Reviewerly/exHarmony huggingface dataset:
Design of Portable Clean Water Storage Facilities for Street Vendors

Culinary development in Indonesia is very rapid, it makes the proliferation of street food traders in big cities Indonesia one of them is pecel lele. The street food vendors must provide cutlery and drinking because many buyers eat the dishes in place, therefore there will be activities to wash cutlery and drink. These activities require clean water used for washing, generally the traders only bring a supply of clean water to wash only one or two drums it is very less. Moreover, the placement of drums on the floor making it difficult to remove the Clean Water. The water is used repeatedly so it is not efficient and effective. The purpose of this study is to create a design of clean water storage facilities for street vendors that are used during the activity of washing cutlery and drinking. The method used is qualitative to determine the needs of users by way of interviews and direct observation. The results of the design in the form of clean water storage facilities for street vendors with an emphasis on ergonomics aspects, so that users can be safer and more comfortable during activities. The results of this design is expected to be a reference and reference research and design in similar terms.
Paper Waste Reduction Efforts Through Digitalization

Paper waste reduction efforts through digitalization. Waste is an endless problem to dealt with, through the ages, humankind necessities have escalated, and with the escalation of humankind necessities, the waste produced by humankind has also escalated. The more advanced a civilization, the more waste is produced. Paper waste is one of the wastes. Paper has becoming a basic need for human which is used as a media for communicating and exchanging information. But to produce paper, a large number of trees had to be cut down, alongside of the byproduct waste come out from producing it. Also, the harmful effect of unhappiness of the paper waste which is one of the main causes of the global warming. Therefore, an act of reducing paper waste is needed. Digitalization is one of the options to reduce paper waste. The definition of digitalization is a process to convert printed form, audio form, and video form to digital form. The purpose of digitalization is to aim for efficiency and optimization for storage purpose, security purpose.
CREATING VISUAL ASSETS OF KOREAN VOCABULARY CARD GAME NOLJA

The Korean vocabulary card game Nolja is a game designed to assist the learning process of Korean vocabulary in an interactive way and suitable for women aged 15-25 years. However, as a new game, Nolja has a fatal problem with its design, namely, the placement of illustrations on the player card which makes the game finish too quickly, there are no tools to assess language proficiency on the card, the typeface on the card is less legible, and strokes on the illustration are too thin, Therefore, it is necessary to design a visual asset for the Nolja vocabulary card game. Visual asset design for Nolja includes game theme design, illustration, educational game system, layout, and content arrangement. This design aims to produce a Nolja card game design that is interactive, educational, and suitable for women aged 15-25 years who have the motivation and function to convey Korean language subject matter well. The design is carried out by qualitative methods through interviews with experts and extreme users in the game and learning the Korean language, distributing surveys to 100 target markets. This design is also supported by data collection from literature studies from books, journals, and articles from the Internet. The design produces a visual asset for the Nolja vocabulary card game with a stronger educational game system and a design style that matches the target market's wishes so that it can be well received by the market.&#x0D; &#x0D; Keyword: Korean language, Visual learning media, Flashcards, Picture card games, Visual assets.
Revisiting Facial Key Point Detection—An Efficient Approach Using Deep Neural Networks

Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eyes centre, the eye's inner and outer corners, the mouth centre, and the nose tip from which human emotions and intent can be explained. The focus of our work has been evaluating transfer learning models such as MobileNetV2 and NasNetMobile, including custom CNN architectures. The objective of the research has been to develop efficient deep learning models in terms of model size, parameters, and inference time and to study the effect of augmentation imputation and fine-tuning on these models. It was found that while augmentation techniques produced lower RMSE scores than imputation techniques, they did not affect the inference time. MobileNetV2 architecture produced the lowest RMSE and inference time. Moreover, our results indicate that manually optimized CNN architectures performed similarly to Auto Keras tuned architecture. However, manually optimized architectures yielded better inference time and training curves.
Image watermarking based on remainder value differencing and extended Hamming code

Due to the availability of various photo editing tools, intruders can tamper with an image very easily. So, various watermarking and tamper detection approaches have been proposed by researchers. Basically, tamper detection techniques focus on embedding the watermark, extracting the water mark, and identifying the tampered regions. But it is very important that the tampered pixels should also be corrected. We bring forward an image watermarking technique for tamper detection and correction using remainder value differencing (RVD) and extended Hamming code (EHC). It operates on a pixel group of size 2 × 2. Watermark bits (WBs) are generated from four most significant bits of the pixels in a pixel group by EHC and concealed in four lower bit planes by the principle of RVD. The WBs are extracted at the receiver along with the identification of tampered pixels. The tampered pixels are corrected by the developed correction logic. As the principle of RVD is used, precautions are taken to avoid the fall-off boundary problem. The efficacy of this technique is accessed through various quality metrics. It is noted that it performs better than the existing techniques. The recorded peak signal-to-noise ratio value is 45.49 dB with structural similarity value 0.9889. The tampered pixels are identified and corrected.
A Survey and Classification on Recommendation Systems

In today’s modern world, the data is growing exponentially and the traditional systems are not able to fulfil the user’s requirements. To fulfil the needs of the users, various companies like Amazon, Netflix, etc. are using recommender systems which recommend content or various type of data on the basis of the user’s previous activities and interactions with the system. In the recommender system, mainly three approaches are present, i.e., content-based, collaborative filtering and knowledge-based approaches. Due to their wide applicability, recommender systems have become an area of active research and in this context, this paper furnishes a survey and comparative discussion of existing approaches. The survey draws a conclusion on how different recommendation techniques are cooperating with today’s growing technology trends and also discusses the challenges faced by them.
Implementing Autonomous Navigation on an Omni Wheeled Robot Using 2D LiDAR, Tracking Camera and ROS

This paper demonstrates the implementation and results of autonomous navigation algorithms on an Omni Wheel-based Robot using ROS (Robot Operating System). The basis of this application is autonomous navigation of the robot using Simultaneous Localization and Mapping (SLAM), specifically GMapping and Autonomous Path Planning Algorithms. The actual robot is Arduino-based, equipped with a tracking camera for Odometry data and a 2D LiDAR sensor for laser scan data of the environment. The robot is built on Omni wheels, making it possible to perform holonomic movements. The results of tuning the autonomous algorithms for this holonomic robot are also presented.
Convolutional Gated MLP: Combining Convolutions and gMLP

To the best of our knowledge, this is the first paper to introduce Convolutions to Gated Multi-Layer Perceptron (gMLP) and contributes an implementation of this novel Deep Learning architecture. Google Brain introduced the gMLP in May 2021. Microsoft introduced Convolutions in Vision Transformer (CvT) in Mar 2021. Inspired by both gMLP and CvT, we introduce convolutional layers in gMLP. CvT combined the power of Convolutions and Attention. Our implementation combines the best of Convolutional learning along with spatial gated MLP. Further, the paper visualizes how CgMLP learns. Visualizations show how CgMLP learns from features such as outline of a car. While Attention was the basis of much of recent progress in Deep Learning, gMLP proposed an approach that doesn’t use Attention computation. In Transformer based approaches, a whole lot of Attention matrixes need to be learnt using vast amount of training data. In gMLP, the fine tunning for new tasks can be challenging by transfer learning with smaller datasets. We implement CgMLP and compare it with gMLP on CIFAR dataset. Experimental results explore the power of generalization of CgMLP, while gMLP tends to drastically over-fit the training data. To summarize, the paper contributes a novel Deep Learning architecture and demonstrates the learning mechanism of CgMLP through visualizations, for the first time in literature.
Multitask Learning-Based Simultaneous Facial Gender and Age Recognition with a Weighted Loss Function

Traditionally, researchers train facial gender and age recognition models separately using deep convolutional networks. However, in the real world, it is crucial to build a low-cost and time-efficient multitask learning system that can simultaneously recognize both these tasks. In multitask learning, the synergy among the tasks creates imbalance in the loss functions and influences their individual performances. This imbalance among the task-specific loss functions leads to a drop in accuracy. To overcome this challenge and achieve better performance, we propose a novel weighted sum of loss functions that balances the loss of each task. We train our method for the recognition of gender and age on the publicly available Adience benchmark dataset. Finally, we experiment our method on VGGFace and FaceNet architectures and evaluate on the Adience test set to achieve better performance than previous architectures.
Ensemble of Deep Learning Enabled Tamil Handwritten Character Recognition Model

Recently, digitalization of handwritten characters has become a hot research topic and finds applicability in different domains. At the same time, recognition of Tamil handwritten characters is a tedious task compared to other languages. Therefore, this paper presents a new ensemble deep learning-based Tamil handwritten character recognition (EDL-THCR) model. The EDL-THCR model recognizes and classifies the Tamil handwritten characters. In addition, data preprocessing approach is involved using bilinear interpolation technique to normalize the images. Besides, an ensemble of capsule network (CapsNet) and VGGNet models take place for feature extraction process. Finally, softmax layer is employed to classify the Tamil characters in an effective way. A comprehensive experimental analysis is carried out on benchmark dataset, and the results portrayed the better performance of the EDL-THCR technique.
Visualizing Crime Hotspots by Analysing Online Newspaper Articles

With improvements in technology, India is growing at a fast pace, which has led to a great deal of urbanization. However, instead of reducing, the rate of crime has increased these past couple of years. The general public must be educated on how safe an area is, so that they may take the appropriate actions to protect themselves. Every day, we see many local crimes published in internet news articles, but not everyone has the time to read them all. They contain information that can be used to determine the safety of a location. Thus, in this paper, we propose an end-to-end solution based on Natural Language Processing to inform users of the crime rate in their area. We create a model that analyzes crimes mentioned in local news articles and collects data such as location and incident type. The model uses the concept of Named Entity Recognition to extract the locations and the crime that has occurred. To take advantage of the benefits of transfer learning, we built the model using Google's BERT framework. It was trained on CONELL2003 with custom modifications and was put to the test using real-time data gathered from several online news outlets’ crime pieces. Our model has an F1 score of 83.87% and a validation accuracy of 96%. The information collected via internet was visualized on a heat map using bokeh package. We display metrics such as name of the location, number of crimes occurred in that area and the recent most crime that has occurred which provides a quick overview and benefits our users.
Handwritten Mixed Numerals Classification System

Optical Character Recognition is a growing field in pattern recognition, where we can find lots of work done on different Indic languages such as Tamil, Telugu, Gujarati, Bangla, Kannada, etc. Previous work for OCR based on Indic languages show the need for compound OCR system for Indic languages, which can able to deal with documents having details in multiple languages. The proposed system for the classification of handwritten mixed numerals can overcome the limitations of the existing monolingual system based on OCR. We trained a model to identify numerals of Hindi and Gujarati languages. Here we used the projection profile technique as feature extraction over 22,400 images of numerals to generate a feature set. We tried different classifiers such as Naïve Bayes classifier for multi-class classification, Support Vector Machine, K-Nearest Neighbors, and Multi-Layer Perceptron network over the train and test image datasets having a ratio of 70:30 to evaluate and select the best model with the highest performance. We have successfully achieved an accuracy of 66.94%, 90.78%, 84.61%, and 88.21% using mentioned classifiers for Gujarati-Hindi mixed handwritten numerals.
TPACK and Teachers’ Self-Efficacy: A Systematic Review

Technological Pedagogical Content Knowledge (TPACK) studies have surged over the past few years, however, there is a lack of studies that have comprehensively reviewed and synthesized data on teachers’ TPACK self-efficacy. The present review aimed to provide data on research methods, study samples, subject domains, and evaluation approaches used in the TPACK studies to date. The review also aimed to analyze teachers’ TPACK self-efficacy, self-efficacy beliefs, computer self-efficacy, and technology support concerning professional development. Five best bets (most searched databases) were selected on the Electronic Business Source Complete (EBSCO) host platform. An abstract level screening was conducted for 136 peer-reviewed articles, and 75 articles were selected for the detailed screening. The analyses were focused on year-wide appearance of TPACK studies, research methods, study samples, subject domains, and evaluation approaches used. The growth and development of TPACK self-efficacy was examined using the narrative approach. Results indicated that professional development interventions were effective in improving teachers’ TPACK self-efficacy. Also, TPACK-based argumentation practices helped participants strengthen their perceptions toward the integration of technology in classrooms. The implications of the findings for teacher preparation programs and other professional development activities were presented.
A Note on the Dependence Structure of Hierarchical Completely Random Measures

Hierarchical models offer a principled framework to make inference and predictions on different (groups of) observations by leveraging their common features. In a nonparametric setting, the borrowing of information is controlled by the dependence structure induced on a vector of random measures. Two different hierarchical specifications are now well-established in the literature: we compare their dependence structures, provide some intuition on how to enhance their flexibility, and highlight a possibly misleading behaviour of their pairwise covariance. This note is based on some recent results in [3].
Qualitopix: Artificial intelligence-based quantitative quality assurance of immunohistochemistry staining-The Henry Ford Health experience

Abstract Introduction/Objective Immunohistochemistry (IHC) offers crucial patient data for diagnosis, prognosis, and treatment. Pathologists currently assess stain quality subjectively, comparing control sections to patient tissue. Qualitopix (Visiopharm, Denmark) is a cloud-based platform that enables objective, quantitative analysis of staining consistency through external cell line controls. Methods/Case Report Over six months, we tested 1121 slides from five cell-line blocks with epitopes of different intensities for ER, PR, Ki-67, Her-2 Neu, and PD-L1. Slides were stained using Ventana Benchmark Ultra (Roche, Basel, Switzerland) for ER, PR, and Her-2 and Dako Omnis (Agilent, Santa Clara, California, United states) for Ki-67 and PD-L1. They were scanned with DP 200 scanner (Roche) and uploaded to Qualitopix for image analysis. Cells were detected using artificial intelligence and classified based on diaminobenzidine (DAB) staining intensity, reported as H-scores (0-100). Outliers were determined at 1 standard deviation. a selection of outliers was rescanned. Inter- scanner comparisons and repeatability studies were performed for ER and 50 within and between the DP 200 and HT scanners (Roche). Results (if a Case Study enter NA) Our analysis revealed a 26-34% rate of outliers at 1 SD for the five stains, 10 times higher than the currently reported number from our lab. Most outliers (70-100%) remained out of range at rescanning, suggesting pre-analytical technical issues. 5-33% of slides failed analysis due to technical errors. Poor discrimination between sequential cores of varying intensity for ER and Ki-67 was noted. Inter-scanner comparison and repeatability studies demonstrated precise and consistent H-scores within and across both scanning platforms for ER and PR. Conclusion Qualitopix offers automated, objective, and quantitative quality assurance for IHC stain quality assessment, a hitherto manual, subjective and qualitative-driven process. Our findings emphasize the need for continuous IHC quality monitoring. The origin of outliers, technical failures, and their correlation with pathologist's subjective findings require further investigation.
Resources for Developing Your A.I. Literacy

This resource review includes materials designed to help you deepen your understanding of A.I. technologies and consider how these technologies can be leveraged to both mitigate your workload and support student learning.
GERAÇÃO DE SÉRIES SINTÉTICAS DE IRRADIAÇÃO DIÁRIA PARA AS CONDIÇÕES CLIMÁTICAS BRASILEIRAS

A basic step for design and performance simulation of solar generation systems is to obtain climate data for the site of interest. The simulation of photovoltaic systems, for instance, often makes use of data in the hourly temporal resolution when the goal is to compute the energy generated by the plant. The procedure commonly used to solve the problem of lack of data is the use of some “chain methods” in order to generate synthetic series of solar irradiation in the timescale required. Most of the studies presented in the literature use an empirical approach to calibration of models from local measurement data, generally from Europe and United States. Thus, the work presented here aimed to adapt one of these models to Brazilian climate with focus on the initial stage of generation of synthetic data of solar radiation, when average monthly series are converted into series in the daily scale. Local measurement allowed to propose six different models, one national and five regional. The performance of each of the six proposed models was evaluated against popular models available in literature using metrics to compare probability density functions and sequential characteristics of the daily series. Regional models showed superior performance and its direct application or even improvement using more advanced data clustering methods is encouraged.
COMPARAÇÃO ENTRE OS MÉTODOS DE MEDIDA DA IRRADIÂNCIA SOLAR DIFUSA (ANEL E DISCO) PARA DIFERENTES COBERTURAS DE CÉU

The objective was to verify the performance of shading ring and disk measuring methods of diffuse solar radiation in relation to the method of difference (reference) for five different sky coverage: all sky conditions, cloudy sky, partly cloudy skies, partially open and open. The Solar Radiometry Laboratory Botucatu-UNESP provided a 1998 database of global, diffuse and direct irradiances. The performance verification was carried out of the linear regression analysis and deviations between the ring and disk measurement methods relative to the reference method. The results show that the shading ring method, besides the geometrical corrections, require additional numerical corrections to compensate for the circunsolar radiation blocked by the ring. The disk method showed better performance with differences below 2.5%.
SOLARMAP

The photovoltaic solar energy sector in Brazil is on a path of searching for technical and commercial information to provide knowledge and data that is easily accessible to actors in order to conduct more assertive decision-making. In view of this, this article aims to present a facility created for the photovoltaic solar energy scenario in Brazil through the methodology of a technical-commercial content platform. Based on the roadmap methodology, the proposed platform assists the development of the specific sector. To this end, the idea of SOLARMAP was created, which is configured as an analytical platform of the photovoltaic solar energy sector in Brazil integrated by a database updated daily with information on the generation of photovoltaic energy distributed in Brazil and that facilitates the essential and infographic consultation of information specific to the theme examined by the user of the system. SOLARMAP covers the online tool and the quarterly publication of industry reports based on scientific articles and publications. The sector is promoted from the dissemination of this information, thus, the guarantee of technical-commercial information occurs, consequently, increases the level of satisfaction of the agents involved and contributes to the development of competitiveness.