Reviewerly/exHarmony huggingface dataset:
Computer visual syndrome in medical students in virtual education of a Peruvian university during 2021

Introduction: During virtual classes in the context of COVID-19, students were exposed to digital screens for many hours, so they could present computer vision syndrome. Objective: To determine the frecuency of computer vision syndrome in sixth-year students of the Faculty of Human Medicine of the Ricardo Palma University in the context of virtual education due to COVID-19 during the period October - November 2021. Results: Most of the students were young adults (54%) and female (60%). The frequency of computer visual syndrome was 93%, it occurred in 94% of women and 90% of men. Most of the students reported having myopia (44%) and astigmatism (22%). Regarding visual symptoms, the students mainly presented tearing (7.9%), itching (7.6%), and headache (7.6%). Conclusions: In the present study, a high frequency of computer visual syndrome was found in medical students who took virtual classes.
Identifying Discourse Elements in Writing by Longformer for NER Token Classification

Current automatic writing feedback systems cannot distinguish between different discourse elements in students' writing. This is a problem because, without this ability, the guidance provided by these systems is too general for what students want to achieve on arrival. This is cause for concern because automated writing feedback systems are a great tool for combating student writing declines. According to the National Assessment of Educational Progress, less than 30 percent of high school graduates are gifted writers. If we can improve the automatic writing feedback system, we can improve the quality of student writing and stop the decline of skilled writers among students. Solutions to this problem have been proposed, the most popular being the fine-tuning of bidirectional encoder representations from Transformers models that recognize various utterance elements in student written assignments. However, these methods have their drawbacks. For example, these methods do not compare the strengths and weaknesses of different models, and these solutions encourage training models over sequences (sentences) rather than entire articles. In this article, I'm redesigning the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements corpus so that models can be trained for the entire article, and I've included Transformers, the Long Document Transformer's bidirectional encoder representation, and the Generative Improving a pre trained Transformer 2 model for utterance classification in the context of a named entity recognition token classification problem. Overall, the bi-directional encoder representation of the Transformers model railway using my sequence-merging preprocessing method outperforms the standard model by 17% and 41% in overall accuracy. I also found that the Long Document Transformer model performed the best in utterance classification with an overall f-1 score of 54%. However, the increase in validation loss from 0.54 to 0.79 indicates that the model is overfitting. Some improvements can still be made due to model overfittings, such as B. Implementation of early stopping techniques and further examples of rare utterance elements during training.
Applying “Designerly Framing” to Understand Assisted Feeding as Social Aesthetic Bodily Experiences

What could it mean to take a designerly perspective of a robotic eating aid to get a more holistic understanding of meals as social and embodied experiences? In this article, we provide a new perspective of bodily experiences of assisted feeding. We apply “designerly framing” in the context of meals and Human Robot Interaction (HRI) and contribute with insights for researchers with backgrounds other than design into how “designerly framing” can foreground social and aesthetic use. The study focuses on experiences of assisted feeding of five people with impairments in their arms or hands. All of the subjects have long-term experience of meal assistance, and four also have experience of using a robotic eating aid. The data collection comprises seven interview sessions held in peoples homes, a functional analysis of the meal experience, and a workshop held at a design agency. The “designerly framing” is also supported by a theoretical framework describing different types of use to open the meal as a design space. This complements and extends existing knowledge on acceptance and abandonment of assistive technology (AT) and assistive robotics for the meal.
General English Lecturer Professionalism (A Case Study at USN Kolaka)

This study aimed to describe the reflection of the professionalism of general English lecturers at USN Kolaka. This reflection was very important as the first gateway for evaluating the quality of general English learning at USN Kolaka. The study was motivated by the university’s low state of general English learning. For this purpose, this study used a qualitative descriptive method. Data were collected through questionnaires and in-depth interviews with 8 (eight) general English lecturers at USN Kolaka. The results of the questionnaire showed that the 8 (eight) lecturers reflected on their professionalism at a high level, In contrast, the results of the interview confirmation showed that every professional indicator has been carried out but not optimally. Thus, it was concluded that there was an effort by the general English lecturer at USN Kolaka to improve their professionalism. This study recommends follow-up support from the university to increase the number of lecturers in terms of good governance management process for lectures welfare.
Teaching and learning with innovative technologies and practices at primary school level.

The introduction of computer science to primary schooling age is relatively new, as traditionally it was primarily set aside for secondary and tertiary level learning (Heintz et al., 2016). Experts agree that even young children can understand fundamental concepts of computational thinking (CT), and that it is important to develop skills related to CT from a young age (Boccini et. al, 2016, p.48).&#x0D; &#x0D; Increasingly computer science is becoming a compulsory area of curriculum for many countries across the world, as reported by Bocconi et al. and there has been a recent increase in the integration of CT and computer science in mandatory education, as evidenced by the recent changes in educational curricula (p9., 2016). In New Zealand, the Technology curriculum was recently refreshed with the main revisions being the addition of CT and designing and developing digital outcomes as technological areas (Ministry of Education, 2017a). The intention of digital technologies curriculum content is to “significantly contribute to students developing the knowledge and skill they need as digital citizens and as users of digital technologies across the curriculum” (Ministry of Education, 2017b, p.3).&#x0D; &#x0D; There is also an expectation that all teachers are responsible for building capacity in digital fluency and literacy. It is the teacher's responsibility to effectively use these tools, and to in turn educate students on how to take advantage of these tools for their learning (Wright, 2010, p.46). The main rationale for introducing CT in many countries is to promote the development of 21st century skills necessary for full engagement in the digital realm (Bocconi et al., 2016, p.8).&#x0D; &#x0D; ByteEd, a New Zealand based educational resource company, have recently developed a new approach to the teaching of computer science at a primary school level that incorporates 21st century skill development. The Play Code Learn series of STEM (Science, Technology, Engineering and Mathematics) kits utilise an unplugged-to-digital methodology and explore future-focused technologies of Augmented Reality (AR) and programming.&#x0D; &#x0D; Based on the research of Bell and Vahrenhold (2018), who state unplugged activities for students engage them with lasting ideas in computer science. Integrating physical digital tasks along with unplugged tasks proves to be more beneficial for learning. The kits enable students to learn and understand digital concepts before transitioning to putting skills and knowledge into action in a digital environment.&#x0D; &#x0D; This presentation delves into the impact of the first Play Code Learn kit, Dinosaur Steps, on teaching and learning in two New Zealand classrooms. The use of an unplugged approach has proven to be advantageous to learners and highlights a significant shift in knowledge retention and the understanding of concepts, skills and literacy after using the Dinosaur Steps kit and related teaching resources during Term 4 2022.
Event-Based Image Deblurring with Dynamic Motion Awareness

Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself. Complementary information from auxiliary sensors such event sensors are being explored to address these limitations. The latter can record changes in a logarithmic intensity asynchronously, called events, with high temporal resolution and high dynamic range. Current event-based deblurring methods combine the blurry image with events to jointly estimate per-pixel motion and the deblur operator. In this paper, we argue that a divide-and-conquer approach is more suitable for this task. To this end, we propose to use modulated deformable convolutions, whose kernel offsets and modulation masks are dynamically estimated from events to encode the motion in the scene, while the deblur operator is learned from the combination of blurry image and corresponding events. Furthermore, we employ a coarse-to-fine multi-scale reconstruction approach to cope with the inherent sparsity of events in low contrast regions. Importantly, we introduce the first dataset containing pairs of real RGB blur images and related events during the exposure time. Our results show better overall robustness when using events, with improvements in PSNR by up to 1.57 dB on synthetic data and 1.08 dB on real event data.
A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION

Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively.
Expert Validation of a Python Test, Reliability, Difficulty and Discrimination Indices

In recent years, different countries have implemented the teaching of computer programming from the first grade in schools, with the aim of incorporating computational thinking as a new way of thinking that is a necessary skill for scientific and technological development, a fundamental axis for development in the 21st century. Recent reviews of the state of the art have shown that this task is only being carried out by countries that have given space to technology and science, incorporating it at the elementary school level. However, developing countries do not yet consider the significance of the issue, so to date they have not taken the necessary steps in this direction. In addition, learning computer programming is fundamental for countries to join technological development, so that they can be creators of technology and not just users of it. The problem is that there is no didactic development for the teaching of programming, nor validated evaluation instruments to quantify the learning of computer programming. The objective of this research is to validate a programming test that evaluates technical skills in the Python programming language. The instrument proposed to be validated is a 90-item test, which after validation by experts was reduced to 70 items, and after the psychometric analysis that considered the calculation of reliability, difficulty and discrimination indexes, resulted in the proposed 45-item instrument, as a standard instrument for the evaluation of learning in the Python programming language. It should be noted that the validation methodology was carried out using the classical theory of psychometric analysis.
Comparison of Apriori, Apriori-TID and FP-Growth Algorithms in Market Basket Analysis at Grocery Stores

Market Basket Analysis is an analysis of consumer behavior specifically from a certain group/group. Market Basket Analysis is generally used as a starting point for seeking knowledge from a data transaction when we do not know what specific pattern we are looking for. Market Basket Analysis in this study is applied to the search for patterns of purchasing groceries at grocery stores and then analyzed by season. This study aims to compare the Apriori, Apriori TID and FP-Growth methods in determining consumer transaction behavior and calculating the quantity of consumer transactions in several seasons based on data obtained from the Market Basket Analysis database. In the results of this study, it is known that FP-Growth has the best performance among the other two algorithms, but uses more memory than other algorithms. The Apriori-TID algorithm uses lighter and faster memory than the Apriori Algorithm
Tour Experience with Interactive Map Simulation based on Mobile Augmented Reality for Tourist Attractions in Banjarmasin City

Tourist attractions are one of the attractions in the tourism sector, especially in the city of Banjarmasin. Along with the progress of the tourism sector, it is necessary to develop, provide facilities, management, and promotion. The purpose of this research is to build an application to identify tourist attractions in the City of Banjarmasin using Augmented Reality (AR) visualization by creating an interactive map simulation as a marker that will bring up three-dimensional objects, the application is called AR-AyoKeBanjarmasin. The method used in this research consists of Extreme Programming (XP) as a method in the software development process, Marker-Based Tracking as the main method in pattern detection techniques in marker images that will display three-dimensional objects that have been programmed in AR applications, A/B Testing method is used to select the interface design expected by potential users, Black- Box Testing as a method of testing application functionality, and User-Experience Questionnaire (UEQ) as a technique and instrument to evaluate User Experience (UX) when using the AR application. The results show that the design chosen for the user interface is design A so that it can be continued to the coding stage. Functional testing gives the result that all features in the application have functioned as expected and non-functionally involving 50 respondents using a UX approach stating that the application has implemented the user convenience aspect with an average value of 2.93 with the predicate of Excellent. So that the AR-AyoKeBanjarmasin application can be recommended for use by tourists
Usability Evaluation in Knowledge Management System (KMS) Using System Usability Scale (SUS) Method

An unattractive system interface and the lack of user motivation in using the Knowledge Management System (KMS) is one of the reason why usability testing is needed. Usability testing aims to determine the usability of the system, whether the system is in accordance with the purpose of its application. Usability Testing will uses System Usability Scale (SUS) method which involves users in the test. The results of this study are usability scores and recommendations for improvement that have been developed using the prototyping method based on the results of problem identification. For the problem identification, researcher will uses interview technique with 5whys method. After all, the score results is 80.25 for prototype of system improvement. This research can be used as a reference in the development of the system later
New blender-based augmentation method with quantitative evaluation of CNNs for hand gesture recognition

&lt;p&gt;In this study, we extensively analyze and evaluate the performance of recent deep neural networks (DNNs) for hand gesture recognition and static gestures in particular. To this end, we captured an unconstrained hand dataset with complex appearances, shapes, scales, backgrounds, and viewpoints. We then deployed some new trending convolution neuron networks (CNNs) for gesture classification. We arrived at three major conclusions: i) DenseNet121 architecture is the best recognition rate through almost evaluated red, green, blue (RGB) and augmentation datasets. Its performance is outstanding in most original works; ii) blender-based augmentation help to significantly increase 9% of accuracy, compared to the use of a RGB cues; iii) most CNNs can achieve impressive results at 97% accuracy when the training and testing datasets come from the same lab-based or constrained environment. Their performance is drastically reduced when dealing with gestures collected in unconstrained environments. In particular, we validated the best CNN on a new unconstrained dataset. We observed a significant reduction with an accuracy of only 74.55%. This performance can be improved up to 80.59% by strategies such as blender-based and/or GAN-based data augmentations to obtain an acceptable result of 83.17%. These findings contribute crucial factors and make fruitful recommendations for the development of a robust hand-based interface in practice&lt;/p&gt;
Rainfall prediction model in Semarang City using machine learning

The erratic distribution of rainfall greatly affects people's daily activities, especially in Semarang City, so it is necessary to predict rainfall. Correct prediction of rainfall can improve community preparedness in dealing with natural disasters. Algorithms for machine learning and data mining have been extensively utilized in research involving rainfall data from various regions. The primary objectives of this study are to find the best regression algorithm and use machine learning algorithms to predict rainfall in Semarang. The dataset used is daily rainfall data for the City of Semarang from the meteorological, climatological, and geophysical agency (BMKG). Machine learning algorithms such as multiple linear regression, random forest regression, and replicated neural networks will be used to conduct regression analysis on this dataset. The mean absolute error and Root mean squared error techniques are utilized to evaluate the performance of machine learning algorithms. With an error rate of 13.055 for root mean squared error (RMSE) and 6.621 for mean absolute error (MAE), the results of the research indicate that the performance of the neural network algorithm is superior to that of other algorithms.
Adoption of Blockchain Technology in Pharmaceutical Supply Chain to fight Counterfeit Medicine Market

Blockchain Technology (BT) is recognized as a technology with multiple applications in many industries due to the nature of its utilization. In the pharmaceutical industry, an effective supply chain is crucial to mitigate the counterfeit medicine market, which is currently a big challenge, especially for low or middle-income countries. This research investigates the possibility of implementing blockchain technology solutions in Pakistan’s pharmaceutical industry by introducing blockchain Application Software. The pharmaceutical industry and General Practitioners from hospitals and clinics were interviewed in the public and private sectors to understand this possibility. Data coding was conducted on the data gathered from the interviews, and from these codes, categories were extracted, these categories were then further refined to extract the final themes. Finally, the paper discusses that based on the need of the current situation, the proposed BT system would present a solution for making the pharmaceutical supply chain more transparent.
Parallax-Tolerant Unsupervised Deep Image Stitching

Traditional image stitching approaches tend to leverage increasingly complex geometric features (point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome the adverse conditions by adaptively learning robust semantic features, but they cannot handle large-parallax cases due to homography-based registration. To solve these issues, we propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. It provides accurate alignment for overlapping regions and shape preservation for non-overlapping regions by joint optimization concerning alignment and distortion. Subsequently, to improve the generalization capability, we design a simple but effective iterative strategy to enhance the warp adaption in cross-dataset and cross-resolution applications. Finally, to further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks. Compared with existing methods, our solution is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes. Extensive experiments show our superiority over the SoTA methods, both quantitatively and qualitatively. The code is available at https://github.com/nie-lang/UDIS2.
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset &amp; split generation for autonomous vehicles

Autonomous driving (AD) perception today relies heavily on deep learning based architectures requiring large scale annotated datasets with their associated costs for curation and annotation. The 3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain, including rural, urban, industrial sites and universities from 13 countries. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds. We also propose a novel method for sequential dataset split generation based on iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU improvement over the original split proposed by SemanticKITTI dataset. A complete benchmark for semantic segmentation task was performed, with state of the art methods. Finally, we demonstrate an Active Learning (AL) based dataset distillation framework. We introduce a novel heuristic-free sampling method called ego-pose distance based sampling in the context of AL. A detailed presentation on the dataset is available here https://www.youtube.com/watch?v=5m6ALIs-s20.