Reviewerly/exHarmony huggingface dataset:
Meningkatkan Hasil Belajar Siswa dengan Model Pembelajaran Picture And Picture Kelas V SDN 4 Sukoharjo

In class V learning at SDN 4 Sukoharjo, Tirtomoyo District in 2020/2021, student learning outcomes are low . Of the 13 students, only 4 students (31%) completed with an average grade of 62.46 even though the KKM score was 70. The purpose of this study is to improve student learning outcomes by applying the Picture and Picture learning model. The research was carried out in 2 cycles, cycle 1 on October 27, 2020 and cycle 2 on November 3, 2020. The results of improving learning using the Picture and Picture learning model are quite significant, in the first cycle students who complete are 8 students (62%) with a class average of 73.23 and in the second cycle students who complete increase to 13 students (100%) with the average value of the class increased to 88.92. From these results, it can be concluded that the application of the Picture And Picture learning model can improve the learning outcomes of the fifth grade students of SDN 4 Sukoharjo Semester 1 of the 2020/2021 Academic Year.
Balancing Value Underestimation and Overestimationwith Realistic Actor-Critic

Model-free deep reinforcement learning (RL) has been successfully applied to challenging continuous control domains. However, poor sample efficiency prevents these methods from being widely used in real-world domains. This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. To learn such policies, we introduce uncertainty punished Q-learning, which uses uncertainty from the ensembling of multiple critics to build various confidence-bounds of Q-function. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25% performance improvement on the most challenging Humanoid environment compared to SAC.
IMAGE RETRIEVAL IN IOT-BLOCKCHAIN WITH MULTI-USER AUTHENTICATION -

Image classificationis very important in the analysis of digital images using Block Chain technology. Retrieval patternbed learning is the most effective which aims to establish a relationship between the current and the previous query sessions. It does itb by analyzing the image retrieval pattern. We propose a very new response-based and contentbased image retrieval system. Content-based image retrieval from large resources has nowadays led to a wide range of inbombies and more applicationb. In this paperb we present a content-based image retrieval system that uses color and texture as visual features to represent the contents of an image area. Our contributor, we attribute the extrusion features to the archaburb features, which is different from the BBW shaded regions, which is different from the Image after segment. This increases system effectiveness. In our analysis, we provide a comparison between a retrieval result based on features extracted from the extracted BBB color of the entire image, and features extracted from the extracted BB texture of the imageB regions. the cryptographic pixel values of an image data are stored on the blockchain technology, ensuring the privacy and security of the image data. it is based on the number of pixels change rate (NPCR), the unified averaged changed intensity (UACI), and information entropy analysis, we examine the strength of proposed image encryption algorithm ciphers with respect to differential attacks for retrival of Image from database. This is very approach able and effective method for image retrieval. One can say, this is an effective method.
Classification of Cervical Cancer Through C4.5 Algorithm

In today’s scenario, cervical malignant growth is one of the most affecting diseases in females. Its pace of event is around 80% in low and middle-pay nations. Cancer research is very important for the early treatment of cancer, which encourages medical treatment facilities for patients. The aim of this experiment is to correctly classify the cervical malignancy information in a short duration. Owing to this purpose, a dataset obtained from the UCI repository was used. It contains 668 samples, 30 attributes and 4 objective factors such as Schiller, Biopsy, Hinselmann and Cytology. These samples are used for training and testing purposes. To correctly classify the dataset, the C4.5 algorithm is used. Results show that our method outperforms previous algorithms in terms of classification accuracy and training time performance.
Modeling the Preferences of Demographical Reading History: A Primary Case Study of a Public Library in Shanghai

Abstract Reading habits are influenced by various aspects, such as personal characteristics, reading history and book characteristics. It remains uncertain that which factor determines reader's choice. In this primary work, we attempt to model the preferences based on readers’ demographical information to examine the impact factors on their reading habits. We selected all the reading information of Shanghai Pudong Library in 2015 as the dataset. By applying the logistical regression and likelihood ratio tests, we found that readers’ stickiness, gender, and books’ publication time significantly influence readers’ preferences. Future work will focus on facilitating the model and expanding the data.
On Optimal Interpolation In Linear Regression

Understanding when and why interpolating methods generalize well has recently been a topic of interest in statistical learning theory. However, systematically connecting interpolating methods to achievable notions of optimality has only received partial attention. In this paper, we investigate the question of what is the optimal way to interpolate in linear regression using functions that are linear in the response variable (as the case for the Bayes optimal estimator in ridge regression) and depend on the data, the population covariance of the data, the signal-to-noise ratio and the covariance of the prior for the signal, but do not depend on the value of the signal itself nor the noise vector in the training data. We provide a closed-form expression for the interpolator that achieves this notion of optimality and show that it can be derived as the limit of preconditioned gradient descent with a specific initialization. We identify a regime where the minimum-norm interpolator provably generalizes arbitrarily worse than the optimal response-linear achievable interpolator that we introduce, and validate with numerical experiments that the notion of optimality we consider can be achieved by interpolating methods that only use the training data as input in the case of an isotropic prior. Finally, we extend the notion of optimal response-linear interpolation to random features regression under a linear data-generating model that has been previously studied in the literature.
An Empirical Analysis on Software Development Efforts Estimation in Machine Learning Perspective

The prediction of effort estimation is a vital factor in the success of any software development project. The available of expert systems for the software effort estimation supports in minimization of effort and cost for every software project at same time leads to timely completion and proper resource management of the project. This article supports software project managers and decision makers by providing the state-of-the-art empirical analysis of effort estimation methods based on machine learning approaches. In this paper ?ve machine learning techniques; polynomial linear regression, ridge regression, decision trees, support vector regression and Multilayer Perceptron (MLP) are investigated for the purpose software development effort estimation by using bench mark publicly available data sets. The empirical performance of machine learning methods for software effort estimation is investigated on seven standard data sets i.e. Albretch, Desharnais, COCOMO81, NASA, Kemerer, China and Kitchenham. Furthermore, the performance of software effort estimation approaches are evaluated statistically applying the performance metrics i.e. MMRE, PRED (25), R2-score, MMRE, Pred(25). The empirical results reveal that the decision tree-based techniques on Deshnaris, COCOMO, China and kitchenham data sets produce more adequate results in terms of all three-performance metrics. On the Albretch and nasa datasets, the ridge regression method outperformed then other techniques except pred(25) metric where decision trees performed better.
ТРАНСФОРМАЦІЯ ОСВІТИ ЯК СИСТЕМИ ПІДГОТОВКИ ЛЮДИНИ ДО ЖИТТЯ ПІД ВПЛИВОМ СОЦІОКУЛЬТУРНОЇ ДИНАМІКИ ХХІ СТОЛІТТЯ

Radical changes in the world order, new trends in the economy, culture, political and legal relations require a revision of the paradigmatic foundations of education in the XXI century. Solving this problem is a very important task, because the education system provides the formation of civic, professional, moral and other qualities that contribute to the formation of the individual as a member of a particular social community.The close relationship between values and the process of forming value orientations with educational practices is one of the most important basic constants of the interdependence of education and spiritual values. Trying to identify and explain the main trends of socio-cultural change in the XXI century, scientists use several basic theoretical models, which in turn allows us to see possible directions for updating the educational system.Number of new socio-cultural phenomena in the XXI century need their further scientific understanding and clarification of their impact on educational processes.
Hábitos de estudio y educación a distancia en estudiantes de ciencias médicas en tiempos de COVID-19

Background : distance learning developed in medical sciences in times of COVID-19 is a learning model self-managed by the student. It requires adequate study habits to guarantee an efficient learning. Objective : to describe study habits developed by medical students of the Faculty of Medical Sciences of Sagua la Grande, University of Medical Sciences of Villa Clara, during distance learning in times of COVID-19. Methods : a cross-sectional descriptive study was carried out, giving a questionnaire to 29 2nd-year medical students of the academic year 2020-2021, at the aforementioned university. The following variables were assessed: professional motivation, environmental factors, planning, reading efficacy, study methods and strategies, task-solving efficiency. Results : 25 students (86,2 %) expressed motivation for the profession. The environmental factors were favorable in 79,31 % of the cases; family duties affected 13,79 %. 89 % devoted five hours or less to study; only 7 students (24,13 %) designed a timetable for the purpose. 44,82 % did not end the oriented activities in time. The mostly used methods and strategies were summarizing and note taking. Most of them (93,10 %) reported difficulties in teacher-student communication to go over points. Conclusions : 2nd-year medical students have favorable motivational components for study, but there are difficulties.
Underwater image enhancement based on computational imaging and deep learning

In order to solve the problem of quality degradation of underwater image due to absorption and scattering of water body, this paper proposes a method of underwater image enhancement based on the combination of computational imaging and deep learning. The method has achieved good results in removing image blur and scattering noise. It can effectively enhance the target images in turbid water, which will allow underwater image applications to have a wider range of areas.
Improving Penginyongan Literacy in Digital Era Through E-Paper Magazine of Ancas Banyumasan

Digital revolution has changed people’s behavior and culture in communicating and consuming information using online media that is why the existence of printed media in local language is increasingly displaced and threatened with extinction. The number of magazine in local language published is dwindled as they are not able to compete against magazines in Indonesian language. This is also supported by the breakdown of regeneration of printed media readers shifting to digital media. The dominance of Indonesian language as national language over local language used for speaking and the lack of local language development as local content in the school curriculum shows that the literacy level of millennial generation is low on Penginyongan language in Barlingmascakep area (Banjarnegara, Purbalingga, Banyumas, Cilacap and Kebumen). Ancas magazine becomes vernacular press and local wisdom based printed media providing educative, persuasive and recreational information. The use of gadget or electronic device connected to internet network can be used to improve the culture of Penginyongan literacy on millennial generation in digital era. Based on the problems above, the research question in this research was how the implementation of e-paper magazine of Ancas Banyumasan improved the culture of Penginyongan language literacy in millennial era. The objective of this research was to maintain the existence of local magazine of Ancas Banyumasan in the development of digital based information technology in the Internet of Things (IoT) era and then it can be awareness media in maintaining and preserving local culture value of Banyumasan.
Face Recognition using Transfer learning on a pre-trained model(VGG16)

Face recognition finds its major applications in health care and marketing. Among many biometrics like finger print, iris, voice, hand geometry, signature etc., face recognition has attained the most popular research orientation because of high recognition rate, uniqueness and large number of features. Some of the challenges in face recognition task include the differences in illumination, poses, expressions and background. Now-a-days deep learning methods are widely employed and they ensure promising results for image recognition and classification problems. Convolutional Neural Networks automatically learn features at multiple levels of abstraction through back propagation with convolution layers, pooling layers and fully connected layers. In this work, an automated face recognition method using Convolutional Neural Network (CNN) with transfer learning approach is proposed. The CNN with weights learned from pre-trained model VGG-16 on huge ImageNet database is used to train the images from the face database. The extracted features are fed as input to the Fully connected layer and softmax activation for classification. Two publicly available databases of face images - Yale and AT&T are used to test the performance of the proposed method. Experimental results verify that the method gives better recognition results compared to other methods.
스마트 앱을 이용한 밀 품종 판별 기초 연구

D.N.A. is an important keyword of the 4th industrial revolution which stands for data, network and artificial intelligence. The application of D.N.A. has rapidly increased to agricultural area. The Autonomous Greenhouse Challenge, for example, is the third international competition to find the optimal algorithm to continuously growing vegetables in greenhouses. In this study, MIT App Inventor (MAI) was used to develop an app for smart devices that allow to classifying wheat grain varieties. Conventionally, image aquisition, construction of a region of interest, background removal, and classification model development has been used for feature recognition process. Features obtained from the images acquired using several morphological features such as longest length, shortest length, eccentricity, circularity, direction, and area. Using MAI to take single grain photos of all varieties and label the images, the feature recognition runs without any additional image processing methods. In this study, 7 varieties (Geungang, GG; Goso, GS; Baikjung, BJ; Arijinheuk, AJH; Ariheuk, AH; Yeonbaik, YB; Hwanggeumal, HGA) were used. Among them, white varieties are YB, GG and BJ. AJH and AH are black and red are GS and HGA. AJH has the lowest L* value of 31.82±1.14, while YB is 57.88±2.14. AJH has a positive a* value of 8.31±1.29 and YB has a positive b* value of 29.57±0.11.
LibGuides: Library Technologies: FAQ

The aim of this libguide is the help students with accessing the library technologies, such as the computers, printers, etc.
Teaching Special Education During the COVID-19 Pandemic: An Exploratory Mixed Methods Analysis

The arrival of the global pandemic transformed the system of teaching and learning instantaneously for school staffs, students and their families.  It has become more apparent that the change in venue has brought about conditions that directly impact the quality of the teaching and the degree to which students learn.  Special educators in particular, given the heterogenous nature of those that they teach and their unique learning needs, are faced with challenges that are arguably beyond most other educators.  The current study utilized a mixed-methods approach in an effort to investigate and delve into the perspectives special education teachers hold related to: (a) The support and training they received; (b) Feelings of self-efficacy; and (c) general impressions about the students and families they interact with.  General results and analysis were indicative of several findings.  Specifically, that special education teacher participant felt supported by their faculty and administrative staffs, the teachers did a significant amount of independent learning, that their respective pre-service, formal training was lacking in preparing for virtual learning, and all participants reported significant additional stress challenges, including working with technology.  Limitations and future research possibilities are discussed.
Online Learning In Pandemic Time And Its Impact-Pros And Cons

With the COVID-19 pandemic bringing the entire world down to its knees, education sector was also disrupted in a major way. Pedagogy and students alike had to adjust and adapt to the new ways of teaching and learning with the IoT being the backbone of the online classes. Smart phones, laptops, tablets etc. have replaced notebooks and collaborative tools like Webex, Zoom, Teams, Hangouts etc. are the new blackboards. After nearly two years in the new normal, we have tried to weigh the benefits and issues with this system. Evident gains are sustained continuity of education for all while infrastructural along with access issues have stressed the less privileged students. With the pandemic not going away anytime soon, we do hope that the systemic issues would be resolved and there is definitely a light at the end of tunnel for the students in the online learning system.
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.
Presencial o virtual? fortaleses i febleses de l\'escola a partir del temps de confinament

catalaLa situacio sanitaria del COVID19 ha provocat notables canvis en les formes d’actuacio dels centres educatius. La mes notable ha estat implantar un sistema d’educacio a distancia. En l’article tractem de preguntar-nos sobre les semblances i diferencies de l’ensenyament a distancia i presencial. Ho fem a partir de les dades d’una enquesta a families duran tel confinament. Potser fortaleses i febleses de les actuacions educatives no estiguen tant distants en una forma o altra d’ensenyament EnglishThe health situation of COVID19 has led to significant changes in the ways in which educational establishments operate. The most notable has been the implementation of a distance education system. In the article, we are trying to ask about the similarities and differences in distance and presence education. We do this from the data from a survey to families that last tel. Perhaps the strengths and weaknesses of educational performances are not so distant in one form or another of teaching
Research on Registration Algorithm Based on Hybird Feature Point Detection Using GaoFen-3 Image

To solve the problem of the unstable feature point extraction and the difficult feature matching in SAR image processing, we proposed a detection algorithm based on hybrid feature extraction. First, the repeated gray information is removed to reduce the complexity and import the real-time performance of the algorithm. Then MSER is utilized to extract regional extremes as candidates. And Harris-Laplace algorithm, which ensures uniform and stable feature-point-distribution, is employed to detect the feature points. Next, an improved SAR-SIFT algorithm is used for the feature matching. Finally, FSC and VFC are devised to refine the feature matching results. The dataset we used is the GaoFen-3 series image as it retains fine edge and structural texture information. The experiments have demonstrated the effectiveness of the proposed algorithm with insert performance metric here.