Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK (LKPD) UNTUK MENGAKOMODASI KEBERAGAMAN SISWA PADA PEMBELAJARAN TEMATIK KELAS II DI SD MUHAMMADIYAH DANUNEGARAN

Research describes descriptive to develop LKPD to Accommodate Student Diversity in Class II Thematic Learning, study the feasibility of LKPD products based on expert validators, and study students' responses to LKPD products. This research was conducted at Muhammadiyah Danunegaran Elementary School. Data collection techniques using interviews, questionnaires, and documentation. The research method used in this study is the Research and Development (R&amp;D) method. The steps of LKPD product development according to Borg and Gall's suggestion are 5 simple steps, namely: (1) product analysis to be developed or exploratory study, (2) initial product development, (3) expert validation and product improvement, (4) limited trials and product revisions, (5) main trials and final products. The research subjects were second-grade students of Muhammadiyah Danunegaran Elementary School. The results of the research are LKPD products that have been successfully developed. The feasibility of LKPD products has received a good rating from the validator material with an average score of 149, a good score from the validator, a linguist with an average score of 60, a good list from the validator, a media expert with an average score of 59, and also both from the teacher class with an average score of 75. Limited and primary trials show positive student responses with a percentage of 100%. Thus, the development of this LKPD product is of good quality and helps students in the thematic material of grade II Elementary School.
How does Job Satisfaction Affect the Job Performance of Employees?

The paper seeks to evaluate OYO Homes & Hotels, an Indian Hotel chain that holds the position of the third-largest hospitality chain in the world. The purpose of this paper is to evaluate how job satisfaction affects the performance of employees in OYO as a service provider company. The research will be based on secondary information gathered from databases, Internet and relevant information provided on OYO's official website. To fulfill the objective, the project aims to analyze the employees' main causes of job satisfaction, strategies in dealing with job dissatisfaction, and changes in performance. Finally, with the analysis, the project will establish recommendations for OYO to maximize employee performance in relation to job satisfaction.
Towards automatic drawing animation using physics-based evolution

We demonstrate a system to automatically animate hand-drawn characters. Starting with skeleton extraction, meshing and vertex skinning, our system simulates characters using a neural network in a physics-based environment. Using an evolutionary algorithm, it searches for networks that move characters far while keeping a good posture. We validated the system through a user study with 26 participants. For most drawings (60 %), they felt satisfied with the generated animation, and in 76% of cases, they wished to draw and animate additional characters. The participants reported mostly positive emotions after seeing the animations. Only a minority had feelings of strangeness or had negative emotions. This work demonstrates the possibility of creating an automated 2-D character animation system making little assumption on what is drawn. We believe that this work can enable more children to engage in creative play and explore their imagination.
Development Cycle Modeling: Resource Estimation

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.
Robust entanglement of an asymmetric quantum dot molecular system in a Josephson junction

We demonstrate the generation of robust entanglement of a quantum dot molecular system in a voltage-controlled junction. To improve the quantum information characteristics of this system, we propose an applicable protocol which contains the implementation of asymmetric quantum dots as well as the engineering of reservoirs. Quantum dots can provide asymmetric coupling coefficients due to the tunable energy barriers through the gap voltage changes. To engineer the reservoirs, superconducting leads are used to prepare a voltage-biased Josephson junction. The high-controllability properties of this system give the arbitrary magnitude of entanglement by the arrangement of parameters. Significantly, the perfect entanglement can be achieved for an asymmetric structure in response to the increase of bias voltage, and also it continues saturated with the near-unit amount.
A Novel Approach to Wheelchair Design and Operation Using Multi-Function Controller

An electric multi-function controlled wheelchair is designed and fully tested.This wheelchair is controlled by several physiological variables namely; voice, head movement, finger bending, breathing pressure and Electrooculography (EOG).The patient has the choice to use any of these variables to control the wheelchair.The voice command is recorded by a voice recognition module with its microphone, whereas, the head and finger motion operate through the gyro accelerometer and flex sensors.A pressure sensor is used to determine the force of breathing and EOG signals are used to control the wheelchair movement.All of the inputs are processed using a microcontroller.Testing of the wheel chair using the mentioned variables is carried out successfully with accuracy between 88 to 96% for various control modules and safety consideration as a primary goal.
2D Irregular Optimization Nesting Method based on Adaptive Probabilistic Genetic Simulated Annealing Algorithm

Serial plates are not reasonable in material saving and stress dispersion.To design orthopedic plates ideally and conveniently, this paper proposes a method to optimize plates through editing semantic parameters based on average bone model.Firstly, for the reasonable distribution of serial plates in number and size, an average bone model is created from the existing bones, among which each bone has a contribution to the average model.Secondly, a common orthopedic plate with semantic parameters is constructed on average bone model and it can be conveniently modified.Lastly, optimizing the thickness of the plate through finite element analysis and genetic algorithm to meet the stress condition and use as little material as possible.The simulation results indicate that the method can save material and disperse the stress of the plates so that it can effectively optimize the orthopedic plates.
Influence of Solar Position Calculation Methods Applied to Horizontal Single-Axis Solar Trackers on Energy Generation

Photovoltaic systems have been explored as a solution to meet the growing demand for electricity from a clean and renewable source. However, the low energy conversion efficiency of photovoltaic panels is one of the critical factors that hinder the competitiveness of this energy source concerning the others. An effective way to improve the efficiency of photovoltaic systems is by using solar trackers. The tracking strategy most used in photovoltaic plants employs algorithms to calculate the Sun position. This work presents energy generation estimation applying six algorithms in horizontal single-axis solar tracking: the Solar Position Algorithm (SPA) and Grena 1–5 algorithms. The aim is to evaluate the influence of these algorithms on energy generation. For all simulated locations, comparing to an ideal scenario, the SPA presented the best energy generation results. However, the other algorithms showed negligible differences between themselves, which allows us to conclude that any of the algorithms can be used without implying significant energy losses. Thus, Grena 1–2 can be highlighted for easier implementation.
Identity-sensitive loss guided and instance feature boosted deep embedding for person search

Person search aims at detecting and re-identifying pedestrians from whole monitoring images, which is vital for intelligent surveillance. However, this task is still challenging due to the extremely few instances per training identity and inherent fine-grained differences among different identities. To this end, this work proposes an identity-sensitive loss guided and instance feature boosted pipeline to extract deep discriminative feature embedding for person search. First, a prior anchor pre-trained network (PAPN) is designed to obtain proper initial state for the whole deep person search training baseline. Second, a new loss function called instance enhancing loss (IEL) is proposed to learn identity-sensitive features by introducing unlabeled identity information. Specifically, the proposed IEL can selectively utilize unlabeled identities with similar appearances to labeled identities to train the person search network. Third, considering the intra-class compactness of features learned by center loss and contextual inter-class relations, two instance boosting strategies (Boosting) are used to learn more discriminative features. Extensive experiments on two benchmark datasets, namely CUHK-SYSU and PRW, demonstrate the effectiveness of our approach.
Analysis of Factors that Influence Student Learning Achievement

This study aims to analyze the factors that affect student achievement in the Department of Economic Education at the Faculty of Tarbiyah and Teacher Training of UIN Suska Riau. Population of this study was 201 students with a sample of 100 people. Data collected using questionnaire and documentation instrument. The data analysis technique used multiple regression analysis. The results showed that learning motivation has a positive and significant effect on learning achievement. The learning interest has a positive and significant effect on learning achievement. The family environment has a positive and significant effect on learning achievement, and teh learning motivation has also a positive and significant effect on learning achievement. There is a joint influence of learning motivation, interest in learning, family environment and learning models, together on learning achievement.
An Extension of Entanglement Measures for Pure States

To quantify the entanglement is one of the most important topics in quantum entanglement theory. In [arXiv: 2006.12408], the authors proposed a method to build a measure from the orginal domain to a larger one. Here we apply that method to build an entanglement measure from measures for pure states. First, we present conditions when the entanglement measure is an entanglement monotone and convex, we also present an interpretation of the smoothed one-shot entanglement cost under the method here. At last, we present a difference between the local operation and classical communication (LOCC) and the separability-preserving (SEPP) operations, then we present the entanglement measures built from the geometric entanglement measure for pure states by the convex roof extended method and the method here are equal, at last, we present the relationship between the concurrence and the entanglement measure built from concurrence for pure states by the method here on 2 \otimes 2 systems. We also present the measure is monogamous for 2\otimes 2 \otimes d system.
Consistent Unsupervised Estimators for Anchored PCFGs

Abstract Learning probabilistic context-free grammars (PCFGs) from strings is a classic problem in computational linguistics since Horning (1969). Here we present an algorithm based on distributional learning that is a consistent estimator for a large class of PCFGs that satisfy certain natural conditions including being anchored (Stratos et al., 2016). We proceed via a reparameterization of (top–down) PCFGs that we call a bottom–up weighted context-free grammar. We show that if the grammar is anchored and satisfies additional restrictions on its ambiguity, then the parameters can be directly related to distributional properties of the anchoring strings; we show the asymptotic correctness of a naive estimator and present some simulations using synthetic data that show that algorithms based on this approach have good finite sample behavior.
Comparing field data using Alpert multi-wavelets

In this paper we introduce a method to compare sets of full-field data using Alpert tree-wavelet transforms. The Alpert tree-wavelet methods transform the data into a spectral space allowing the comparison of all points in the fields by comparing spectral amplitudes. The methods are insensitive to translation, scale and discretization and can be applied to arbitrary geometries. This makes them especially well suited for comparison of field data sets coming from two different sources such as when comparing simulation field data to experimental field data. We have developed both global and local error metrics to quantify the error between two fields. We verify the methods on two-dimensional and three-dimensional discretizations of analytical functions. We then deploy the methods to compare full-field strain data from a simulation of elastomeric syntactic foam.
Development of a Resonant Scanner to Improve the Imaging Rate of Astigmatic Optical Profilometers

An astigmatic optical profilometer provides numerous advantages such as compact size, low cost, and high resolution. In 2018, a z-axis modulation mode of the astigmatic optical profilometer was developed to realize quantitative height measurement for a surface comprising complex materials. However, the current imaging rate of the z-axis modulation mode is time-consuming. In this article, a resonant scanner was proposed to execute a high-speed z-axis scanning motion. By inducing the resonant mode, the resonant scanner enabled both a large travel range of over 87 μm and high oscillation frequency of 1.576 kHz. Additionally, a data analysis process was proposed to calibrate the nonlinear movement of the resonant scanner. Experimental results demonstrated that the developed optical profilometer successfully captured the quantitative height and reflectivity of the complex material comprising chrome patterns on a glass substrate. Furthermore, an imaging rate of 256 s/frame was achieved, which was approximately 50 times faster than that of a previous system employing a commercial flexure-guided scanner.
Forbidden subspaces for level-1 quantum approximate optimization algorithm and instantaneous quantum polynomial circuits

We present a thorough investigation of problems that can be solved exactly with the level-1 quantum approximate optimization algorithm (QAOA). To this end, we implicitly define a class of problem Hamiltonians that are employed as phase separators in a level-1 QAOA circuit and provide unit overlap with a target subspace spanned by a set of computational basis states. For one-dimensional target subspaces, we identify instances within the implicitly defined class of Hamiltonians for which quantum annealing (QA) and simulated annealing (SA) have an exponentially small probability of finding the solution. Consequently, our results define a demarcation line between QAOA on one hand and QA and SA on the other, and highlight the fundamental differences between an interference-based search heuristic such as QAOA and heuristics that are based on thermal and quantum fluctuations like SA and QA respectively. Moreover, for two-dimensional solution subspaces, we are able to show that the depth of the QAOA circuit grows linearly with the Hamming distance between the two target states. We further show that there are no genuine solutions for target subspaces of dimension higher than 2 and smaller than ${2}^{n}$. We also transfer these results to instantaneous quantum polynomial (IQP) circuits.
Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.
Faculty members' readiness implementing e-learning in higher education Saudi Universities: A cross-sectional study

Background/ Objectives: This article reports on an e-learning readiness study that was carried out to determine the institutional, individual, and communication factors of the two major stakeholder groups (teachers and institutions) in the higher education universities in Saudi Arabia.Methods: A sample of 139 faculty members working in ten higher education universities in Saudi Arabia was surveyed in this cross-sectional study.A pre-validated survey questionnaire of e-learning was adopted to collect data from the participants' between April and May 2019.Findings: The pre-validated survey tool of Teachers' Readiness for Online Learning Measure (TROLM) of an 18 item scale has been explored in four factors: Communication self-efficacy, institutional support, self-directed learning, and learning-transfer self-efficacy.The findings of this study are instrumental in implementing successful e-learning resources (strategies) in Higher Education Institutions and will also benefit e-learning initiatives in similar institutions in other regions of Saudi Arabia and beyond.The Study demonstrated the supportive e-learning strategies Blackboard system that facilitated faculty members' readiness to teach university students during the sudden transition from traditional methods to the e-learning platforms.
Learning-Based Computer-Aided Prescription Model for Parkinson's Disease: A Data-Driven Perspective

In this article, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods.
Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration

The multispectral remote sensing image (MS-RSI) is degraded existing multispectral camera due to various hardware limitations.In this paper, we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First, the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding, the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration, our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.
A Multi-blocked Image Classifier for Deep Learning

onvolutional Neural Networks (CNN) have been very successful in classification and object recognition. A lot of related work has been done to modify the performance of the networks, but they could either perform better with accuracy at high cost or decrease the time taken by a model on training datasets. We propose a deep neural network model ’Multi-Blocked Model’ which tends to decrease this gap and is efficient and accurate with less convolutional layers, easy to deploy online or embedded systems. We choose three datasets that are publicly available and popular for their own uniqueness among the datasets in deep neural networks. These three diverse datasets are: Modified National Institute of Standards and Technology (MNIST), Street View House Number (SVHN) and the Canadian Institute for Advanced Research with 10 Cases (CIFAR-10). Our stateof-the-art Multi-Blocked model is presented well on all three data sets. Dropout is added to overcome the overfitting problem. The multi-blocked model is designed in a way that it uses a minimum number of parameters so that it is able to run on a Graphical Processing Unit (GPU), which requires less power. The experimental results show that our proposed Multi-Blocked model tends to achieve the accuracy of these datasets by 99.40%, 90.8%, 88.07% consuming under 2 GB of graphical memory.