Reviewerly/exHarmony huggingface dataset:
Development of Mathematics Mobile Learning Application on Android-based Smartphone Using Social Aritmetics

Abstract Great interest in the use of mobile phones, especially types of smartphones can be utilized in the field of education and given that most internet users are students, the development of applications to help implement learning or known as mobile learning will be a good prospect. The aim of this research is to describe the development process and produce the Mathematics Mobile Learning application on Android-based Smartphones with Social Arithmetic material in good quality. This study refers to the development model according to Plomp (2013) which consists of three stages, namely (1) preliminary research, (2) prototyping phase, (3) assessment phase. The application developed is tested in a limited way to six seventh grade students with different schools. The application developed is in the good category, because it fulfills the criteria of valid, practical, and effective. The results of the application validation developed were declared valid because it obtained a total average score of 2.96. The application developed fulfill theoretically practical criteria because the application is declared valid from the validators and empirically practical because the response questionnaire filled in by the research subject is 81.5% where based on the practicality criteria of the media, it can be stated that the application as learning media can be used without revision. In addition, the application developed also fulfills the effective criteria because all students who are subjects receive score minimum 78 so that it can be stated that the application developed is very effective. Keywords: Learning Media, Social Arithmetic, Mathematics Mobile Learning.
Coding with Purpose: Learning AI in Rural California.

We use an autoethnographic case study of a Latinx high school student from an agricultural community in California to highlight how AI is learned outside classrooms and how her personal background influenced her social-justice oriented applications of AI technologies. Applying the concept of learning pathways from the learning sciences, we argue that redesigning AI education to be more inclusive with respect to socioeconomic status, ethnoracial identity, and gender is important in the development of computational projects that address social-injustice. We also learn about the role of institutions, power structures, and community as they relate to her journey of learning and applying AI. The future of AI, its potential to address issues of social injustice and limiting the negative consequences of its use, will depend on the participation and voice of students from the most vulnerable communities.
RANCANGAN SISTEM INFORMASI ANEMIA PADA PLATFORM ONLINE “SRIKANDI HEALTH” BERBASIS PROGRESSIVE WEB APPS

Anemia is one of the nutritional problems that has the potential to hinder the development of the quality of Indonesian human resources. The support of science and information technology in the form of a website application can be a solution to facilitate education, monitoring and management of anemia so as to accelerate the handling of anemia. This study aims to design an anemia information system in the form of an online platform with the name "Srikandi Health" based on Progressive Web Apps to support the prevention of anemia in Indonesia in Indonesia. This research was conducted in December 2020 in Bantul, Yogyakarta. This study uses the prototype method. The stages of the prototype method start from gathering requirements, building prototyping, and evaluating protoptyping. The result of this research is the prototype of the online application "Srikandi Health" based on Progressive Web Apps. Conclusion Srikandi Health prototype is designed to meet the needs analysis of the system, namely to facilitate monitoring and prevention of anemia in adolescents.The suggestion of this research is the implementation of the system so that the system becomes more complete and can be applied to the wider community.
Reading Interests of Class VIII Students of SMP Negeri 1 Watansoppeng on Narrative Texts in Bugis Language

This research is a survey research. Surveys are used to collect correct data or information using a relatively small sample. The purpose of this study was to describe the reading interest of class VIII of SMP Negeri 1 Watansoppeng on the narrative text of Bugis language. This research was conducted on September 13 - October 11 in the odd semester of 2020/2021. The population in this study were all students of class VIII SMP Negeri 1 Watansoppeng, totaling 192 students. Sampling in this study used the Krejcie and Morgan tables. The sample in this study were 127 students of class VIII SMP Negeri 1 Watansoppeng. The data collection technique used was to provide online questionnaires via Google Form to students. Reading Interests of Class VIII Students of SMP Negeri 1 Watansoppeng towards Narrative Texts in Bugis language are in the medium category with a percentage value of 64.6% (82 students) of 127 students. This means that basically students like reading narrative text in Bugis language, but more because in the context of doing school work, not because they have reading interest in the narrative text in Bugis language
PEMBUATAN WEB SERVICE DENGAN GOOGLE SPREADSHEETS SEBAGAI SOLUSI INTEGRASI APLIKASI MULTIPLATFORM

Multiplatform application development is an unavoidable technological demand. Startup industries are increasingly emerging by offering convenience from various aspects of life, retail, finance, green life, artificial intelligence to the Internet of Things. This is in line with the increasing growth of innovation both systemically and technically, it does not rule out the possibility that these innovations include ways to use the minimum resources possible to produce the maximum possible output. The use of the Restfull API is a good solution to solve this problem, the output, which can be a JSON file, can be communicated with various application platforms. This study will review the use of popular applications such as Google Spreadsheets to become a media provider for web services. This method is implemented in the process of updating student data at the Nurdin Hamzah University in the Dikti Feeder.
Implementasi Waterfall dan Agile dalam Perancangan E-Commerce Alat Musik Berbasis Website

The development of Information Technology (IT) is growing very rapidly. The rapid development of IT makes internet technology the main communication tool that is in great demand by the public. With the internet technology will facilitate and speed up the search for information, information is very important in supporting the course of a company to achieve the desired goals. Online transactions are a necessity that answers the problems that arise today. In online transactions, a person can freely make purchases, sales and find things he wants to do without having to meet with buyers or sellers through laptops, computer devices, or smartphones. For this reason, the authors design a web-based musical instrument sales system so that people can easily get information about selling musical instruments online quickly and precisely both regarding products, profiles, prices, and also how to shop at online stores and can directly make sales transactions. The author makes the design of this information system using UML (Unified Modeling Language), the Waterfall method, and Agile the author implements to create a website-based E-commerce application. As a result, users can shop and view information about musical instruments.
Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection

This work tackles the unsupervised cross-domain object detection problem which aims to generalize a pre-trained object detector to a new target domain without labels. We propose an uncertainty-aware model adaptation method, which is based on two motivations: 1) the estimation and exploitation of model uncertainty in a new domain is critical for reliable domain adaptation; and 2) the joint alignment of distributions for inputs (feature alignment) and outputs (self-training) is needed. To this end, we compose a Bayesian CNN-based framework for uncertainty estimation in object detection, and propose an algorithm for generation of uncertainty-aware pseudo-labels. We also devise a scheme for joint feature alignment and self-training of the object detection model with uncertainty-aware pseudo-labels. Experiments on multiple cross-domain object detection benchmarks show that our proposed method achieves state-of-the-art performance.
Evaluating the Performance and Ability Explain of Market Index Returns by Selected Stock Portfolios Based on Throughput Accounting Criteria in Comparison with the New Network Matrix Model

One of the strategies used in active portfolio management is the matrix model which can be used to form different portfolios with different characteristics of stocks or companies. In this study, with the data of 156 companies listed on the Tehran Stock Exchange during the period 2011 to 2018 using the network matrix model and based on throughput accounting criteria, portfolio formation and their performance with the portfolios of the new network matrix model (Defensive, neutral and aggressive stocks) and market portfolio were compared. The results show that the proposed network matrix model portfolios based on throughput accounting criteria have higher performance than the new network matrix model in terms of Sharpe, Sortino, upside potential, and omega criteria. Also, portfolios consisting of stocks of companies with high system performance, in addition to the above criteria, have higher performance in terms of Jensen's Alpha criteria than the new network matrix model, and in terms of upside potential and omega criteria, have higher performance than the market portfolio. The performance of portfolios consisting of stocks of companies with low system performance has a stronger correlation with the market portfolio compared to the new network matrix model.
Thoreau’s Social World

One of the most popularly salient images of Thoreau taken from Walden is Thoreau as an asocial hermit, whether an honest one or a hypocrite. The Honest Hermit view sees his time in the woods as a praiseworthy retreat from other people in pursuit of higher values among nature. The Hypocrite Hermit view takes his forays into town – and the assumption that his mother did his laundry especially – as a sign that while he aimed to look holy out there in his hermitage, and would have been holy if he had achieved what he claimed, he did not live up to his own standard.
Spatial Judgments in Impossible Spaces Preserve Important Relative Information

In virtual reality (VR), Impossible Spaces allow rooms to overlap each other in physical space, enabling developers to better utilize the limited space available for VR systems. Prior work has explored detect thresholds for an impossible spaces, but little work has considered how impossible spaces affect users’ understandings of spatial relationships within virtual environments. We present a study evaluating how impossible spaces affected participants’ judgments of a room’s width, and how this was impacted by whether participants considered each room individually, or within the context of the entire space. Participants’ judgments of single rooms was not impacted by being in an impossible space, however judgments were significantly smaller when considering an impossible space as a whole. Even so, participants’ judgments preserved the respective ratio between overlapping rooms, indicating that the relative sizes of different rooms is preserved in impossible spaces. This suggests that while absolute spatial information may be disrupted by impossible spaces, important relative information can be preserved. However, it is not yet clear how much of this effect can be attributed to lower-level perception and higher-level cognition.
Predicting user movement frequent patterns in mobile service environment

Mobile users can be attracted towards the services through their mobile gadgets by means of “Information Service and Application Provider (ISAP)” anytime around the world. When customers move within the cell network, their carrier requirements based totally on the locations use to be tracked in a central mobile user transactional database. The systems that provide mobile service offer users valuable data through mobile gadgets. Depending on volatile user movement behavioral patterns, mobile provider structures have the potential of successfully mining a unique request from abundant information. In this paper, consumer movement and conduct patterns are studied with respect to the hassle of predicting identical cell access styles based on joining the subsequent three forms of characteristics: location (L), timestamp (T), and services (S). Traditional cell carrier structures are inadequate in handling delicate consumer movement conduct pattern without taking L, T, and S. In proposed system of this paper, FP-Growth set of rules is used to locate the frequent styles. It stores the frequent patterns in a tree and hash statistics structure to calculate the prediction ratio. Prediction ratio is used to locate the match of asked services.
Напрями діяльності клубних об’єднань педагогічних ЗВО України

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
The relationship between interpersonal intelligence and linguistic intelligence with mathematics learning achievement in high school students

In teaching, the teacher emphasizes identifying the dominant intelligence and learning styles to improve student achievement. However, exploring interpersonal and linguistic intelligence and its relationship to student achievement is still rarely done. This correlational research was conducted to describe the relationship between interpersonal and linguistic intelligence with high school students' mathematics learning achievement. The samples of this study were students of class XI SMA Budi Dharma Cancar Indonesia (n=30). The data were collected using a questionnaire and the pure grades of the students' midterm exams. The data analysis technique uses Product Moment Correlation as a statistical test tool to test the hypothesis of the relationship between two variables after fulfilling the prerequisite test. The results showed a positive relationship between interpersonal and linguistic intelligence and students' mathematics learning achievement. Although this study was limited to two bits of intelligence, it is essential to note that the findings of this study indicate that the number of intelligence at the dominant level can predict learning achievement in school.
Significant Contribution in Healthcare by using IoT

Nowadays, the heart-related disease is rapidly rising. Most patients, in some cases, may not identify their health condition. Even in rural areas, doctors are not available 24x7. Due to the advancement of modern technology, these diseases can be detected early and can be treated on time. So, the developed device can continuously track the patient's heartbeat and temperature, and the recorded data can be sent to the concerned doctor so that treatment can be provided to the patient. Here the IoT (Internet of Things) is being used for monitoring the patient's health status and send wirelessly to the IoT server. This device can also be monitored from remote places. This device is mainly helpful for aged and disabled people who find it difficult to go to doctors daily or for patients who need continuous monitoring of their health status. The designed hardware will capture the real-time heartrate and temperature value and send the data to the concern IoT server using any mobile or Wi-Fi network having with internet facility. Before sending the data the preprocessing is being done by the attached microcontroller in the respective sensors module. The value sent in the IoT server will be used for generating the online graph through the Application Program Interface (API) that are used in developing the web and mobile application. In this system the authenticated user can view the output trough developed mobile application web application.
STUDENTS’ PERCEPTION OF LANGUAGE POLITENESS IN AN ONLINE LEARNING

This research aims to describe students’ perception of language politeness in an online learning. The method used in this research was qualitative descriptive. The population of the research was all undergraduate students of General English subject in a private university in east Jakarta. The sample was taken using purposive sampling which focuses on first semester students, in this case 116 respondents. Data collection was taken by giving questionnaire through Google Forms. The questionnaire was made using verbal indicators such as language style, deixis, punctuation and six maxims of politeness by Leech and non-verbal features such as attire and paralinguistic aspects. The result shows if in general, the students’ perception to politeness in an online learning dominantly showing positive perception.
Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization

Risk management is critical in decision making, and mean-variance (MV) trade-off is one of the most common criteria. However, in reinforcement learning (RL) for sequential decision making under uncertainty, most of the existing methods for MV control suffer from computational difficulties caused by the double sampling problem. In this paper, in contrast to strict MV control, we consider learning MV efficient policies that achieve Pareto efficiency regarding MV trade-off. To achieve this purpose, we train an agent to maximize the expected quadratic utility function, a common objective of risk management in finance and economics. We call our approach direct expected quadratic utility maximization (EQUM). The EQUM does not suffer from the double sampling issue because it does not include gradient estimation of variance. We confirm that the maximizer of the objective in the EQUM directly corresponds to an MV efficient policy under a certain condition. We conduct experiments with benchmark settings to demonstrate the effectiveness of the EQUM.
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference

While recent research on natural language inference has considerably benefited from large annotated datasets, the amount of inference-related knowledge (including commonsense) provided in the annotated data is still rather limited. There have been two lines of approaches that can be used to further address the limitation: (1) unsupervised pretraining can leverage knowledge in much larger unstructured text data; (2) structured (often human-curated) knowledge has started to be considered in neural-network-based models for NLI. An immediate question is whether these two approaches complement each other, or how to develop models that can bring together their advantages. In this paper, we propose models that leverage structured knowledge in different components of pre-trained models. Our results show that the proposed models perform better than previous BERT-based state-of-the-art models. Although our models are proposed for NLI, they can be easily extended to other sentence or sentence-pair classification problems.
Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows

One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal models. In this work, we propose a method for estimating bivariate structural causal models using a combination of normalising flows applied to density estimation and variational Gaussian process regression for post-nonlinear models. It facilitates causal discovery, i.e. distinguishing cause and effect, by either the independence of cause and residual or a likelihood ratio test. Our method which estimates post-nonlinear models can better explain a variety of real-world cause-effect pairs than a simple additive noise model. Though it remains difficult to exploit this benefit regarding all pairs from the T\"ubingen benchmark database, we demonstrate that combining the additive noise model approach with our method significantly enhances causal discovery.
CLOUD COMPUTING ENVIRONMENTS

Cloud environment providers have setup several data centers at different geographical locations over the Internet to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centres to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (Inter Cloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. graphical, hosting application
Feature Importance in a Deep Learning Climate Emulator

We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically, we consider a multiple-input-single-output emulator that uses a DenseNet encoder-decoder architecture and is trained to predict interannual variations of sea surface temperature (SST) at 1, 6, and 9 month lead times using the preceding 36 months of (appropriately filtered) SST data. First, feature importance methods are employed for individual predictions to spatio-temporally identify input features that are important for model prediction at chosen geographical regions and chosen prediction lead times. In a second step, we also examine the behavior of feature importance in a generalized sense by considering an aggregation of the importance heatmaps over training samples. We find that: 1) the climate emulator's prediction at any geographical location depends dominantly on a small neighborhood around it; 2) the longer the prediction lead time, the further back the "importance" extends; and 3) to leading order, the temporal decay of "importance" is independent of geographical location. An ablation experiment is adopted to verify the findings. From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider. From the perspective of network architecture, the spatio-temporal relations between the inputs and outputs we find suggest potential model refinements. We discuss further extensions of our methods, some of which we are considering in ongoing work.