer simulations con‐ ducted for RTR manipulator.
Skeleton-based Human Activity Classification in Sparse Image Sequences

Research results on human activity classification in video are described, based on initial human skeleton estimation in video frames. Both single person actions and two-person interactions are considered. The initial skeleton data is estimated in selected video frames by OpenPose, HRNet or other dedicated library. Important contributions of presented work are computational steps of skeleton tracking and -refinement, and relational feature extraction from pairs of skeleton joints. It is shown, that this feature engineering significantly increases the classification accuracy. Regarding the final neural network encoder-classifier, two different architectures are designed and tested. The first solution is a lightweight MLP network, implementing the idea of a "mixture of pose experts". Several pose classifiers (experts) are trained independently on different time periods (snapshots) of single-person visual actions (or 2-person interactions), while the final classification is a time-related pooling of weighted expert classifications. All pose experts use the same deep encoding network. The second (middle weight) solution is based on a LSTM network.Both solutions are trained and tested on the action set of the well-known NTU RGB+D dataset, although only 2D data are used.Our results show comparable performance with some of the best reported STM- and CNN-based classifiers for this dataset. We conclude that by reducing the noise of skeleton data, highly successful lightweight- and midweight-approaches to visual activity recognition in image sequences can be achieved.
Multimodal Robot Programming Interface Based On RGB-D Perception and Neural Scene Understanding Modules

In this paper, we propose a system for natural and intuitive interaction with the robot. Its purpose is to allow a person with no specialized knowledge and no training in robot programming to program a robotic arm.We utilize data from the RGB-D camera to segment the scene and detect objects. We also estimate the configuration of the operator's hand and the position of the visual marker to determine the intentions of the operator and the actions of the robot. To this end, we utilize trained neural networks and operations on the input point clouds. Also, voice commands are used to define or trigger the execution of the motion. Finally, we performed a set of experiments to show the properties of the proposed system.
The Development of Teacher Training Curriculum for Organizing of Learning Experiences to Enhance Social Development for Persons with Autism Spectrum Disorder

The present study sought to (1) assess the needs in the development of the teacher training curriculum on providing learning experiences for promoting social development among persons with autism spectrum disorder (ASD), (2) develop the curriculum, and (3) examine its effectiveness. A mixed-method approach and research and development were adopted. The data were collected during July 2022–April 2023. The results are reported: 1. A review of documents, literature and related studies showed that teacher preparation was required for promoting persons with ASD’s social development. Additionally, the survey indicated that the school directors’ primary need for self-improvement was the frequency of attending training programs (PNIModified = 0.54), while that of the teachers was knowledge and understanding of promoting social development (PNIModified = 0.21). Also, the school directors’ strongest need in providing learning experiences was continuous promotion of social development (PNIModified = 0.47), and that of the teachers was implementation of learning activities based on lessons plans (PNIModified = 0.21). 2. The developed curriculum comprised eight components: rationale, objectives of the curriculum, contents, training activities and methods, training materials, durations, venues, and measurement and evaluation. Concerning its quality, the curriculum achieved a high level of suitability (x = 4.38, S.D. = 0.16). 3. After the curriculum implementation, the teachers achieved higher post-training scores (x = 22) than their pre-training scores (x = 17.20) and were most satisfied (x = 4.71, S.D. = 0.08). Provision of learning experiences achieved a mean score of 84.67%.
COVID-19 Collectivism

Abstract This chapter explores how efforts to address COVID-19 in the years to come will require concern for the public good and a willingness to engage in collective activities toward better therapeutics and social supports for those affected. As an example, the chapter cites a slogan used by the Long COVID Alliance, “Nothing about us, without us.”
Can Contact Tracing Work Here?

Abstract The chapter discusses the shortcomings of contact tracing in the United States during the pandemic. The chapter identifies how lack of testing capacity, workforce limitations, and lack of trust in public health services all contributed to these shortcomings. It suggests the importance of addressing these issues to shore up testing infrastructure in the event of future pandemics.
Collective Management of Copyright and Neighbouring Rights: Current Status and Prospects

The article examines the current issues of the institute of collective management of copyright and related rights. The author substantiates the agency nature of legal relations arising from an agreement between copyright holders and collective management organisations. It argues in favour of necessity to extend the institute of collective management to such ways of using musical and musical-dramatic works as their inclusion in a complex object and making them publicly available. The judicial practice according to which allows to compel the user to conclude a remuneration agreement with an accredited collective management organisation is critically evaluated. Some changes to the existing model of participation of collective management organisations in the protection of copyright and related rights are proposed.
Exploration of factors predicting the accuracy rate of the English reading section of the College Scholastic Ability Test using the decision tree model.

The study aimed to identify factors that could affect the accuracy rate of the English section of the College Scholastic Ability Test (CSAT). A total of 45 variables related to the passages and questions that influence the rate of accuracy were included. The decision tree regression model was used to find out the possible variables and analyze the impact of each factor. The decision tree model revealed that the question type had the greatest impact on the accuracy rate of the English reading comprehension test, followed by the points of the question, adjective variation (ADJV), the number of dependent clauses per T-unit (DC_T), the number of clauses per T-unit (C_T), word count (nwords), and topic. In conclusion, this study shows that question-related factors have a greater impact on the accuracy rate than passage-related factors. Furthermore, this study provides implications for teaching and learning related to English reading in the College Scholastic Ability Test.
Informal digital learning of English and L2 willingness to communicate of Korean secondary learners: The mediating role of English as an international language

Informal Digital Learning of English (IDLE) is often associated with Willingness to Communicate in a second language (L2 WTC). This study aims to examine whether English as an International Language (EIL) perceptions mediate the relationship between IDLE and L2 WTC of learners. Survey data was collected from 206 secondary school students in Korea. The questionnaire of IDLE activities asked whether they watch videos in English (Receptive IDLE) and communicate with others in English (Productive IDLE) through social media like YouTube and Instagram. L2 WTC survey asked how participants were willing to speak in English in the classroom. EIL perception scale examined the perception of the status of English, the variety of English speakers’ accents, and communication strategies. The results found that EIL perception partially mediated the relationship between IDLE and L2 WTC. The findings suggest that learners who participate in IDLE activities have more positive EIL perceptions, which leads to a higher level of WTC. The present study has pedagogical implications for teachers to encourage secondary school students to engage more frequently in IDLE activities autonomously which may enhance positive attitudes toward the varieties of English and the level of L2 WTC i n t he c lassroom.
A case study of teaching English poetry through EdTech tools with learner-centered approach: Focusing on fostering pre-service English teachers’ collaborative communication, creative thinking and digital literacy

This study explores the integration of Educational Technology (EdTech) in a learner-centered approach to teaching English poetry, focusing on collaborative communication, creative thinking, and digital literacy, which are the competencies that students need to thrive, outlined by the Ministry of Education of the Republic of Korea. The research involved 17 participants from a “Teaching English Poetry” class. Prior to the midterm, the researcher demonstrated the use of various EdTech tools for teaching English poetry. After the midterm, pre-service English teachers were required to conduct micro-teaching using at least three different EdTech tools. Surveys based on the Technological Pedagogical Content Knowledge (TPACK) framework were used to measure how teacher’s TPACK capabilities affect students’ collaborative communication, creative thinking, and digital literacy. The results indicate a significant improvement in students’ engagement and comprehension of poetry, facilitated by the diverse capabilities of EdTech tools. These tools also promoted effective teamwork and problem-solving, enhancing students’ collaboration competencies. In conclusion, this study underscores the importance of EdTech tools in enriching the teaching and learning of English poetry, particularly in cultivating creativity, collaboration, and digital literacy. By fostering an environment conducive to collaboration and interactive learning, EdTech not only boosts student motivation but also enhances their comprehension and appreciation of English poetry.
Machine learning-based item difficulty prediction model using item text features

This study investigates the difficulty of English reading passages in the College Scholastic Ability Test (CSAT) in South Korea, analyzing corpus linguistic features from 2015 to 2023 using Coh-Metrix software. A notable innovation of this research is the application of the Random Forest algorithm, which overcomes the limitations of traditional regression analysis, such as multicollinearity and overfitting. Focusing on textual complexity, the analysis utilizes 108 explanatory variables to predict the difficulty of CSAT English items. Rigorous methods, including grid search and cross-validation, were employed to ensure the reliability of the results. The Random Forest regression model demonstrated high predictive accuracy. Crucial variables influencing the difficulty of items were identified, providing valuable insights for educators and policymakers to improve CSAT preparation strategies. This research marks a significant advancement in language assessment by combining corpus analysis with machine learning algorithms. It not only enhances the prediction of test difficulty but also supports students and teachers with effective, tailored preparation strategies to address the varied challenges encountered in English public education.
Exploring the chronostratigraphy of a Bronze Age settlement through core drilling

Abstract Core drilling has been used successfully in archaeology for a variety of applications, including mapping stratigraphy across large areas, estimating feature depth and content, understanding site formation processes, as well as complementing information obtained through surface collection and geophysical survey. In this paper, we provide an overview of the first core drilling campaign carried out in 2021 on the outer settlement of the Bronze Age tell from Toboliu in western Romania. The overall aim of this coring program was to gather information regarding the nature of various features within the settlement, with a focus on the anomalies visible in the geomagnetic site plan. Based on 165 drilled cores, a reconstruction of the site’s stratigraphy is presented in this paper. This reconstruction was controlled and confirmed by excavations in one of the coring transects where the stratigraphic interpretation of the cores could be linked with the excavated stratigraphy. Additionally, samples for radiocarbon dating were collected from several cores. Based on this data, we discuss preliminary insights into possible house locations and preservation issues, as well as the chronological development and occupation sequence of this complex site.
Using OSSEs to Evaluate GXS Impact in the Context of International Coordination

Abstract The Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring ofGEOsounders, to improveweather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements, and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the timescales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high temporal resolution atmospheric profile information over much of the western hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts.
A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

To estimate earthquake losses for regional buildings, it is important to consider the varying seismic performances of different types of buildings. To achieve this, a dynamic elastic–plastic analysis based on detailed structural modeling is more accurate than the capacity spectrum method based on a single degree of freedom to obtain the response and damage of various structures under seismic activity and provide a more precise estimate of earthquake losses for buildings. Detailed information about the building facilities is necessary to create a fine structural model. However, obtaining precise actual structural details can be challenging with existing methods, especially when there are a large number of buildings to consider. This paper proposes a new method called story-based random structure (SBRS) modeling to address this issue, which is based on the common and reasonable layout of the structure. The process involves choosing design parameters that can represent the structural arrangement of a building as variables. These parameters include the materials’ type and strength, proportions, the components’ size, and other relevant factors. The values for these variables are determined based on engineering experience and design specifications. The range of values for these variables is also determined to ensure that all design requirements are met. Finally, the Latin Hypercubic Sampling (LHS) method is used to randomly sample and combine the variables, establishing a detailed structural model of the building with structural uncertainty. According to the results of the analysis, this method can simulate the structure and component information of a building by using a probabilistic approach, even without knowing the specific structural design. This method can be based on a small amount of readily available building information and solves the problem of the rapid and refined modeling of a large number of buildings at the regional scale. It also makes it possible to estimate the earthquake loss of regional buildings based on their seismic capacity.
Application of Autonomous Driving Algorithm Based on HSV and Yolov5 in Micro Intelligent Vehicles

Artificial intelligence driving technology has become the main direction of technological progress in intelligent vehicles. To address the issue of road recognition and traffic sign recognition response in the field of intelligent vehicle autonomous driving, a vehicle autonomous driving algorithm based on HSV road detection and yolov5 traffic sign recognition is proposed and applied to micro intelligent vehicles. The hardware of the mini smart car is controlled by NVIDIA Jetson AGX Orin, and the software method is embedded in the Ubuntu system. The yolov5 neural network is used to solve the classification and location problems of traffic signs. The results of experimental testing prove that micro intelligent vehicles can drive according to regulations on simulated roads and accurately recognize road signs, and can make corresponding actions accurately. The research results provide certain reference value for the application and development of vehicle autonomous driving.
Single-loss hash image retrieval method based on improved visual transformer

Deep hashing methods have gained popularity in image retrieval due to their advantages such as low storage requirements and high efficiency. However, existing deep hashing methods for large-scale image retrieval tasks suffer from issues including low discriminative power of binary hash codes, difficult optimization of losses, and low retrieval accuracy. This paper proposes a single-loss hash image retrieval method based on an improved visual transformer to address these issues. The proposed method utilizes a pre-trained Vision Transformer (ViT) on ImageNet as the backbone network, augmented with a hash coding layer to extract image features more comprehensively. Additionally, we design a single learning objective loss function that addresses the discriminative power of hash codes and quantization errors, thereby eliminating the complexity of adjusting various loss weights. Experimental evaluations on ImageNet100, NUS-WIDE, CIFAR10, and MS-COCO datasets demonstrate the superior performance of the proposed method compared to contemporary methods, indicating its adaptability to diverse data.