Reviewerly/exHarmony huggingface dataset:
The Relation of Indonesian Language Learning to the Environment

Learning Indonesian is very closely related to complete grammar and right. This requires student to be active and creative in learning. Student's activity in learning is very necessary to see language development student, even though in reality student's language acquisition does not start from learning Indonesian. Based on the questionnaire that the author distributed regarding "TheRelation of Indonesian Language Learning to the Environment” to Padang State University students, Medan State Polytechnic, Ministry of Health Health Polytechnic of the Republic of Indonesia Padang, Jambi University, and Andalas University. Of the total number of 39 respondents who took part in filling out the questionnaire. From filling out the questionnaire, it was dominated by women, namely 32 people with the percentage of 82.1% and men as many as 7 people with a total percentage of 17.9%. Results data from the percentage of questionnaires that have been distributedand filled out by respondents conducted through google form can be seen as follows. According to the author, students as respondents have knowledge that can be categorized as good awareness of the role of the environment in learning Indonesian even though the background is different majors. This can be a picture forthe teacher to teach environment-based language learning with a supportive environment and motivate students to make it easier to understand creative Indonesian language material, innovative and useful for life.
Dimension Analysis Of The Emergency Spillway Of Tirawan Dam With The Application Of The System Dynamic Model

Tirawan Dam emergency spillway is planned to be able to drain the initial flood discharge when the gate above the main spillway is closed so that there will be sufficient time to open the gate to pass the peak flood discharge through the spillway safely. The emergency spillway elevation is designed to accommodate maximum water as a natural reserve during the dry season. This study aims to analyze the factors that play a role in emergency spillway design in meeting the availability of optimal raw water storage and increasing the safety factor against flooding using a simulation modeling method with the help of the Vensim PLE program. The modeling is made in three stages. The first is the analysis of the system model, the second is the creation of a caustic diagram, and the third stage is the simulation of the model and scenario model. The simulation results of this model scenario obtained an effective emergency spillway elevation of +69.50 m and a width of 20 m. The spillway can pass the Q1000 design flood without the control gate operation with a freeboard of 0.41 m with a raw water reserve of 240,649.69 m3. From the simulation results, it is also known that the operation of the main spillway serves to add a safety factor to the discharge capacity of the flood discharge during an emergency condition and cannot be operated to optimize the reservoir.
Complementary descriptions of Euler characteristics of simplicial incidence pseudographs

Abstract We introduce incidence pseudographs and simplicial incidence pseudographs with a poset topology, which provide discrete representation of continuous objects and helpful tools in digital image analysis. We prove closed or open sub-pseudograph matching theorems in a regular incidence pseudograph. By embedding a finite simplicial incidence pseudograph into an optimal simplicial incidence pseudograph, we obtain several formulae which give complementary descriptions of Euler characteristics of finite simplicial incidence pseudographs and its relative pseudographs. These descriptions provide new methods of calculating Euler characteristics by boundary counts rather than all nodes and make it possible, in some cases, to simplify the complexity of computing Euler characteristic of finite simplicial incidence pseudographs.
A Novel Technique to Defraud Credit Card by Handling Class Imbalance Problem Using Machine Learning

Typically, classification algorithms work poorly when confronted with unbalanced datasets, and the resulting effects are skewed against the majority class. As a result, an effective model is required to identify unbalanced data, particularly in the context of fraud detection. For these types of issues, the classifier's accuracy is not trusted because the cost of predicting a fraud sample as a non-fraud sample is extremely high. In general, imbalanced learning happens when some types of data distributions significantly outnumber other data distributions in the instance space. There is a need of technique such as under sampling or oversampling in order to learn from unbalanced datasets. A novel over sampling method has been suggested for learning from unbalanced datasets in this paper. The basic impression here is that a weighted distribution for diverse outnumbered class instances has been utilized depending on their degree of complexity to learn, with more pretended evidence leading to the outnumbered ones, being more troublesome to learn. As a result, with regard to data distributions, the suggested approach improves learning first by bringing down the bias familiarized using class difference, and then by pliantly conveying the classification judgement boundary toward challenging instances.
Appraisal of high-stake examinations during SARS-CoV-2 emergency with responsible and transparent AI: Evidence of fair and detrimental assessment

In situations like the coronavirus pandemic, colleges and universities are forced to limit their offline and regular academic activities. Extended postponement of high-stakes exams due to health risk hereby reduces productivity and progress in later years. Several countries decided to organize the exams online. Since many other countries with large education boards had an inadequate infrastructure and insufficient resources during the emergency, education policy experts considered a solution to simultaneously protect public health and fully resume high-stakes exams -by canceling offline exam and introducing a uniform assessment process to be followed across the states and education boards. This research proposes a novel system using an AI model to accomplish the complex task of evaluating all students across education boards with maximum level of fairness and analyzes the ability to fairly appraise exam grades in the context of high-stakes examinations during SARS-CoV-2 emergency. Basically, a logistic regression classifier on top of a deep neural network is used to output predictions that are as fair as possible for all learners. The predictions of the proposed grade-awarding system are explained by the SHAP (SHapley Additive exPlanations) framework. SHAP allowed to identify the features of the students' portfolios that contributed most to the predicted grades. In the setting of an empirical analysis in one of the largest education systems in the Global South, 81.85% of learners were assigned fair scores while 3.12% of the scores were significantly smaller than the actual grades, which would have had a detrimental effect if it had been applied for real. Furthermore, SHAP allows policy-makers to debug the predictive model by identifying and measuring the importance of the factors involved in the model's final decision and removing those features that should not play a role in the model's “reasoning” process.
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled data. These tasks are particularly interesting in an unsupervised setting due to the difficulty and cost of obtaining dense image annotations, but existing unsupervised approaches struggle with complex scenes containing multiple objects. Differently from existing methods, which are purely based on deep learning, we take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. Specifically, we examine the eigenvectors of the Laplacian of a feature affinity matrix from self-supervised networks. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. Furthermore, by clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions, i.e. semantic segmentations. Experiments on complex datasets (Pascal VOC, MS-COCO) demonstrate that our simple spectral method outperforms the state-of-the-art in unsupervised localization and segmentation by a significant margin. Furthermore, our method can be readily used for a variety of complex image editing tasks, such as background removal and compositing.
EDUCATIONAL AND CREATIVE MANAGEMENT TECHNOLOGY ACTIVITIES OF STUDENTS IN EXTRACURRICULAR INSTITUTIONS

The authors point out the main principles of management of pupils’ activities, came to the conclusion about a great meaning of mechanism of self-governing in this process. The authors present the technology of management of pupils’ studies and creative activities as the totality of stages and ways of activities corresponding to them, give examples of using rating system in the out-of-school institutions.
Clustering and Forecasting of Covid-19 Data in Indonesia

Indonesia reported its first case of Covid-19 in March 2020, which was suspected to have been infected by a foreigner who visited Indonesia. The distribution of cases that occurred in Indonesia has an uneven frequency considering that Indonesia is an archipelagic country, in the analysis of Covid-19 cases in Indonesia, there are many provinces and some have the same pattern of case characteristics. time series so that forecasting analysis can be used. So that clustering analysis and forecasting of Covid-19 data can be used in Indonesia. The analysis was carried out with 2 stages of analysis, namely clusters using the clustering hierarchy method and forecasting using the ARIMA method. By using 288 data from January 1, 2021 – October 15, 2021, the results show that the daily Covid-19 cases by province in Indonesia can be grouped into 2 clusters, in the forecasting analysis only one province is taken from each cluster used in determining the model, cluster 1 used data from the province of Banten and cluster 2 used data from the province of West Java. By using R software, a model for each cluster is obtained, namely ARIMA(0,1,1) for cluster 1 and ARIMA(2,1,2) for cluster 2. From the forecasting results obtained data until October 30, 2021 shows the number of cases tends to be constant.
Emotion Detection Based on Sentiment Analysis: An Example of a Social Robots on Short and Long Texts Conversation

Purpose:The aim of this paper is to present a solution to detect emotions from text obtained in a conversation with a social robot.Emotions will be detected using sentiment analysis based on the English and Polish lexicon.Design/methodology/approach: Data from social robot conversation records will be converted into text and then split into short and long speech.The original language utterances will then be analysed using the Polish lexicon, while the translated texts will be analysed using the English emotional lexicon.Findings: The results obtained indicate the same or similar distribution of emotions made by sentiment analysis using both plNetWord and NRC lexicons.Practical Implications: The results obtained can be used for further research addressing the creation and development of lexicons based on the selected language.They are also applicable to the implementation of solutions for detecting and responding to conversational emotions by social robots.Originality/Value: The analyses so far mostly take up the subject of textual analysis in English.The aim of the present study is to analyse a Polish text and to compare the results obtained with those for English texts.The analysis of differences in the emotional sentiment of utterances may lead to the construction of more effective models based on the chosen language.
LMix:Regularization Strategy for Convolutional Neural Networks

Abstract Convolutional neural network models, as well as the training samples necessary, have grown in size in recent years. Mixed Sample Data Augmentation is provided to further improve the model's performance, and it has yielded good results.Mixed Sample Data Augmentation allows the network to generalize more effectively and improves the baseline performance of the model. The mixed sample strategies proposed so far can be broadly classified into interpolation and masking. However, interpolation-based strategies distort the data distribution, while masking-based strategies can obscure too much information. Although Mixed Sample Data Augmentation has been proven to be a viable technique for boosting deep convolutional model baseline performance, generalization ability, and robustness, there is still room for improvement in terms of image local consistency and data distribution.In this research, we present a new Mixed Sample Data Augmentation that uses random masking to increase the number of image masks while retaining the data distribution and high-frequency filtering to sharpen the images in order to emphasize recognition regions. Our experiments on CIFAR-10, CIFAR-100, Fashion-MNIST, SVHN, and Tiny-ImageNet datasets show that the LMix improves the generalization ability of state-of-the-art neural network architectures. And our method enhances the robustness of adversarial samples.
The Use of An Accounting Information System for Monitoring Website-Based Sales Call Activities in Garment Company

There are several ways that can be used to increase sales, one of which is using a sales call. At PT. Ricky Mumbul Daya, sales call is one of the main activities carried out to increase sales. Although the process of recording revenues and costs related to sales call activities at that company has used the system, but the process is still not optimal. This is because the data input process is only done by the cashier. Meanwhile, the monitoring process is still done manually. The purpose of this research is to create a Website-Based Accounting Information System for Sales Call Monitoring and tested whether the level of performance and effort expectations of the system has an influence on user behavioral intention. The system development method used is the System Development Life Cycle. Meanwhile, to test the level of the three variables, it will begin with collecting data with a questionnaire and then the data will be analyzed using several testing methods. The result of this research is that the information system created can complete the expected tasks and concluded that the level of performance and effort expectations of the system have an influence on the user’s behavioral intention.
ENHANCED SUPER RESOLUTION FOR REMOTE SENSING IMAGERIES

Abstract. Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.
Towards Equal Opportunity Fairness through Adversarial Learning

Adversarial training is a common approach for bias mitigation in natural language processing. Although most work on debiasing is motivated by equal opportunity, it is not explicitly captured in standard adversarial training. In this paper, we propose an augmented discriminator for adversarial training, which takes the target class as input to create richer features and more explicitly model equal opportunity. Experimental results over two datasets show that our method substantially improves over standard adversarial debiasing methods, in terms of the performance--fairness trade-off.
Gender Bias in Occupation Classification from the New York Times Obituaries

Technological developments such as artificial intelligence can strengthen social prejudices prevailing in society, regardless of the developer's intention.Therefore, researchers should be aware of the ethical issues that may arise from a developed product/solution.In this study, we investigate the effect of gender bias on occupational classification.For this purpose, a new dataset was created by collecting obituaries from the New York Times website and is provided in two different versions: With and without gender indicators.Category distributions from this dataset show that gender and occupation variables have dependence.Thus, gender affects occupation classification.To test the effect, we perform occupation classification using SVM (Support Vector Machine), HAN (Hierarchical Attention Network), and DistilBERT-based classifiers.Moreover, to get further insights into the relationship of gender and occupation in classification problems, a multi-tasking model in which occupation and gender are learned together is evaluated.Experimental results reveal that there is a gender bias in job classification.
CDEdit: A Highly Applicable Redactable Blockchain with Controllable Editing Privilege and Diversified Editing Types

Redactable blockchains allow modifiers or voting committees with modification privileges to edit the data on the chain. Trapdoor holders in chameleon-based hash redactable blockchains can quickly compute hash collisions for arbitrary data, and without breaking the link of the hash-chain. However, chameleon-based hash redactable blockchain schemes have difficulty solving the problem of multi-level editing requests and competing for modification privileges. In this paper, we propose CDEdit, a highly applicable redactable blockchain with controllable editing privilege and diversified editing types. The proposed scheme increases the cost of invalid or malicious requests by paying the deposit on each edit request. At the same time, the editing privilege is subdivided into request, modification, and verification privileges, and the modification privilege token is distributed efficiently to prevent the abuse of the modification privilege and collusion attacks. We use chameleon hashes with ephemeral trapdoor (CHET) and ciphertext policy attribute-based encryption (CP-ABE) to implement two editing types of transaction-level and block-level, and present a practical instantiation and security analysis. Finally, the implementation and evaluation show that our scheme only costs low-performance overhead and is suitable for multi-level editing requests and modification privilege competition scenarios.
COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY

The number of rainy days is a calculation of the rainy days that occur in one month. In recent years, there has been a decrease in rainy days in some parts of Indonesia. One of the areas at risk of quite a high decreasing number of rainy days is the Bengkulu City area. The decrease in the number of rainy days is one of the impacts caused by climate change. The community will feel the impact of climate change-related to the season, especially those working in the agricultural sector. In compiling the planting calendar, it is necessary to consider the seasons to estimate water availability. This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. The criteria for selecting the best model used was Mean Absolute Deviation (MAD). The MAD value in the SARIMA method was 4,16, 5,07 in the SVR model, and 3,67 in the GA-SVR model. Based on these results, it can be concluded that the GA-SVR model is the best model for forecasting the number of rainy days in Bengkulu City.
TEACHING STRATEGY IN ENRICHING THE EFL STUDENTS’ VOCABULARY THROUGH LITERATURE

Vocabulary acquisition is one of the important elements that can support the students’ language proficiency. It helps students to follow and enjoy the teaching content delivered by their teachers. The educator of the EFL context, particularly at lower secondary school, attempts to design the teaching material which is oriented to enrich the students’ vocabulary acquisition. The research discovers that various ways can be used to build students’ vocabulary. One of them is literature. Therefore, this research aims to investigate can English literary works be integrated with the teaching strategies to develop the EFL students’ vocabulary. To observe it, the researchers use library research to find and analyze some sources (e.g. articles, proceedings, books) as the collected data, which discuss enriching English vocabulary through literature. The result shows that two things must be considered if teachers would like to incorporate literary works in their content. 1) Types of literary works used by teachers to enhance their learners’ vocabulary, 2) Teacher’s teaching strategy to make the EFL learners memorize the unrecognize words.