Reviewerly/exHarmony huggingface dataset:
Perancangan Kuesioner Penjaminan Mutu Pembelajaran Daring di Universitas Sari Mulia

The Covid-19 pandemic has changed the pattern of activities in almost all fields, including the world of education. The world of education, from early childhood education (PAUD and TK) to higher education, since March 2020 has implemented a study from home policy (Study from Home), resulting in many changes in all aspects, for example, the implementation of teaching and learning is carried out online. . Changes in the state of the Teaching and Learning Process  certainly have an impact on student performance and level of understanding. The implementation of online Teaching and Learning Process, from an early age to the tertiary level, has many obstacles, for example internet service facilities and accessibility, the ability of parents to guide their children, differences in the level of understanding of the material presented by teachers/lecturers, and others. As an effort to maintain the quality of education during the Covid-19 pandemic, an evaluation instrument for the implementation of online Teaching and Learning Process is needed, so that the results can be used to improve the quality of learning. The instrument used was a questionnaire which was circulated to students and lecturers at Sari Mulia University, Banjarmasin. The consideration in making questionnaires is the possibility of differences in answers between lecturers and students, so a questionnaire is compiled that can accommodate both. The results obtained are questionnaires which include aspects of: (1) the media used; (2) internet connectivity; (3) Learning management system; (4) Internet data subsidies; (5) Teaching and Learning Process.
The age structure of the scientific and pedagogical staff: data from the regions of Russia

The purpose of our study was to evaluate the indicators characterizing the age structure of the scientific and pedagogical staff of universities and other higher educational institutions in the regions of Russia. In the course of the work, the indicators characterizing the specific weights of teachers belonging to five age groups from 25 to 34 years, from 35 to 44 years, from 45 to 54 years, from 55 to 64 years and over 65 years in the total number of teachers working in higher education institutes were evaluated. The study used official statistical information for 82 regions of Russia. We used the density functions of the normal distribution as models. The results of the research allowed us to draw conclusions: the largest number (more than a quarter) of teachers was observed in the group from 35 to 44 years; 24% of teachers were aged from 45 to 54 years; 20% of teachers were slightly older. The least number of teachers was observed in the age groups over 65 years and from 25 to 34 years. The proposed methodological approach and the results obtained have originality and scientific novelty, since the assessment of regional features of the age structure of scientific and pedagogical personnel in the regions of Russia has not been carried out before.
Educar en tiempos de crisis: desafíos y alternativas

This article deals with education in emergencies, contextualized to the crisis caused by the COVID-19 pandemic. Education in emergencies requires the joint efforts of governmental, social and community institutions, with the purpose of protecting children, adolescents and young people in their right to education and, at the same time, in their biopsychosocial wellbeing. Although the right to education in crisis situations is affected, limited and violated in terms of accessibility, affordability, adaptability and/or acceptability, strategies are defined to guarantee educational continuity, with distance education being the main alternative during the response stage. This must be accompanied by curricular adaptations, which are characterized by their practical, realistic and achievable character; as well as by the socio-emotional education of students, which can promote the healthy elaboration of what has been experienced and stimulate resilience, through the use of playful dynamics and various resources.
РЕЗУЛЬТАТЫ ЭКСПЕРИМЕНТАЛЬНО-ТРАСОЛОГИЧЕСКИХ ИССЛЕДОВАНИЙ, ПРОВОДИВШИХСЯ В ЛЕТНЕЙ АРХЕОЛОГИЧЕСКОЙ ШКОЛЕ В БОЛГАРЕ (РЕСПУБЛИКА ТАТАРСТАН)

Actual use-wear method is successfully used for study the sets of tools from different species of raw materials applicable not only to the Stone Age, but also to subsequent eras. This requires the expansion of the experimental base of tool standards, which is necessary for verifi cation of functional analysis, interpretation of the features of ancient technologies. The paper discusses the results of recent experimental and use-wear studies were organized and conducted by the eponymous section of the school in Bulgar (Republic of Tatarstan) and the expedition of the Institute for the History of Material Culture of the Russian Academy of Sciences. The experiments were related to obtaining the standards necessary to characterize certain categories of little-studied tools. They were organized in compliance with the procedures developed during the long-term experimental expeditions of the Leningrad Branch of Institute of Archaeology of the USSR Academy of Sciences / Institute for the History of Material Culture of the Russian Academy of Sciences. The research included: theoretical preparation, problem setting, ensuring the approximation of experiments to ancient conditions, seriality of the same experiments, one experimenter to establish signifi cant patterns, statistical processing of results, detailed fi xation in various ways (description, graphically, macro-, microphotography). The obtained data will be a reliable source for further experimental and traceological studies.
Penerapan Model Poe Berbasis Blended Learning Untuk Meningkatkan Keterampilan Berpikir Tingkat Tinggi Peserta Didik Kelas XI SMA

This classroom action research aims to improve students' higher order thinking skills by using the POE model based on blended learning in learning. The research method used is classroom action research. The subjects of this study were students of class XI MIPA1 SMA N Painan in the 2020/2021 semester II academic year, totaling 36 people. The object of this research is the students' higher order thinking ability. This research was conducted in two learning cycles, with the stages in each cycle being planning, action, observation/evaluation, and reflection. The higher order thinking ability in question is the score of students obtained in doing the higher order thinking skills test (C4, C5 and C6) which consists of 10 essay questions. This research is said to be successful if the average value of students' higher order thinking skills is 78, with classical completeness of 85%. The data that has been collected is then analyzed descriptively. The results of the analysis show 1) the average value of students' higher order thinking skills in the first cycle is 76.70 with 72% classical completeness and in the second cycle is 84.50 with 86% classical completeness.
At the end of the rainbow – Scientific Colour Maps for science and society

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]At the end of the rainbow – Scientific Colour Maps for science and societyAuthorsGraceShephardiDFabioCrameriiDPhilipHeronSee all authors Grace ShephardiDCorresponding Author• Submitting AuthorUniversity of Oslo, Centre for Earth Evolution and Dynamics (CEED)iDhttps://orcid.org/0000-0002-3459-4500view email addressThe email was not providedcopy email addressFabio CrameriiDUniversity of OsloiDhttps://orcid.org/0000-0002-1989-2729view email addressThe email was not providedcopy email addressPhilip HeronDepartment of Earth Sciences, Durham University, Durham, United Kingdomview email addressThe email was not providedcopy email address
Security Augmented Symmetric Optical Image Cryptosystem Based on Hybrid Transform Employing Rear Mounted Technique Using Three Different Complex Masks

This paper presents the utilization of the novel rear mounted triple phase masking procedure to increase the security of traditional double random phase encoding (DRPE) scheme with a variation of hybrid transform. Two chaotic random phase masks and a deterministic phase mask are used in the scheme to enhance its security as opposed to traditional DRPE which uses random phase masks. From the previous studies on cryptanalysis of DRPE, it is clear that the second lens used in it proves to be irrational which leads to cryptanalytic attacks on DRPE. The enhanced framework discards the invalidation of the second lens and therefore reinforces DRPE security. The hybrid transform is a combination of fractional Hartley transform and Gyrator transform that improves the quality of the cryptosystem. The carried out simulations exhibit the efficacy of the designed symmetric cryptosystem.
Machine Learning Vs Deep Learning: Which Is Better For Human Activity Recognition

Human activity recognition(HAR) is used to describe basic activities that humans are performing using the sensors that we have in smartphones. The data for this activity recognition is captured by various sensors of mobile phones or wristbands such as accelerometer, gyroscope and gravity sensors.HAR has grabbed the attention of various researchers due to its vast demand in the fields of sport training, security, entertainment health monitoring,computer vision and robotics. In this project we compare different machine learning and deep learning algorithms to find a better approach for HAR. The dataset comprises six activities i.e. walking, sleeping, sitting,moving upward, moving downwards and standing.In this demonstration we also showed confusion matrix,accuracy and multi log loss of various algorithms. With the help of accuracy, confusion matrix of algorithms we compare and determine the best approach for HAR. This will help in future research to map the activities of humans using one of the best approaches used
Diagnosis of Lung Disorder Using Immune Genetic Algorithm and Fuzzy logic to Handle Incertitude

In this paper, we present an immune based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme applies to lung CT images and to detect and classify lung nodules. Classification of lung tissue is a significant and challenging task in any computer aided diagnosis system. This paper presents a technique for classification of lung tissue from computed tomography of the lung using the Gaussian interval type-2 fuzzy logic system. The type-2 Gaussian membership functions (T2MFs) and their footprint of uncertainty (FOU) are tuned by immune, genetic algorithm, which is the combination of immune genetic algorithm (GA) and local exploration operator. An immune, genetic algorithm estimates the parameters of the type-2 fuzzy membership function (T2MF). By using immune, genetic algorithm, converging speed is increased. The proposed local exploration operator helps in finding the best Gaussian distribution curve of a particular feature which improves the efficiency and accuracy of the diagnosis syste
THE USE OF NONLINEAR DYNAMIC SYSTEM AND DEEP LEARNING IN PRODUCTION CONDITION MONITORING AND PRODUCT QUALITY PREDICTION

To improve the product quality and production efficiency in industrial production, the industrial production process is explored. First, for the production state in industrial production, the radio frequency identification technology (RFID) is used to monitor the state of the nodes in the production process and collect data. Based on the problem of blind area in industrial production, an adaptive controller design based on uncertain nonlinear dynamic systems is introduced; second, based on this, for the problem that product quality is difficult to guarantee in the process flow, the improved multi-layer feedforward network (back propagation, BP) algorithm is used to predict it. Finally, the control diagnosis algorithm based on multi-order least squares support vector machine (LS-SVM) is used to analyze the abnormal cause of the product. The results show that the use of the RFID can monitor the production process in real time and obtain real-time product quality information data. The proposed adaptive controller based on uncertain nonlinear dynamic system has good applicability in solving the problem of blind area. The improved BP neural network algorithm improves the prediction accuracy in the process of product quality prediction and increases the convergence speed of the algorithm. The control diagnosis algorithm based on multi-order least squares that support vector machine (SVM) can classify and learn the historical data of products, as well as perform multi-order search and analysis on the causes of abnormal quality. It helps relevant personnel to carry out process improvement and control, thereby achieving the purpose of improving product quality. Through this investigation, it is found that the method proposed can monitor the state of industrial production in real time and help relevant personnel to control and predict product quality. It can be applied in actual processes.
Aspects That Children Develop In The Kindergartens

The kindergarten can be overwhelming for kids, especially kindergarten kids who may have never entered a classroom before. As a teacher, we can make the transition to school a bit easier by providing our class with fun and memorable activities. This article provides information on the aspects that children develop in the kindergartens.
Hybrid Blind Image Watermarking Scheme using RDWT-DCT with Porcellio Scaber Algorithm

Watermarking is a technique which offers more robustness and good imperceptibility. Even if the algorithmic principle is public or the existence of hidden information is known, it is difficult to attacker to destroy the embedded watermark without degrading the watermarked media severely. In existence, non-blind watermarking (NBW) schemes and different combinations of techniques are proposed for watermarking procedure. But these methods failed to provide the good imperceptibility, lack of robustness and embedding capacity standards, respectively. Thus, to overcome this problem, a hybrid algorithm for blind watermarking (BW) is proposed, which utilizes redundant discrete wavelet transform (RDWT), discrete cosine transform (DCT) together to have the advantages of all three. In addition, optimization of proposed RDWT-DCT is obtained using a new bio-inspired algorithm called Porcellio Scaber algorithm (PSA). Thus, the visual perception of extracted watermark image also good enough by maintaining the robustness against various image attacks. Experiments are carried out to test the proposed BW algorithm and it is successful under various geometrical and non-geometrical attacks. Finally, the performances of proposed BW using RDWT-DCT with PSA optimization is compared with various existing BW approaches in terms of various quality metrics like peak signal to noise ratio (PSNR), structural similarity (SSIM) index, root mean square error (RMSE) and normalized correlation coefficient (NCC).
Pengaruh Pendekatan Pembelajaran Matematika Realistik Terhadap Hasil Belajar Matematika Ditinjau dari Minat Komputasi Siswa

This study aims to find out the effect of Realistic Mathematics Education toward mathematic learning result of students in grade III Elementary School Students II Gianyar’s Subdistrict. The results showed that. (1) there was a difference in learning outcomes of Mathematics between groups of students who followed Realistic Mathematics Education and groups of students following the conventional learning. (2) there is an interaction effect between the learning model and the computation interest toward the learning outcomes of Mathematics. (3) there are differences in learning outcomes of Mathematics between groups of students who follow the Realistic Mathematics Education and groups of students who follow conventional learning in groups of students who have high computation interest and vice versa.
KLASIFIKASI GAMBAR JAMUR BERACUN DAN BUKAN BERACUN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) STUDI KASUS: AMANITA PHALLOIDES, AMANITA CAESAREA, CANTHARELLUS CIBARIUS, OMPHALOTUS OLEARIUS, VOLVARIELLA VOLVACEA

Mushroom poisoning is one of the phenomena that occur in society due to errors in identifying mushrooms and eating them. The effects are varied, ranging from mild cases, such as nausea, vomiting, diarrhea, dizziness, to the more severe case, namely death. As part of efforts to minimize the occurrence of similar incident, a solution is created through the approach of artificial intelligence, which is to generete a model that can distinguish toxic mushrooms and non-toxic one. There are five species of mushroom that is carried out in this research, those are Amanita caesarea, Cantharellus cibarius, and Volvariella volvacea as non-toxic mushrooms and Amanita phalloides and Omphalotus olearius as toxic mushrooms. There are 142 total images used in this research. For training data, there are 100 images, with 20 images for each species. For validation data, there are 37 images in total, with 7 images for each species, except for species V. volvacea, which has 9 images. And, as for test data, one image for each species. The model was created using Convolutional Neural Network (CNN). The final model has an accuracy rate of 78%. During the training process, data augmentation techniques are used, which is useful to reproduce the same image, but different from original image, by using some transformations, such as rotation, horizontal and vertical flip, adding noise, affine transform, blurring, and center crop. There are 7 images generated from the data augmentation process plus one original image. The batch size used during training phase is 32. Most prediction error are caused by V. volvacea, where 9 images used as validation in total, only 3 images are predicted to be correct, the rest are predicted to be A. phalloides. Overall, the model’s performance is quite good in classifying the species of A. caesarea, A. phalloides, C. cibarius, and O. olearius but biased against V. volvacea. Therefore, the model produced in this research is not reliable enough to be applied to the wider community as a “tool” to distinguish these mushrooms. Keywords: A. caesarea, A. phalloides, C. cibarius, O. olearius, V. volvacea, Deep Learning, CNN
GridToPix: Training Embodied Agents with Minimal Supervision.

While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards. Indeed, without shaped rewards, i.e., with only terminal rewards, present-day Embodied AI results degrade significantly across Embodied AI problems from single-agent Habitat-based PointGoal Navigation (SPL drops from 55 to 0) and two-agent AI2-THOR-based Furniture Moving (success drops from 58% to 1%) to three-agent Google Football-based 3 vs. 1 with Keeper (game score drops from 0.6 to 0.1). As training from shaped rewards doesn't scale to more realistic tasks, the community needs to improve the success of training with terminal rewards. For this we propose GridToPix: 1) train agents with terminal rewards in gridworlds that generically mirror Embodied AI environments, i.e., they are independent of the task; 2) distill the learned policy into agents that reside in complex visual worlds. Despite learning from only terminal rewards with identical models and RL algorithms, GridToPix significantly improves results across tasks: from PointGoal Navigation (SPL improves from 0 to 64) and Furniture Moving (success improves from 1% to 25%) to football gameplay (game score improves from 0.1 to 0.6). GridToPix even helps to improve the results of shaped reward training.
Privacy-Preserved Access Control in E-Health Cloud-Based System

As information and communication technology has advanced taking the world economy along, with enhanced services having permeated our every waking moment, the health sector is not far behind with e-Health, the information technology offering, unambiguously a development the world couldn’t have done without. To the array of benefits of lasting and undeniable value in e-Health, a cloud-based environmental implementation would give the advancement its human face. Despite the visible and plentiful benefits intrinsic from e-Health in cloud environment, there remain security and privacy challenges that are preventing the full array of benefits from being used. Given numerous organizations totally rely upon this e-health data, it is imperative for the data owners to secure the e-health data but difficulties in encryption remain. For example, before sending to the cloud, the data is normally encrypted and each time the client changes the accreditations, re-encryption needs to be carried out which is a weakness in e-Health applications. However, when the data owner does one degree of encryption and cloud does the second degree of encryption, accordingly decentralizing the application’s access control requirements and easing the decision of which client can access which information effectively putting a stop to security breaches during the encryption process itself, which eliminates the need for a gathering supervisor when the client enters or exits the application. In the proposed framework, for adept maintenance of e-health record in clinics, the group-key administration is used effectively to deal with access control arrangements having privacy and security protection of clients preserved in the cloud.
A White Paper on Ethics

Ethics are central to all human development in the current knowledge era where technological innovations and human organisations are playing a pivotal role in shaping the pace and progress of societies. The most effective way to bring ethical consciousness to play its rightful role is to build it from the very early stage of person’s life, particularly from primary education stage itself. Given the complexity of structures which navigate the enormously diverse functions and professions in society, the codes and standards in various professions had been instrumental value in the practice of ethics. In this context, the Indian heritage and traditions could be of seminal value for the development of ethics. The current trend of educational reform to provide student centric and holistic education finds its echoes from the rich heritage in ancient India which used to inculcate spirit of equality, social awareness and practical efficiency in pupils and thus leading the education system towards the highest level.
On the Difficulty of Segmenting Words with Attention

Word segmentation, the problem of finding word boundaries in speech, is of interest for a range of tasks. Previous papers have suggested that for sequence-to-sequence models trained on tasks such as speech translation or speech recognition, attention can be used to locate and segment the words. We show, however, that even on monolingual data this approach is brittle. In our experiments with different input types, data sizes, and segmentation algorithms, only models trained to predict phones from words succeed in the task. Models trained to predict words from either phones or speech (i.e., the opposite direction needed to generalize to new data), yield much worse results, suggesting that attention-based segmentation is only useful in limited scenarios.