Reviewerly/exHarmony huggingface dataset:
Literary Teaching Based on Information and Communication Technology (ICT): An Inquiry Approach

This research aims to describe the management of ICT-based literary teaching with an inquiry approach at the Indonesian Language and Literature Education (PBSI) Study Programs at Universitas Islam Negeri Syarif Hidayatullah and Muhammadiyah University, both in Jakarta, Indonesia. This research used qualitative methods to study the problems, including interviews and direct observation techniques with visual documentation. The data were analyzed using qualitative interactive model techniques. The results reveal that in lesson planning the use of ICT and the inquiry approach is not discussed in detail even though their use is expected. In organizing learning, the categories of lecture content follow the RPS content, there is a grouping of students for discussion on a theme which is then presented. In the implementation, ICT-based learning is conducted with zoom meetings and assisted by WhatsApp group media. The application of the inquiry approach is conducted by group discussion on a theme which is then discussed. Lecturers play a role in guiding and directing students in conducting the steps of the inquiry approach. In the assessment stage, it is conducted with an authentic assessment-based assessment, performance assessment and written assessment.
WEB BASED POLICE COMPLAINT MANAGEMENT SYSTEM

Filing and managing complaints can be a time-consuming and frustrating process for individuals, organizations, and businesses.To address this issue, online complaint registration forms have become increasingly popular to simplify the complaint filing process.This paper presents the development of an online complaints registration form that provides users with an easy and efficient way to file complaints.The form is designed to be user-friendly and accessible, allowing users to file complaints easily and quickly, from anywhere and at any time.The portal includes a face recognition module that uses advanced machine learning algorithms, such as the Random Forest Classifier and Support Vector Machines, to identify potential criminals or suspects accurately and efficiently.The system includes features for tracking and monitoring complaints, assigning complaints to appropriate personnel, and managing the resolution process.The effectiveness of the system is evaluated through a user satisfaction survey, which shows that the majority of users find the form to be useful, easy to use, and efficient.The results indicate that the online complaints registration form has the potential to improve the efficiency and effectiveness of complaint management systems for various organizations and businesses.
Handwritten Devnagari Character and Joint Devnagari Character Recognition Using Deep Learning

Abstract: In our day to day life we use devnagari to communicate with each other verbally. There are many people in our country who still like to write their documents in devnagari only. In our project we recognizing devnagari as well as joint devnagari characters. The character images in our dataset are imposed by joint characters, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of joint devnagari character image samples are carried out by using one of the deep convolution neural networks known as CNN. The handwritten datasets is collected artificially from users in the age range of 18–21, 22–25, and 26–30. It consists of joint devnagari text that are used to evaluate the experiment's performance. The datasets are comprised of many classes. Those classes include devnagari characters, devnagari digits as well as joint devnagari characters. After performing essential steps. It is observed that the performance of CNN Classifiers like Random Forest is overall high. An overall accuracy of 94% is achieved during the recognition of devnagari character set and an accuracy of over 90% is accomplished with respect to handwritten data samples with training and testing proportions of 70% and 30% in both of the cases for the number of classes of over 58
Spam SMS Prediction Using Machine Learning

Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this project, a dataset of real SMS Spams from UCI Machine Learning repository is used, and after pre-processing and vectorization, different machine learning algorithms are applied to the dataset. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced and converted into an open-source website. The SMS spam collection set is used for testing the method. After collecting the various supervised learning algorithms, we find that the Multinomial Naïve Bayes algorithm gives us 97.1% Accuracy and 100% Precision
Log-linear modeling between risk factors and interactions for human papillomaviruses infection and papanicolaou smear abnormalities

Abstract Modeling qualitative variables and their interactions often require multidimensional analysis through Log-linear models. Furthermore, these models are useful as alternatives in fields where probabilistic classification is required, such as speech recognition or pattern classification. This work uses log-linear modeling as a methodological approach to the analysis of 1114 valid cases of women participating in a human papillomavirus infection and cervical cancer screening program, thus relating a public health problem to biophysical knowledge. The objective of the study was to evaluate the main effects and interactions between the variables compared to the independence model. A backward stepwise selection with a 5% probability of elimination was performed to arrive at the best hierarchical model starting on the covariates that were significant in a previous bivariate analysis. This allows us to understand how biophysical process modeling can identify biomarkers and propose prevention methods for human papillomavirus infection and Papanicolaou smear abnormalities.
Optimization of college physical education curriculum based on G-DINA

Abstract Physical education curriculum in colleges and universities has been paid more and more attention and turned one of the important contents of education curriculum. In order to make up for the deficiency that traditional tests can only get a general score and further optimize the physical education curriculum, this study selected G-DINA model according to Wald statistic, analyzed the physical education curriculum textbooks, and clarified the cognitive attributes and hierarchical relations of the curriculum. Then, pursuant to the attributes and hierarchical relations obtained, this study constructed typical assessment model matrix and developed the curriculum cognitive diagnosis test paper based on Q matrix. Through comparing results of the two physical education curriculum diagnosis test papers based on G-DINA model, it can be seen that the ratio of students who master the attributes A4 and A5 is climbed up to 67.2% and 59%, respectively, which indicates that students’ overall mastery of A4 and A5 knowledge blocks has been significantly improved after teachers’ intensive and in-depth optimized teaching. Therefore, it can be concluded from the above that the optimized teaching of physical education courses in colleges and universities plays a significant role, and at the same time, for teachers, G-DINA model is conductive to fully controlling the teaching feedback effect, timely adjusting the teaching key and difficult points, and predicting the depth and breadth of teaching trend. Even further, the model is helpful for college physical education institutes to deepen the physical education curriculum optimization in colleges and universities.
EXPERT SYSTEM-BASED GASTROENTERITIS DIAGNOSIS USING THE FUZZY METHOD AND CERTAINTY FACTOR

Gastroenteritis is inflammation of the stomach and intestines with symptoms of diarrhea and often fever. The disease in question is repeated defecation, more than four times, and the type of liquid stool, which can be accompanied by blood or mucus. Digestive disorders this disease occurs due to viruses, bacteria, or parasites. This disease is often underestimated because the symptoms are almost the same as diarrhea, so if it is not handled properly, it can endanger the patient's life, especially with conditions that are getting weaker and worse. This study aims to create an expert system application program that can diagnose Gastroenteritis, the results of which can show the disease suffered by the patient. 16 symptom data will be used, namely Fever above 38C, Chills, Headache, Nausea, Lack of appetite, Easy Drowsiness, Constant thirst, Watery stools, Discomfort, Stomach cramps/bloating, Dehydration, Weight loss, Diarrhea with blood, restlessness, weakness, no urine in the diaper for 3 hours or more. To demonstrate its confidence percentage, the authors chose the Fuzzy Logic and Certainty Factor methods as the basis for calculating the archival samples from the clinic with 100% confidence percentage. Based on the expert system trials and the resulting Certainty Factor values, it can be concluded that the expert system that has been created makes it easier to determine Gastroenteritis so that it can be handled properly and correctly.
Measurement of Objective Video Quality in Social Cloud using Machine Learning

Abstract: This paper focuses on the objective of video quality analysis (VQA) and proposes a deep learning-based approach to measure quality degradation. The study involves conducting experiments on various social clouds (SCs) and low-quality videos. Specifically, the selected videos are uploaded to SCs to evaluate the differences in video service and quality. The average of all videos, denoted as Avg 100, is used to measure the quality, while the peak signal-to-noise ratio (PSNR) is found to have no significant impact on other indicators. By utilizing deep learning techniques, this research aims to provide optimal video quality and multimedia services that meet the standards of Quality of Service (QoS) and enhance the overall Quality of Experience (QoE) for users
The Growth of Bacillus cereus (IHB B 379) Isolates at Different Temperature and Concentration of Mercury Chloride (HgCl2)

&#x0D; &#x0D; &#x0D; Bacillus cereus (IHB B 379) has been isolated from the sediments of the illegal gold mining area located in Danau Biru Singkawang, these bacteria have resistant properties by being able to grow on media containing HgCl2 with a concentration of 500 ppm. This study aimed to determine the effect of temperature and concentration of mercuric chloride (HgCl2) on the growth of Bacillus cereus (IHB B 379) and to determine the temperature and concentration of mercuric chloride (HgCl2) on the optimum growth of Bacillus cereus (IHB B 379). The study used a Completely Randomized Factorial Design (CRD) consisting of two treatment factors. The first factor was a different temperature treatment with three levels, namely 320C, 370C, and 420C. The second factor was the treatment of the mercuric chloride (HgCl2) concentration with three levels, namely 0 ppm, 250 ppm, and 500 ppm. The Bacillus cereus (IHB B 379) growth test used the delusional method and the number of colonies was calculated using the Total Plate Count (TPC) method. The results showed that the growth of Bacillus cereus (IHB B 379) was seriously affected by the temperature and concentration of HgCl2 . The best growth pattern of Bacillus cereus (IHB B 379) was occurred at 320C and concentration HgCl2 250 ppm. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
I learned it through the hologram

Holography is an advanced coherent imaging technique that records and reproduces 3D images using the principles of interference and diffraction. It finds numerous applications in fields such as biomedicine, engineering, art, and microscopy. Teaching holography at university provides students with a unique opportunity to learn more about optical physics. Moreover, it fosters creativity and innovation, as students can explore the potential of Digital Holography for their own future research.
A Study of Perceived Reading Difficulties and Reading Strategies: The Case for Online Reading

Reading is never considered a simple activity that requires readers to only identify words written on a page or screen.It requires mental activities to comprehend their meaning and recognize relationships between words and ideas.There are many factors that may affect reading comprehension including readers' background knowledge on the text they read, vocabulary, motivation and text complexity.This study is conducted to explore learners' perception on their reading difficulties, their use of reading strategies and the relationship between them in the case of online reading.This quantitative research collected data from 275 random respondents from various faculties in a public university.The questionnaire has 4 sections that contain items on demographic profile, reading difficulties, global strategies, problem-solving strategies and support strategies.In general, the findings show that there is a high significant association between global reading strategies and readers problem-solving strategies as well as support strategies.However, the study found the perceived difficulties experienced by the learners have no significance to the reading strategies that they use.Nevertheless, the teaching of such strategies should be incorporated in the reading instruction.
How expired domain names of scientific journals can be a goldmine

The article is about how expired domain names of scientific journals can be a goldmine. Some people buy expired domain names of scientific journals and then create fake versions of the journals.
The Influence of Physiological Learning Styles on the Completion of an Online Task in a Law Degree

Advances in educational technology offer many opportunities in the English for Specific Purposes (ESP) classroom: the development of students’ motivation and autonomy; the integration and evolution of Task-Based Language Teaching (TBLT) with the emergence of different types of online learning tasks; or the concern of students’ Perceptual Learning Styles in multimodal environments. This study investigates new generations’ perceptual learning preferences and how digital tendencies may influence students’ academic achievement after the implementation of an online task (i.e., ‘Lesson’).&#x0D; An experimental (n=150) and a control group (n=21), from the first year of the bachelor’s degree in Law at a Spanish university participated in this study. Four stages were followed: (1) ‘Perceptual Learning Styles’ Questionnaire’ completion, (2) ‘Lesson’ performance, (3) online exam implementation, and (4) data analysis. After the intervention, results indicate the presence of the ‘Multimodal’ learning style and high levels of academic achievement of multimodal students.
Methods of introducing additive technologies into the educational process in the training of future graphic designers

Additive technologies provide many opportunities for improving activities in various fields, but the specifics of exploring their work and application in the educational process of students enrolled in design education programmes have been understudied. The purpose of this study is to develop methods of implementing additive technologies in the educational process of training future graphic designers and to test their effectiveness. To achieve this purpose, the research methodology used was a mixed-methods study. The study involved students and teachers of Khortytsia National Academy. Through video conferencing and discussions between teachers, the implementation of the “Additive Technologies” course in the educational process (study of 3D printing and modelling) and ways to use additive technologies in other subjects were developed and agreed upon. It was established that before the experiment, students had virtually no experience in learning or working with additive technologies, which was the foundation for the course curriculum. The students’ assessment of the quality of learning on the scale “The instructional materials motivation survey” before and after the experiment was examined. A questionnaire has been developed to explore students' opinions on the effectiveness of using additive technologies in the work of a graphic designer, and their impressions of the methods of introducing additive technologies into the educational process. The survey found that the majority of students highly appreciated the impact of using additive technologies in the work of a graphic designer and believe that their study is necessary for effective learning and work in the future. In addition, students highly appreciated the teachers’ ability to use additive technologies in the learning process. The methods of introducing additive technologies proposed in the study can be used in the training of higher education students in the field of 02 – Art, considering the specifics of the curriculum. In addition, there are prospects for using additive technologies to perform design work, internships using additive technologies, and defending qualification projects with real-time object manufacturing.
IMPLEMENTASI DIGITALISASI REKAM MEDIS UNTUK MENDUKUNG REKAM MEDIS ELEKTRONIK

The development of the digital era has highlighted the importance of integrating routine and high-quality data as part of the digital transformation process. One crucial data component that needs to be maintained is the Electronic Medical Record (EMR). Puskesmas Baloi Permai is currently piloting the implementation of EMR and requires technical assistance in digitizing paper-based medical records into electronic form. Digitization is necessary to ensure the continuity of patients' medical history information. This effort aims to support the successful implementation of EMR at Puskesmas Baloi Permai. Solutions are implemented to address the partner's challenges through training and the transfer of knowledge and technology. Training activities involve counseling and demonstrations. The knowledge and technology transfer in Puskesmas Baloi Permai entails introducing new methods and technologies for digitizing medical records and replacing outdated practices. The activities are divided into two stages: initially, a presentation is given on the procedures for digitizing electronic medical records, followed by discussions on the digitization process. To ensure the success of medical record digitization, demonstrations are conducted, including sorting the medical record forms to be scanned, simulating the scanning process, saving files for easy identification, and importing image files into the EMR system through appropriate media.
PaintSeg: Training-free Segmentation via Painting

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which creates a contrast between the original image and a painted image in which a masked area is painted using off-the-shelf generative models. During the painting process, inpainting and outpainting are alternated, with the former masking the foreground and filling in the background, and the latter masking the background while recovering the missing part of the foreground object. Inpainting and outpainting, also referred to as I-step and O-step, allow our method to gradually advance the target segmentation mask toward the ground truth without supervision or training. PaintSeg can be configured to work with a variety of prompts, e.g. coarse masks, boxes, scribbles, and points. Our experimental results demonstrate that PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and point-prompt segmentation tasks, providing a training-free solution suitable for unsupervised segmentation.
DC CoMix TTS: An End-to-End Expressive TTS with Discrete Code Collaborated with Mixer

Despite the huge successes made in neutral TTS, content-leakage remains a challenge. In this paper, we propose a new input representation and simple architecture to achieve improved prosody modeling. Inspired by the recent success in the use of discrete code in TTS, we introduce discrete code to the input of the reference encoder. Specifically, we leverage the vector quantizer from the audio compression model to exploit the diverse acoustic information it has already been trained on. In addition, we apply the modified MLP-Mixer to the reference encoder, making the architecture lighter. As a result, we train the prosody transfer TTS in an end-to-end manner. We prove the effectiveness of our method through both subjective and objective evaluations. We demonstrate that the reference encoder learns better speaker-independent prosody when discrete code is utilized as input in the experiments. In addition, we obtain comparable results even when fewer parameters are inputted.
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment

Recent advances in text-to-image diffusion models have achieved remarkable success in generating high-quality, realistic images from textual descriptions. However, these approaches have faced challenges in precisely aligning the generated visual content with the textual concepts described in the prompts. In this paper, we propose a two-stage coarse-to-fine semantic re-alignment method, named RealignDiff, aimed at improving the alignment between text and images in text-to-image diffusion models. In the coarse semantic re-alignment phase, a novel caption reward, leveraging the BLIP-2 model, is proposed to evaluate the semantic discrepancy between the generated image caption and the given text prompt. Subsequently, the fine semantic re-alignment stage employs a local dense caption generation module and a re-weighting attention modulation module to refine the previously generated images from a local semantic view. Experimental results on the MS-COCO benchmark demonstrate that the proposed two-stage coarse-to-fine semantic re-alignment method outperforms other baseline re-alignment techniques by a substantial margin in both visual quality and semantic similarity with the input prompt.
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

Geometry and color information provided by the point clouds are both crucial for 3D scene understanding. Two pieces of information characterize the different aspects of point clouds, but existing methods lack an elaborate design for the discrimination and relevance. Hence we explore a 3D self-supervised paradigm that can better utilize the relations of point cloud information. Specifically, we propose a universal 3D scene pre-training framework via Geometry-Color Contrast (Point-GCC), which aligns geometry and color information using a Siamese network. To take care of actual application tasks, we design (i) hierarchical supervision with point-level contrast and reconstruct and object-level contrast based on the novel deep clustering module to close the gap between pre-training and downstream tasks; (ii) architecture-agnostic backbone to adapt for various downstream models. Benefiting from the object-level representation associated with downstream tasks, Point-GCC can directly evaluate model performance and the result demonstrates the effectiveness of our methods. Transfer learning results on a wide range of tasks also show consistent improvements across all datasets. e.g., new state-of-the-art object detection results on SUN RGB-D and S3DIS datasets. Codes will be released at https://github.com/Asterisci/Point-GCC.