Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Memilih Tipe Belajar Anak Retardasi Mental Menggunakan Metode Case Based Reasoning

Mental retardation is an intellectual disability characterized by intelligence being below average. People who are mentally retarded can learn new abilities, but the pace of learning is very slow. There are problems that often occur in the detection that children have mental retardation, especially by parents due to the lack of knowledge of the parents about this, resulting in the mistake of choosing the right type of learning for children. If this is allowed to continue, it will result in the child's mental condition getting worse. Due to these problems, a solution is offered in the form of an expert system application with the Case Based Reasoning (CBR) method, this method will match the symptom data of the old disease with the symptom data consulted by the user so that the similarity number of each type of mental retardation will be produced. The final result of this study is the percentage value of similarity along with recommendations in the form of suggestions and solutions that will be carried out by parents in providing independent education to children with mental retardation. The results obtained were in the form of mental retardation with a percentage of 0%, disabled with a percentage of 82.35%, with disabilities with a percentage of 0%.
Application of Data Mining Classification for Covid-19 Infected Status Using Algortima Naïve Method: Application of Data Mining Classification for Covid-19 Infected Status Using Algortima Naïve Method

The number of virus infected status known as covid -19 is increasing in the southern Jakarta area, namely Pondok Labu, West Cilandak, Jagakarsa, Lenteng Agung, Pasar Minggu and Ragunan, it is necessary to classify the data to find out the negative status of covid-19 virus infection or positive infected with covid virus -19. The technique of classifying positive or negative covid-infected virus status with the naive bayes classification method. To manage the data, software rapid miner 9. 6 is used, the infected status dataset covid -19 is obtained from the websitejeo. compass. CalculationPrediction shows the classification of the Naive Bayes method obtained a positive prediction that shows a figure of 55.48% and a negative prediction result of 44.52%. From the results of the classification of data that has been obtained can be seen that the largest prediction found in the positive status infected with covid -19 virus reached 55.48%.
PEMANFAATAN CERPEN SEBAGAI MEDIA PEMBELAJARAN MENULIS DI KELAS XI TINGKAT SMK

Students are active learners and creative, contextual learning to recognize the identity and act based on their interests, performing and interacting with the environment and study the system of meaning. Various learning to write in class XI vocational level indicates that it was found that students encounter conditions that are not in line with their needs. As a result of learning media is still limited, students are not getting the conditions for active and creative to be a writer. This journal is made ​​to find an alternative solution to the problem of limited media use as a medium of learning to write short stories in class XI vocational level. Once implemented learning, students tend to be more active and creative in learning to write. The ability of students increased after the media used by the teacher As with learning media. The conclusion of this paper is that: (1) short story the media can be a solution to overcome the problem of learning to write in class XI vocational level, (2) media stories help teachers to improve students' skills in more active and creative writing, and (3) media stories to develop students to be active and creative writer.
Where to Use Pointers?

In this chapter, we discuss several pointers use cases, including the use of pointers as function parameters.
Medical Datasets Collections for Artificial Intelligence-based Medical Image Analysis

We collected 32 public datasets, of which 28 for medical imaging and 4 for natural images, to conduct study. The images of these datasets are captured by different cameras, thus vary from each other in modality, frame size and capacity. For data accessibility, we also provide the websites of most datasets and hope this will help the readers reach the datasets.
Sub-Linear Point Counting for Variable Separated Curves over Prime Power Rings

Let $k,p\in \mathbb{N}$ with $p$ prime and let $f\in\mathbb{Z}[x_1,x_2]$ be a bivariate polynomial with degree $d$ and all coefficients of absolute value at most $p^k$. Suppose also that $f$ is variable separated, i.e., $f=g_1+g_2$ for $g_i\in\mathbb{Z}[x_i]$. We give the first algorithm, with complexity sub-linear in $p$, to count the number of roots of $f$ over $\mathbb{Z}$ mod $p^k$ for arbitrary $k$: Our Las Vegas randomized algorithm works in time $(dk\log p)^{O(1)}\sqrt{p}$, and admits a quantum version for smooth curves working in time $(d\log p)^{O(1)}k$. Save for some subtleties concerning non-isolated singularities, our techniques generalize to counting roots of polynomials in $\mathbb{Z}[x_1,\ldots,x_n]$ over $\mathbb{Z}$ mod $p^k$. Our techniques are a first step toward efficient point counting for varieties over Galois rings (which is relevant to error correcting codes over higher-dimensional varieties), and also imply new speed-ups for computing Igusa zeta functions of curves. The latter zeta functions are fundamental in arithmetic geometry.
Sistem Prediksi Curah Hujan Bulanan Menggunakan Jaringan Saraf Tiruan Backpropagation

Rainfall has important role for human life. Rainfall information can be used in several fields including agriculture. As a benchmark for planting periods, water infiltration management, and irrigation. The resources for calculating rainfall are rainfall gauges, ground-based radars and remote sensing satellites. Wonosobo area’s rainfall type is monsoon, meaning that it has one wet period and one dry period. It has fluctuating varied rainfall every month and the availability of rainfall data is uncertain each year. As a mountainous area, Wonosobo’s agricultural sector is very dominant for their economic. Weather Observation, especially rainfall, is important because it can be used by related parties, especially in the agricultural sector. In addition, to provide rainfall data in areas with no observation stations. This study aims to design and implement a rainfall prediction system by developing the Waterfall Model Development Life Cycle (SDLC) Software and implementing backpropagation artificial neural networks (ANN). System development using the SDLC waterfall model was chosen because it is simple, easy to understand and implement. ANN backpropagation is applied in the prediction system because of its advantage that can be applied to a problem related to prediction. Testing on the system built for training and validation produces training accuracy of 93.92% with validation of 73.04%, indicating that the system can be used and has been running expectedly. The best ANN architecture was obtained on the test with input layer 3, hidden layer 12, and output 1 values, learning rate 0.5 momentum 0.9. From the SSE 0.1 target, the SSE is 0.302868.
Internet of Things in Healthcare

Internet of Things (IoT)-enabled devices have made remote monitoring in the healthcare sector possible, unleashing the potential to keep patients safe and healthy, and empowering physicians to deliver superlative care. It has also increased patient engagement and satisfaction as interactions with doctors have become easier and more efficient. Furthermore, remote monitoring of patient’s health helps in reducing the length of hospital stay and prevents re-admissions. IoT also has a major impact on reducing healthcare costs significantly and improving treatment outcomes. Internet of Things, patients’ interactions with doctors were limited to visits, and tele and text communications. There was no way doctors or hospitals could monitor patients’ health continuously and make recommendations accordingly. Keywords:  Hospitals, patients, internet of things Cite this Article G. Ramachandran, S. Kannan, T. Sheela, et al. Internet of Things in Healthcare. Research & Reviews: Journal of Medical Science and Technology . 2019; 8(1): 10–12p.
Adding external factors in time series forecasting. Case study: Ethereum price forecasting

The main thrust of time-series forecasting models in recent years has gone in the direction of pattern-based learning, in which the input variable for the models is a vector of past observations of the variable itself to predict. The most used models based on this traditional pattern-based approach are the autoregressive integrated moving average model (ARIMA) and long short-term memory neural networks (LSTM). The main drawback of the mentioned approaches is their inability to react when the underlying relationships in the data change resulting in a degrading predictive performance of the models. In order to solve this problem, various studies seek to incorporate external factors into the models treating the system as a black box using a machine learning approach which generates complex models that require a large amount of data for their training and have little interpretability. In this thesis, three different algorithms have been proposed to incorporate additional external factors into these pattern-based models, obtaining a good balance between forecast accuracy and model interpretability. After applying these algorithms in a study case of Ethereum price time-series forecasting, it is shown that the prediction error can be efficiently reduced by taking into account these influential external factors compared to traditional approaches while maintaining full interpretability of the model.
Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform.

Path planning is a key component in motion planning for autonomous vehicles. A path specifies the geometrical shape that the vehicle will travel, thus, it is critical to safe and comfortable vehicle motions. For urban driving scenarios, autonomous vehicles need the ability to navigate in cluttered environment, e.g., roads partially blocked by a number of vehicles/obstacles on the sides. How to generate a kinematically feasible and smooth path, that can avoid collision in complex environment, makes path planning a challenging problem. In this paper, we present a novel quadratic programming approach that generates optimal paths with resolution-complete collision avoidance capability.
Epistemic values in feature importance methods: Lessons from feminist epistemology

As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly expanded. Feature importance methods form a popular class of explanation methods. In this paper, we apply the lens of feminist epistemology to recent feature importance research. We investigate what epistemic values are implicitly embedded in feature importance methods and how or whether they are in conflict with feminist epistemology. We offer some suggestions on how to conduct research on explanations that respects feminist epistemic values, taking into account the importance of social context, the epistemic privileges of subjugated knowers, and adopting more interactional ways of knowing.
English Language Teaching During the Times of COVID-19- Challenges and Opportunities: A Brief Study of GFP Students in Muscat College

This article presents the results of a study on the effects of COVID-19 and the changes that crept into English Language Teaching of EFL learners of GFP program in Muscat College, Oman. The study illustrates that Language Teaching as we know underwent a dramatic change due to the unforeseen and unpredictable situation that presented in the form of COVID-19 and the changes that had to be made to curriculum, pedagogy, evaluation of students’ performance while at the same time maintaining the quality of teaching and sustained enthusiasm of the learners. The analysis of students’ summative evaluation results clearly show that a majority of students showed remarkable affinity to learning through the methods employed by teachers using online applications such as Microsoft Teams, Moodle, etc. However, the task at hand for both the teachers and students was not easy. Many students did not have a good WiFi connection, some had difficulty in accessing the network, while some did not have necessary equipment at home, so they were forced to adapt to a varied ways of learning and so on. Though it seems like problems galore, nevertheless almost all the students were able to take their final evaluation online, which proves that if implemented rightly and with little flexibility on the part of both the learner and the instructor, online learning of English Language during the initial part of lockdown proved to be a success in Muscat College.
ПІДГОТОВКА МАЙБУТНІХ ІНЖЕНЕРІВ-ПРОГРАМІСТІВ ДО ТЕСТУВАННЯ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ В УМОВАХ НЕФОРМАЛЬНОЇ ОСВІТИ: ПРОБЛЕМИ ТА ШЛЯХИ УСПІШНОЇ РЕАЛІЗАЦІЇ

This article discusses the problem of preparing student programmers for the process of software testing in real production conditions. It reveals the relevance of the field of software development in general and the testing process in particular, gives a characteristic of the latter. An analysis of the requirements put forward by employers to young specialists-testers is carried out, the main ones are highlighted: knowledge of specific theoretical aspects of activities, the availability of project experience, a high level of development of soft skills. The paper examines the international experience of training software engineers, which highlights special attention of foreign programs to the practices of non-formal education, in particular, to conducting industrial practice at the expense of the resources and programs of partner enterprises, as well as taking into account international certificates of training programs in the assessment of disciplines. In the final part of the work, a description of the concept of non-formal education is given, features of its legal status in Ukraine are highlighted, characteristics of its main features are given, such as the use of innovative teaching methods, flexibility and modifiability, implementation on the basis of specialized or independent institutions. The final part of the article examines the issue of the relevance of non-formal forms of education in the context of training future software engineers for software testing in a real project environment. An analysis of the existing commercial practices present in the educational space of Ukraine is carried out, conclusions are drawn regarding the possibility of using informal training practices within the formal programs of domestic universities.
ENGAJAMENTO DO PROFESSOR EM HOME-OFFICE DEVIDO AO SÚBITO ISOLAMENTO PROVOCADO PELA PANDÊMICA DO COVID-19

This article presents a proposal for the engagement of teachers who are working at home-office due to the social confinement caused by the Covid-19 pandemic.This isolation causes discomfort in many teachers because they are at home, away from face-to-face professional life.To minimize the effects of this isolation, a school created a weekly online meeting, called "Encontro das Canecas", to discuss topics related to the teacher's well-being, to technical aspects of mediation of classes by technology and socio-behavioral attitudes.These meetings were recorded, which allowed an analysis of the topics covered.In this article will be presented the analysis of the data of the recordings in addition to data from a survey carried out with the teachers after the closing of the cycle of meetings of the first semester.The dynamics adopted and the results can help other engineering education institutions to promote meetings of this type, improving the online working conditions that we are currently experiencing.
Current Trends for Blockchain and Smart Contracts (In Financial Services)

The most popular virtual currency (bitcoin) has become so popular because it relies on the concept of Distributed Ledger, realized by means of a technology known as BlockChain. This technology removes the need for third parties (such as authorities) that authorize transactions; in fact, it is a peer-to-peer network where consensus to transactions is given by peers. Furthermore, the distributed nature of BlockChain ensures a high-level of robustness to attacks. However, since its birth (a decade ago) many evolutions have been introduced: now, we distinguish between permissionless and permissioned BlockChains, the concept of smart contract is now supported, and various platforms are available. The contribution of this paper is to provide novices with the current trends, in particular by discussing the adoption of BlockChain technology in financial services.This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Assessment of the intensity and flow of straight solar radiation falling onto the horizontal surface of a room

Abstract The paper presents the results of experimental studies of straight solar radiation in the Central Chernozem region of Russia. The intensity and flow of straight solar radiation falling onto a horizontal surface of a room were evaluated. Measurements were made of the flow of straight solar radiation on a horizontal surface through a translucent structure. Experimental data were obtained using a high-class measuring device in the period from July 1 to August 1. The measurement results showed an intense wave-like change in the solar radiation flux over 8 hours. The paper describes a method for estimating the intensity and flow of straight solar radiation falling onto a horizontal surface of a room. This method of evaluating daytime radiation allows one to determine the density of the total and straight radiation flux. Using this estimate to calculate solar installations used in the Central Chernozem region, their efficiency will increase by 10%.
The Truth Matters: A Citizen's Guide to Separating Facts from Lies and Stopping Fake News in its Tracks

This is a book review of The Truth Matters: A Citizen’s Guide to Separating Facts from Lies and Stopping Fake News in Its Tracks by Bruce Bartlett.
DALE : Dark Region-Aware Low-light Image Enhancement.

In this paper, we present a novel low-light image enhancement method called dark region-aware low-light image enhancement (DALE), where dark regions are accurately recognized by the proposed visual attention module and their brightness are intensively enhanced. Our method can estimate the visual attention in an efficient manner using super-pixels without any complicated process. Thus, the method can preserve the color, tone, and brightness of original images and prevents normally illuminated areas of the images from being saturated and distorted. Experimental results show that our method accurately identifies dark regions via the proposed visual attention, and qualitatively and quantitatively outperforms state-of-the-art methods.
Counterfactual Planning in AGI Systems

We present counterfactual planning as a design approach for creating a range of safety mechanisms that can be applied in hypothetical future AI systems which have Artificial General Intelligence. The key step in counterfactual planning is to use an AGI machine learning system to construct a counterfactual world model, designed to be different from the real world the system is in. A counterfactual planning agent determines the action that best maximizes expected utility in this counterfactual planning world, and then performs the same action in the real world. We use counterfactual planning to construct an AGI agent emergency stop button, and a safety interlock that will automatically stop the agent before it undergoes an intelligence explosion. We also construct an agent with an input terminal that can be used by humans to iteratively improve the agent's reward function, where the incentive for the agent to manipulate this improvement process is suppressed. As an example of counterfactual planning in a non-agent AGI system, we construct a counterfactual oracle. As a design approach, counterfactual planning is built around the use of a graphical notation for defining mathematical counterfactuals. This two-diagram notation also provides a compact and readable language for reasoning about the complex types of self-referencing and indirect representation which are typically present inside machine learning agents.
Avant-garde and Experimentalisms in Poetry for Children from Rodari to the Present Day: a Travel between Authors and Works

In 1985 Franco Fortini wrote provocatively that poetry for children does not exist, arguing that it is alien to their ability to fully grasp its expressive significance. However, there had already been the experience of Gianni Rodari, from whose contribution the critical and historiographic reflection on an authorial (and quality) poetic word addressed to childhood cannot be ignored: he had placed linguistic game at the center of poetry for children, combining the lesson of French surrealism with the Italian futurist experience of Palazzeschi and he consciously placed himself along that modern poetic line that sees a flowering of great importance especially in the post-war period. It is a type of poetry that is enriched by the contribution of a playful and divergent dimension and that knows how to speak the language of children: the rhythm, the assonance, the rhyme, but also the associative procedures grafted through the surrealist techniques naturally meet the child animus and at the same time open to an articulated, plural dimension, rich in ethical and political tension. Along this trajectory of linguistic experimentalism masterfully inaugurated by Rodari, the contribution intends to identify the paths of other authors in the proposal of a poetry aimed at children that is innovative and valid on a content and formal level: the reference is to Roberto Piumini and Pietro Formentini in particular, who have recognized, in poetry for children and young people, a vast and welcoming space for exploration and expressive freedom, but also, more recently, to the refined research of poetesses such as Chiara Carminati and Silvia Vecchini, whose poetic production is constantly embellished by a reflective work of undoubted charm and of notable interest for the investigation on a literary, aesthetic, educational level.
Upaya Meningkatkan Hasil Belajar Siswa Kelas IV Melalui Penggunaan Media Visual di SDN 3 Sukarasa

This research was conducted at SDN 03 Sukarasa on the material on the mind map material from nonfiction stories. This study uses a classroom action research method which is divided into 3 learning cycles. The research aims to improve student learning outcomes through learning using visual media. This study was applied to 8 samples with the duration of the study consisting of 6 meetings. Based on the value analysis shows that there is an increase in student learning outcomes both from cycle I, cycle II, and cycle III. This is because in cycle II learning activities by adding learning media in the form of visual media have a good effect on student learning outcomes, this is evidenced by the increase in the mean value of students. Likewise with the addition of learning media in the form of pictures and learning videos and discussions, question and answer with students in learning cycle III has had a very good influence on student learning outcomes, understanding of the subject matter being taught and the activeness of students and providing a different atmosphere in activities learning.