Reviewerly/exHarmony huggingface dataset:
PENERAPAN METODE K-MEANS UNTUK EVALUASI KINERJA KARYAWAN DI PT. PLN UNIT INDUK DISTRIBUSI BANTEN

Large company data for State companies such as PT. PLN is something normal. HR Division of PT. PLN really needs to process this large number of employee data. An application that can support the HR division is needed to improve employee performance. In designing the grouping application, in this study using the K-Means Clustering method to classify employee performance. The results of the calculation of the status of the cluster obtained 169 employees thanks to good performance, 208 employees were categorized as good and 458 employees were categorized as poorly performing. The results of grouping talent received 473 employees in the very good category, 214 employees in the good category and 150 employees in the poor performance category. The grouping results received 620 employees categorized as very good, 27 employees categorized as good and 141 employees categorized as having poor performance
Geographical distribution of Secondarys schools in Mosul using Geographical Information Systems (GIS)

The rates of large population increase in the city of Mosul in particular, and the urban expansion accompanying this increase have led to great pressure on educational services and thus these services cannot meet the needs of the growing population, as a result of the absence of prior planning to contain any possible increase in the population, and this necessitates the intervention of planners.And decision-makers and development officials to take measures to achieve all the services that the citizen aspires to in terms of fair distribution and choose the appropriate location to provide services with high efficiency.Hence the importance of this study, which aims to analyze and diagnose the reality of secondary schools in the city of Mosul and to reveal the characteristics of their spatial distribution, and their efficiency in providing the necessary service to the population at the level of residential neighborhoods in the city..
Quantum advantage with light

We realize quantum computational advantage in a Gaussian Boson Sampling (GBS) experiment. We inject 25 two mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix. We rule out thermal states, distinguishable photons, and uniform distribution hypotheses. This GBS machine can sample 14 orders of magnitude faster than classical supercomputer.
Design of hybrid neural networks of the ensemble structure

This paper considers the structural-parametric synthesis (SPS) of neural networks (NNs) of deep learning, in particular convolutional neural networks (CNNs), which are used in image processing. It has been shown that modern neural networks may possess a variety of topologies. That is ensured by using unique blocks that determine their essential features, namely, the compression and excitation unit, the attention module convolution unit, the channel attention module, the spatial attention module, the residual unit, the ResNeXt block. This, first of all, is due to the need to increase their efficiency in the processing of images. Due to the large architectural space of parameters, including the type of unique block, the location in the structure of the convolutional neural network, its connections with other blocks, layers, computing costs grow nonlinearly. To minimize computational costs while maintaining the specified accuracy this work set tasks of both the generation of possible topology and structural-parametric synthesis of convolutional neural networks. To resolve them, the use of a genetic algorithm (GA) has been proposed. Parameter configuration was implemented using a genetic algorithm and modern gradient methods (GM). For example, stochastic gradient descent with momentum, accelerated Nesterov gradient, adaptive gradient algorithm, distribution of the root of the mean square of the gradient, assessment of adaptive momentum, adaptive Nesterov momentum. It is assumed to use such networks in the intelligent medical diagnostic system (IMDS), for determining the activity of tuberculosis. To improve the accuracy of solving the classification problem in the processing of images, the ensemble structure of hybrid convolutional neural networks (HCNNs) has been proposed in the current work. The parallel structure of the ensemble with the merged layer was used. Algorithms of optimal choice and integration of features in the construction of the ensemble have been developed
EVALUATION OF THE QUALITY OF PRIMARY EDUCATION BY TESTING 5 FROM THE TEACHER’S PERSPECTIVE

Quality requirements in all areas of education and in all types of schools are one of the basic attributes on which the future of nations, states and humanity depends. The quality of education as a service of a publicly funded society is therefore subject to monitoring by government institutions. Evaluating the quality of a school means finding out data on pre-agreed aspects of school life, the processes that take place in it. In 2015, an innovated state educational program was introduced in primary education. It brought changes especially in the educational standards of individual subjects, where is the attention to performance standards. The content of the curriculum does not become the goal of education, but a means to achieve the goal, which is a competent student. National testing also tries to bring this change in the understanding of content closer to the creation of test tasks. Students tests results cannot be the only and most important measure of a school's quality. Students, parents and teachers need to focus on non-cognitive processes at school. The introduction of testing of 5th grade students was not accepted by teachers with an understanding of effort to increase the quality of education. The aim of the paper is to compare the opinions of teachers from 2017 with the opinions of teachers in 2020 and to find out whether there has been a change in the acceptance of testing of 5th grade students as a tool for measuring the quality of education after primary education.
ANALISIS KESANTUNAN BERBAHASA MAHASISWA UNIVERSITAS BINA SARANA INFORMATIKA DALAM BERINTERAKSI DENGAN DOSEN MELALUI PESAN SINGKAT WHATSAPP: KAJIAN PRAGMATIK

The more younger lecturers among young students, the more they would like to approach them, either during lecture or outside the class. The approach might make the relationship between lecturers and students closer and more emotional. However, it could seem less politeness which students have. This can be seen from the way and the language use through text messages on social medias, particularly on WhatsApp. This research aims to find out the variety of the students' politeness when having interaction with lecturers trough short message on WhatsApp by describing the students' language politeness, the principles of the deviation in politeness, the obedience and the function of the language politeness itself in the relationship between students and lecturers of Universitas Bina Sarana Informatika. Primary data in this research is written conversation records, especially screen shots of chats on WhatsApp. Secondary data is the knowledge of Pragmatic Studies of Bahasa Indonesia including the theories regarding language politeness principles in order to impact to the characters of the nation's next generation. The method used in this research is pragmatic studies, in particular with pragmatic and contextual equivalents.
RESCNN: A Deep Learning Approach for Unmasking Face Mask

Due to outbreak of COVID-19 pandemic, the trend of wearing mask is rising all over the world. Before such pandemic people wear mask only to protect themselves from pollution. While other people are self-conscious about their looks, they hide their emotions from the public by hiding their faces. But in current scenario, after pandemic, it is compulsory to wear mask everywhere as researchers and doctors have proved that wearing face masks works on impeding COVID-19 transmission. Nowadays, all attendance system or surveillance systems, etc. are integrated with AI technology in which face recognition is considered as input variable. So, there is need to determine all facial landmarks to recognize an individual. In this research work, Residual Convolution Neural Network (ResCNN), network is designed and simulated which unmasks the face mask present on face and restore mask area and recognize an individual. The result analysis is performed in three different cases or scenario, one normal frontal facial region with mask, in another case the masked face is tilted and in third case the noisy masked face is taken as input. The noise in image occurs due to many physical conditions. The dataset for training of ResCNN is prepared by masking facial images taken from CelebA dataset and MFR datasets to prove the efficiency of the proposed model.
Study Program Classification System Informatics Engineering of Ubhara Surabaya

One indicator to improve the quality of a university is the number of students who graduate on time. But the problem that often occurs at Bhayangkara University in Surabaya is the number of students entering and the number of students graduating unbalanced. Therefore this research was made to classify the period of study of students by using variables in the form of social studies semester 1-4, school origin, work status, morning / evening class status, and sex. This study aims to classify the length of time a student studies on time or late using the naïve bayes method. The results of this study indicate that the system is able to classify training data and test data on experiments conducted in each batch, the highest accuracy results are 59% and the lowest accuracy results are 56%.
Development and Prediction Analysis of Drug Delivery System Platform Based on Web

The rapid development of network information technology has greatly changed the internal and external information environment of the library. The library and information room of Shanghai Institute of pharmacy, Chinese Academy of Sciences, designed and implemented a web-based drug delivery platform by adopting the new architecture of “one background facing multiple front desk”, B/s multi-layer system, struts + Spring + Hibernate open framework and other modern information technologies. At the same time, it also provides comprehensive, multi-level and high-quality literature information and professional drug delivery services to more than 100 pharmaceutical enterprises and institutions in Zhangjiang pharmaceutical Valley in Pudong.
Supervisor-Worker Problems with an Application in Education

In many e-learning settings, allowing students to choose which skills to practice encourages their motivation and contributes to learning. However, when given choice, students may prefer to practice skills that they already master, rather than practice skills they need to master. On the other hand, requiring students only to practice their required skills may reduce their motivation and lead to dropout. In this paper, we model this tradeoff as a multi-agent planning task, which we call SWOPP (Supervisor- Worker Problem with Partially Overlapping goals), involving two agents—a supervisor (teacher) and a worker (student)—each with different, yet non-conflicting, goals. The supervisor and worker share joint goals (mastering skills). The worker plans to achieve his/her own goals (completing an e-learning session) at a minimal cost (effort required to solve problems). The supervisor guides the worker towards achieving the joint goals by controlling the problems in the choice set for the worker. We provide a formal model for the SWOPP task and two sound and complete algorithms for the supervisor to guide the worker’s plan to achieve their joint goals. We deploy SWOPP for the first time in a real-world study to personalize math questions for K5 students using an e-learning software in schools. We show that SWOPP was able to guide students’ interactions with the software to practice necessary skills without deterring their motivation.
The importance of reading competencies in the context of the “industry 4.0” industrial revolution

This article reveals the notion of “Industry 4.0”, which reflects the concept of the fourth industrial revolution, what relevant professions will appear in the future in connection with this, what skills are needed to acquire these professions, the importance of developing reader competence, and also the issue of the phenomenon of “clip thinking”. It is concluded that it is advisable to formulate ideas about the professions of the future starting from preschool age and emphasize the need to improve the system of training future personnel and improve their skills.
PENGEMBANGAN MEDIA PEMBELAJARAN SAINS BIG SCRAPBOOK TEMA 7 SUBTEMA 3 PADA SISWA KELAS IV SD PATRA DHARMA TARAKAN

This study was intended to develop a big scrapbook as a learning media and to determine the feasibility and level of students’ interest in learning media developed in thematic learning on theme 7 sub-thema 3, the beauty of unity and oneness in my county fo the fourth grade students of SD Patra Dharma Tarakan. This type of research was a research and development (RD) which aimed at producing a product using the ADDIE development model which consisted of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. The data were collected through observation, interview, student’ initial questionnaire, validation, student’ interest questionnaire, and documentation. The resulted product of big scrapbook media consisted of 22 sheets and 42 pages which were made in art paper with A3 paper size (29 x 42 cm), colorful cover paper, and origami patterned paper. The results of validation from media expert was 84%, material expert was 100%, and practitioner was 94%. The students’ interest from the initial product trials was 91.33%, and the usage trials were 93.2%. The validation of a media expert, material expert, and practitioner expert showed that the big scrapbook learning media was suitable for the learning process. The results of the students’ interest regarding to the learning media in the initial product trials and usage trials showed that the level of students’ interest in the big scrapbook learning media was very interested. Keywords: Learning media, Big Scrapbook.
Implementation of Bethesda system for reporting thyroid cytology in an academic institution: A 2 year retrospective study

Thyroid diseases are the most common endocrine diseases in India. Both benign and malignant diseases present as thyroid nodules. Fine needle aspiration cytology (FNAC) has proven to be one of the most effective tools for evaluation of thyroid nodules. Interpretation of FNAC has been standardized by the Bethesda system for reporting thyroid cytopathology (TBSRTC). Aim: To categorize all the thyroid lesions utilizing TBSRTC, in an academic institution for a duration of 2 years and find its histopathological correlation. Materials and Methods: A 2 year retrospective study included all 229 patients who underwent thyroid FNAC in the institution. All the clinical and relevant radiological investigations were retrieved from hospital information system. All Papanicolaou and May Grunwald Giemsa stained smears were categorized under TBSRTC in to 6 categories. Results: Maximum number of cases were between the age group of 31-45yr. Male: Female ratio in our study was 1:13. There were 12 cases (5.2%) under Unsatisfactory/non diagnostic category, 196(85.5%) cases belonged to benign category, 1 case (0.04%) of Atypia of undetermined significance/follicular lesion of undetermined significance,10 cases (4.3%) belonged to follicular/ suspicious for follicular neoplasm,4 cases (1.7%) belonged to suspicious for malignancy & 6 (2.6%) belonged to Malignant category. Cyto histopathology discrepancy rate was 7.14%. Conclusions: TBSRTC brings about uniformity in reporting of thyroid cytology, thereby helping in better patient management and avoiding unwanted surgeries. Keywords: Bethesda system, Fine needle aspiration cytology, Follicular neoplasm, Malignant, Reporting, Thyroid.
International Financial Flows its Effects on Economic Growth in Jordan

This study aims to measure the effects of international financial flows represented in exports, remittances of overseas workers, foreign direct investment, and external grants on the economic growth in Jordan during the periodfrom 1990 to 2018.The study used self-regression for the distributed slowdown periods.The study obtained several important results.First, the study found the presence of a significant and positive effect of foreign exports and grants on economic growth in the long and short term.The study also concluded the existence of the significant and negative effect of workers'remittances on economic growth in the long term, and the insignificance of the effect in the short term.The results indicate that the effects of foreign direct investment on economic growth in the long and short term areinsignificant.The study offers aset of recommendations, including the adoption of the Jordanian government ofa policy that encourages exportsbased on the introduction of national exportable products and the improvement of the quality of Jordanian products.Jordanian workers should be facilitated to work in the foreign markets and their remittances should be utilized for investment purposes instead of spending them for consumer purposes.Periodic reviews of the institutional and legal frameworks pertaining to encouraging investment in Jordan should be conducted and external grants should be directed towards priority projects for the Jordanian economy.
Implementing Abbreviated Personas into Engineering Education

Abstract Personas are fictional archetypal consumers that aid designers and engineers in more effectively creating products with a human interface. As more products shift from strict utilitarian function to meeting additional physical and psychological needs, designers and engineers must implement emotional design in more domains. Learning to employ personas to explore elements of emotional design is beneficial in an academic course and capstone project as these personas allow students to consider engineering requirements from the perspective of Donald Norman’s three aspects of emotional design: visceral, behavioral, and reflective. In this paper, we present an approach to evaluate the efficacy of using abbreviated personas, which are truncated personas containing typical user biographic information, goals, habits, or experiences. In our first experiment at Stanford University the students focused on the use of and outcome from the abbreviated personas and not the persona generation itself. The lessons learned from this experiment were then applied in a capstone course at the US Military Academy to better understand the full extent of implementation into engineering education. The automotive design capstone originated in a mechanical engineering course focused on engineering engagement through story-telling and included three distinct presentation methods for abbreviated personas at a public exhibition. Over 250 participants interacted with the abbreviated personas and manipulated an analog display based on their understanding of each persona. From these participants, 82 provided written feedback and completed exit surveys on the presentation methods for the abbreviated personas. The data indicate that despite some differences between the presentation methods, all the abbreviated personas contained enough information for making design decisions based on user emotion and requirements. The second application of abbreviated personas builds on this notion and unifies the presentation method to focus on the inputs of the abbreviated personas throughout the design/build process in the capstone. Team member interviews and surveys will capture the data from this iteration.
Improved WSA for adaptive FLC in non-symmetrical hydraulic servo-system

Fuzzy logic controller (FLC) performs well in nonlinearity system, while the tuning of FLC is still a challenge. In this paper, FLC is applied to non-symmetrical hydraulic system. To obtain a satisfactory control performance, WSA-IC is used to build adaptive FLC. We test the FLC obtained by WSA-IC using a benchmark control plants and a hydraulic servo-system. Simulations results show that WSA-IC based FLC performs better than GA, DE, and some other heuristic algorithms.
Context Event Features and Event Embedding Enhanced Event Detection

Extracting valuable information from text has always been a hot point for research and event detection is an essential subtask of information extraction. Most existing methods of event detection only focus on sentence-level information and do not consider the correlation between different event types. To address these problems, in this paper, we propose a novel pre-trained language model based event detection framework named CFEE that utilizes document-level information and event correlation to enhance the event detection task. To obtain event correlation, we project all event types into a shared semantic space through a Skip-gram model, where the event correlation can be represented as the distance between event embeddings. In order to capture document-level information, we utilize a bidirectional recurrent neural network to fuse the context information. Experiments on the ACE2005 dataset demonstrate that our proposed model is better than most existing methods, and also demonstrate the effectiveness of event correlation and document-level information.
Agnostic Learning with Unknown Utilities.

Traditional learning approaches for classification implicitly assume that each mistake has the same cost. In many real-world problems though, the utility of a decision depends on the underlying context x and decision y; for instance, misclassifying a stop sign is worse than misclassifying a road-side postbox. However, directly incorporating these utilities into the learning objective is often infeasible since these can be quite complex and difficult for humans to specify.We formally study this as agnostic learning with unknown utilities: given a dataset S = {x_1, …, x_n} where each data point x_i ∼ 𝒟_x from some unknown distribution 𝒟_x, the objective of the learner is to output a function f in some class of decision functions ℱ with small excess risk. This risk measures the performance of the output predictor f with respect to the best predictor in the class ℱ on the unknown underlying utility u^*:𝒳×𝒴↦ [0,1]. This utility u^* is not assumed to have any specific structure and is allowed to be any bounded function. This raises an interesting question whether learning is even possible in our setup, given that obtaining a generalizable estimate of utility u^* might not be possible from finitely many samples. Surprisingly, we show that estimating the utilities of only the sampled points S suffices to learn a decision function which generalizes well. With this insight, we study mechanisms for eliciting information from human experts which allow a learner to estimate the utilities u^* on the set S. While humans find it difficult to directly provide utility values reliably, it is often easier for them to provide comparison feedback based on these utilities. We show that, unlike in the realizable setup, the vanilla comparison queries where humans compare a pair of decisions for a single input x are insufficient. We introduce a family of elicitation mechanisms by generalizing comparisons, called the k-comparison oracle, which enables the learner to ask for comparisons across k different inputs x at once. We show that the excess risk in our agnostic learning framework decreases at a rate of O (1/k) with such queries. This result brings out an interesting accuracy-elicitation trade-off - as the order k of the oracle increases, the comparative queries become harder to elicit from humans but allow for more accurate learning.