Reviewerly/exHarmony huggingface dataset:
PONDOK PESANTREN DAN MENGEMBANGKAN KUALITAS SUMBER DAYA MANUSIA

Human Resources (HR) has a very important position in realizing development capacity, which places humans in their function, namely as a development resource.The purpose of this study was to determine the efforts made by the Pondok Pesantren Darussalam Banyuwangi in developing the quality of human resources, to determine the supporting and inhibiting factors faced by the Darussalam Islamic Boarding School in developing the quality of human resources.To achieve these objectives, researchers used a qualitative research type with descriptive methods.The results showed that; (1) The existing education and teaching system in the Darussalam Islamic Boarding School, namely using the formal education system, non-formal education, and extracurricular education, (2) The efforts of the Darussalam Islamic Boarding School in developing quality human resources, which can be proven by the existence of various activities.and the educational activities in it, of which all these educational programs and activities are in order to develop the quality of faith, science and other skills of the students or students, from which all these resources can be created.quality human resources, (3) Supporting and Inhibiting Factors of Darussalam Islamic Boarding School in developing the quality of human resources, divided into internal factors and external factors.
Вимоги до застосування окремих елементів психологічних тренінгів у процесі викладання іноземної мови за професійним спрямуванням у ЗВО

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
logical-meaningful model of the formation of a culture of educational and research activities among junior university students in the process of teaching English

The purpose of the study is to develop a model for the formation of a culture of educational and research activities among junior students of the university in the process of their teaching English. The article concretizes the concept of "model", examines various types of models and methodological approaches to their construction. The scientific novelty of the research lies in the development of a logical-meaningful model of the formation of the culture of educational and research activities among junior students of the university in the process of teaching English. As a result, it was concluded that the most important property of the developed model is its integrity: the model gives a generalized description of the object of research, isolates the most significant sides for the study, makes it possible to establish a functional relationship between the parameters under study, to increase students' motivation for learning, the development of creative and creative abilities, as well as active research activities.
Object Detector for Visually Impaired with Distance Calculation for Humans

Object detection is a computer vision technique for locating instances of objects in videos. When we as humans look at images or videos, we can recognize and locate objects within a matter of moments. The main goal of this project is to clone the intelligence of humans in doing that using Deep Neural Networks and IOT, Raspberry Pi and a camera. This model could be used for visually disabled people for improved navigation and crash free motion. When we consider real time scenarios, numerous objects come into a single frame. To identify different items simultaneously as they are captured, a strong model needs to be developed. YOLO (You Only Look Once) is a clever convolutional neural network (CNN) that helps in reaching that objective. The algorithm applies a single neural network to the full image, and then divides the image into regions and predicts bounding boxes and probabilities for each region. The bounding boxes are nothing but weighted by the predicted probabilities. The second objective of this model is to calculate distance of humans from the camera, to achieve that haar classifier is created and used. This classifier also helps in enhancing human detection along with distance calculation. Haar is just like a kernel in CNN where the kernel values are determined by training while in Haar they are determined manually. Whenever a person is detected by both YOLO and Haar classifier, a formula which considers height and width of human contours is applied to calculate the distance of it from the camera. As the objects are identified they will be read out using a text-to-speech engine known as gTTS(google text-to-speech) and ,which stores the text in an mp3 file. The package known as Pygame will load and play the mp3 file dynamically as the objects are detected. This developed Deep Learning model is integrated with Raspberry Pi using OpenCV. Though this project is primarily developed to aid visually disabled people, it can have various other applications such as, self-driving cars, video surveillance, pedestrian detection, face detection.
Penurunan Disleksia pada Balita Melalui Permainan Puzzle Alfabeth dan Kartu Huruf

Dyslexia is a disorder in the form of difficulty reading, writing, spelling which results in children not being able to read and speak. Teachers can stimulate children's thinking interested in learning to read using interesting learning techniques such as alphabet puzzle games and letter cards. This study aims to determine the effect of alphabet puzzle games and letter cards to reduce dyslexia. The research design is quasi-experimental with a one-group pretest-posttest design approach, namely measurement of observations before and after being given an alphabet puzzle game and letter cards for 3 months every 7 days. The research subjects were all students under five as many as 66 people. The dependent variable is alphabet puzzle and letter cards and the independent variable is the decrease in dyslexia. The results showed that there was a decrease in dyslexia in toddlers using alphabeth puzzle games (70% to 13%) and letter card games (67% to 16%). The results of the Paired T test statistical test obtained a Sig 2 tailed value of 0.000 (p &lt; 0.05) meaning that there was an influence of alphabet puzzle games and letter cards on the decrease in dyslexia. The conclusion is that there is an effect of alphabet puzzle games and letter cards on the reduction of dyslexia in children under five.
Committees: Proceedings OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2020 (MATHTECH 2020)

First Page
Prospective Classification over Various Handwritten Character Recognition Algorithms – A Survey

Handwritten Character Recognition is comprehensively used to scrutinize and appreciate characters from pictures and secret them to cutting edge substance. Character recognition is head, yet for the most part testing in the field of model recognition with a colossal number of significant applications. It has been a genuine field of investigation since the start of programming since it being a characteristic strategy for associations among PCs and individuals. Even more accurately Character recognition is the route toward recognizing and seeing characters from the data picture and converts it into ASCII or another similar machine editable design.
Applications of triple quadrupole ICP-MS (ICP-MS/MS, or ICP-MS QQQ) in economic geology

Triple quadrupole ICP-MS (Inductively Coupled Plasma -Mass Spectrometry) is the latest generation quadrupole ICP-MS.The front quadrupole set and the reaction cell before the traditional quadruple set make it possible to minimize isotope interferences that are not separable before, e.g., 87 Rb + vs 87 Sr + , and 32 S + vs 16 O 16 O + .Coupled with a laser ablation system, it has several new applications in economic geology research and mineral deposit exploration, including in-situ Rb-Sr dating, measurement of low concentration sulfur, in-situ sulfur isotope analysis, and molybdenite Re-Os dating.In-situ Rb-Sr dating methodology is being developed in several labs.At Colorado School of Mines, with an Agilent 8900 ICP-MS/MS, a Resolution-SE 193nm laser system, O 2 cell gas, Mica-Mg-NP nano-pellet standard, laser downhole fractionation corrected using an averaging method, and machine drift with an exponential model, we achieve ~2% relative uncertainty at two sigma level.This is demonstrated by the dating of a muscovite.Our result is 304 ± 6Ma (MSWD = 1.3), which is statistically indistinguishable from the Ar-Ar ages of 305 ± 3 and 308 ± 3Ma and Re-Os ages of 306 ± 3 and 306 ± 2Ma of molybdenite of the same paragenesis stage [1].For low concentrations of sulfur, using the MS/MS mode, O 2 cell gas, BAM-S005B external standard, 50-100µm spot, 5 Hz and 3-4J/cm 2 laser, the detection limit (DL) is reduced to 3-10 ppm, significantly lower than the ~200 ppm DL at single quadrupole mode.This capacity is applied on hydrothermal calcite around a porphyry-skarn deposit and the S content shows a trend, adding another distal vector for exploration of skarns [2] and porphyry deposits hosted in carbonates.Sulfur isotope analysis using LA-ICP-MS/MS is at an early stage, with an uncertainty of ~1‰ (2SD).[1]
Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)

This paper contributes to the thread of research on the learnability of different dependency annotation schemes: one ('semantic') favouring content words as heads of dependency relations and the other ('syntactic') favouring syntactic heads.Several studies have lent support to the idea that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers.This may be explained by postulating that syntactic approaches are generally more learnable.In this study, we test this hypothesis by comparing the performance of five parsing systems (both transition-and graph-based) on a selection of 21 treebanks, each in a 'semantic' variant, represented by standard UD (Universal Dependencies), and a 'syntactic' variant, represented by SUD (Surface-syntactic Universal Dependencies): unlike previously reported experiments, which considered learnability of 'semantic' and 'syntactic' annotations of particular constructions in vitro, the experiments reported here consider whole annotation schemes in vivo.Additionally, we compare these annotation schemes using a range of quantitative syntactic properties, which may also reflect their learnability.The results of the experiments show that SUD tends to be more learnable than UD, but the advantage of one or the other scheme depends on the parser and the corpus in question.
The survey on cross-border collection of digital evidence by representatives from Polish prosecutors’ offices and judicial authorities

Dynamic development of IT technology poses new challenges related to the cross-border collection of electronic evidence from the cloud.Many times investigators need to secure data stored on foreign servers directly and then look for solutions on how to turn the data into a legitimate source of evidence.To study the situation and propose solutions, I conducted a survey among Polish representatives of public prosecutors' offices and courts.This paper presents information from digital evidence collection practices across multiple jurisdictions.I stated that representatives from the prosecution and the judiciary in Poland are aware of the issues associated with cross-border acquisition and preservation of cloudbased evidence.In their view, many of the problems are time-consuming and ineffective international cooperation, the voluntary nature of cooperation between foreign cloud service providers, lack of harmonized procedures and guidelines, the diversity of legal systems, and the lack of knowledge held by law enforcement officials and the judiciary.This work should be the beginning of an open discussion with practitioners about existing challenges and an invitation for further research with a larger sample of prosecutors and judges.There are no such studies in literature.The paper shows that it is possible to improve current procedures for the cross-border collection of cloud-based digital evidence.
JPGT Preparation towards Accredited and Internationally Reputed Journal

Research by teachers and students at a university is closely related to the quality of the institution. Scientific journals in Indonesia at the international level are still low, including publications in periodicals (scientific journals) that are indexed in international reputable indexers. The management of scientific journals in order to achieve the target of becoming a nationally accredited scientific journal or even becoming a journal of international repute requires an effective and efficient journal management and management system based on the instruments and qualifications required by national journal accreditation institutions such as ARJUNA and reputable international indexing institutions. JPGT will try to meet the criteria for international journals, namely by having reviewers from at least 4 countries and papers published from at least 2 countries. The plan after obtaining national accreditation is to be indexed with international reputation.
Analysis of the Cost-Effective Road Maintenance Program for the Improvement of Road Value in Kabupaten Gresik Using the Forecasting Method

Roads have a very significant role in enhancing various fields as part of the national transport network. Road management includes road construction, improvement, and technical maintenance activities, which are continuously carried out for the physical maintenance of the road. The role of the road service must be fulfilled so as not to compete with the transportation that passes on the road. Based on the phenomena that have been described, we present the analysis of the cost requirements and methods of road handling in addition to the effectiveness of road stability for the next two years. The research was carried out for a case study of the district major road in Gresik Regency, which is represented in this situation by three main roads. The analysis of a data set is based on the variables of historical data. Then, we analyze using time series with moving averages, exponential smoothing, and trend projection. Next, the method for setting a maintaining strategy of overcoming prediction and its relation to the solidity of the roads, using fuzzy tahani. The results analyzed are confirmed by the recommended output for the next two years.
Laplacian based Image Mosaicing

In this paper, we propose to stitch multiple images using the technique of homography estimation. Firstly, Sorting the images,then computing pairwise homography between the images and then by performing image blending the objective is achieved.Then a novel decision fusion framework based on spanning tree and exif data to obtain the order of images is proposed. After the order of images are obtained, to estimate the pairwise projective homographies between the images is done. Then we warp the images according to the computed homography. The warped images later undergo feathering and laplacian blending in order to obtain seamless stitching.
THE <i>RHESUS</i> AS A FOURTH-CENTURY PLAY - (M.) Fantuzzi (ed.) The <i>Rhesus</i> Attributed to Euripides. (Cambridge Classical Texts and Commentaries 63.) Pp. viii + 711. Cambridge: Cambridge University Press, 2020. Cased, £130, US$170. ISBN: 978-1-107-02602-5.

THE RHESUS AS A FOURTH-CENTURY PLAY - (M.) Fantuzzi (ed.) The Rhesus Attributed to Euripides. (Cambridge Classical Texts and Commentaries 63.) Pp. viii + 711. Cambridge: Cambridge University Press, 2020. Cased, £130, US$170. ISBN: 978-1-107-02602-5. - Volume 72 Issue 1
MASTERING RESEARCH SKILLS BY FUTURE ELEMENTARY SCHOOL TEACHERS UNDER THE CONDITIONS OF NON-STANDARD LESSON

This study investigates the development of research skills in the course of higher education training programs for future elementary school teachers. The purpose of this article is to analyze the formation and development of the scientific and pedagogical outlook of higher education students by employing an innovative approach to traditional education. The article identifies the role of modern children’s books in the development of educational competencies by university students in the elementary education field.The authors suggest optimal methods for future teachers’ research competence development by using the material of modern children’s literature. The emphasis on the creative nature of teaching instruction and the application of learning technologies in the education process are outlined. The proposed design foundation for unconventional teaching methods is focused on generating interest in the content of the subject being taught, emphasizing creative approach to the learning exercise completion and ongoing pedagogical evaluation and research. These principles define the educational content and the teaching methods.The author underlines the importance of using the training method in the classroom for the independent assignments to activate the students’ creative potential and curiosity.The study shows how the training session in the works of modern children’s literature could contribute to the language quality and literary education improvements for future elementary school teachers.It is proposed in the article to design a system of training sessions to develop personal and professional competencies and skills required for the future elementary school teachers. The authors hope that the content of the article is of interest to all Ukrainian language students and educators of all levels from elementary to postgraduate education.&#x0D; Keywords: educational process; research skills and competence; language and literary competence; children’s literature, integration; innovation in training; training, hands-on training.
Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness

Seq2seq models have demonstrated their incredible effectiveness in a large variety of applications.However, recent research has shown that inappropriate language in training samples and well-designed testing cases can induce seq2seq models to output profanity.These outputs may potentially hurt the usability of seq2seq models and make the end-users feel offended.To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity.The proposed training framework leverages merely a short list of profanity examples to prevent seq2seq models from generating a broader spectrum of profanity.The framework is composed of a patterneliminating training component to suppress the impact of language patterns with profanity in the training set, and a trigger-resisting training component to provide certified robustness for seq2seq models against intentionally injected profanity-triggering expressions in test samples.In the experiments, we consider two representative NLP tasks that seq2seq can be applied to, i.e., style transfer and dialogue generation.Extensive experimental results show that the proposed training framework can successfully prevent the NLP models from generating profanity.
Learning and Analyzing Generation Order for Undirected Sequence Models

Undirected neural sequence models have achieved performance competitive with the state-of-the-art directed sequence models that generate monotonically from left to right in machine translation tasks.In this work, we train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning.We show that the translations decoded by our learned orders achieve higher BLEU scores than the outputs decoded from left to right or decoded by the learned order from Mansimov et al. ( 2019) on the WMT'14 German-English translation task.On examples with a maximum source and target length of 30 from De-En and WMT'16 English-Romanian tasks, our learned order outperforms all heuristic generation orders on three out of four language pairs.We next carefully analyze the learned order patterns via qualitative and quantitative analysis.We show that our policy generally follows an outer-to-inner order, predicting the left-most and right-most positions first, and then moving toward the middle while skipping less important words at the beginning.Furthermore, the policy usually predicts positions for a single syntactic constituent structure in consecutive steps.We believe our findings could provide more insights on the mechanism of undirected generation models and encourage further research in this direction. 1
An Overview of the Integration of Basic Research and Digital Skills in the High School Curriculum

The new landscape in the education sector calls for diverse basic research and digital integration skills. However, basic education institutions are still facing a challenge of integrating research and digital skills in their teaching and learning approaches. In addressing this challenge, an investigation to establish the curriculum design of the Life Orientation (LO) subject regarding its structure to accommodate research and digital skills was conducted. This was achieved through literature review, document analysis, workshops and brainstorming sessions with LO teachers from two schools that are based in two provinces. Data was analysed using thematic data analysis approach, whereby Design Science Research Methodology (DSRM) was embraced. The findings of the document analysis and workshops led to the development of the proposed process model for the basic research and digital skills integration into teaching and learning of the pilot subject, which can later be used for other subjects.
Diffusion Models Beat GANs on Image Synthesis

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\times$128, 4.59 on ImageNet 256$\times$256, and 7.72 on ImageNet 512$\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\times$256 and 3.85 on ImageNet 512$\times$512. We release our code at this https URL