Reviewerly/exHarmony huggingface dataset:
Modelling Automated Planning Problems for Teams of Mobile Manipulators in a Generic Industrial Scenario

Flexible control strategies are required in industrial scenarios to coordinate the actions of mobile manipulators (e.g., robots and humans). Temporal planning approaches can be used as such control strategies because they can generate those actions for the agents that must be executed to reach the goals, from any given state of the world. To deploy such approaches, planning models must be formulated. Although available in the literature, these models are not generic enough such that they can be easily transferred to new use cases. In this work, a generic industrial scenario is derived from real scenarios. For this scenario, a generic planning problem is developed. To demonstrate their generality, the two constructs are configured for a new scenario, where custom grippers are assembled. Lastly, a validation methodology is developed for the generic planning problem. The results show that the generic industrial scenario and the generic planning problem can be easily instantiated for new use cases, without any new modelling. Further, the proposed validation methodology guarantees that these planning problems are complete enough to be used in industrial use cases. The generic scenario, the planning problems, and the validation methodology are proposed as standards for use when deploying temporal planning in industrial scenarios with mobile manipulators.
A Boosted Minimum Cross Entropy Thresholding for Medical Images Segmentation Based on Heterogeneous Mean Filters Approaches

Computer vision plays an important role in the accurate foreground detection of medical images. Diagnosing diseases in their early stages has effective life-saving potential, and this is every physician's goal. There is a positive relationship between improving image segmentation methods and precise diagnosis in medical images. This relation provides a profound indication for feature extraction in a segmented image, such that an accurate separation occurs between the foreground and the background. There are many thresholding-based segmentation methods found under the pure image processing approach. Minimum cross entropy thresholding (MCET) is one of the frequently used mean-based thresholding methods for medical image segmentation. In this paper, the aim was to boost the efficiency of MCET, based on heterogeneous mean filter approaches. The proposed model estimates an optimized mean by excluding the negative influence of noise, local outliers, and gray intensity levels; thus, obtaining new mean values for the MCET's objective function. The proposed model was examined compared to the original and related methods, using three types of medical image dataset. It was able to show accurate results based on the performance measures, using the benchmark of unsupervised and supervised evaluation.
Data ethics in education: a theoretical, practical, and policy issue

Responsible data use has emerged as an important concept in education, especially in the wake of the COVJD-19 pandemic, which continues to highlight inequities. The knowledge and skills to use data effectively and appropriately are at the heart of data ethics. Educators must tightly couple data literacy with an ethical approach to using data—that is, they must be thoughtful about what they choose to do with data, how they go about their work, and how they center their work to benefit, rather than to harm, those engaged in the work of schooling, including students, teachers, families, and other educators. In this article, intended to provoke thought around data ethics among educators, researchers, and policymakers, we take a broad view of what data are and assert that data ethics go far beyond protecting the privacy and confidentiality of data. To be an ethical data user means using the right data in the right ways for the right purposes. The article lays out a context for data ethics, demonstrates how ethics are coupled with data literacy, provides examples of data ethics in practice, and recommends steps for strengthening ethical data use in practice.
ANALISIS PERSIAPAN GURU DALAM PELAKSANAAN PEMBELAJARAN MATEMATIKA DI KELAS IV SD NEGERI JAMBU 01

This study aims to determine what preparations are made by the teacher in the implementation of mathematics learning.The method used in this research is qualitative with descriptive analysis.Research subjects are informants who provide research data through interviews.The informants in this study were grade IV teachers of Jambu State Elementary School 01.While the data analysis used in this study were data reduction, data presentation and verification.This research resulted in the findings of preparations made by the teacher in the implementation of mathematics learning, among others, which must be prepared are the learning objectives, learning materials, learning methods, learning media, evaluation of learning, students, teachers and the place of learning environment.The obstacles in the preparation are students who often talk to themselves and disturb other friends, then a weak internet connection that causes the process of finding learning resources to be difficult.
The Federal Democratic Republic of Ethiopia

The International Monetary Fund’s (IMF’s) Statistics Department (STA) provided technical assistance (TA) on financial soundness indicators (FSI) to the National Bank of Ethiopia (NBE) during June 15-July 10, 2020. The TA mission took place in response to a request from the authorities, with the support of the IMF’s African Department (AFR). Due to the COVID-19 pandemic and travel restrictions, the mission was conducted remotely via video conferences. The mission worked with the staff of the NBE on the development of FSIs that are in line with the IMF’s 2019 FSI Guide.1 The main objectives of the mission were to: (i) review the source data, institutional coverage, and accounting and regulatory frameworks supporting the compilation of FSIs; (ii) provide guidance for mapping source data for the banking sector to the FSI reporting templates FS2 and FSD as well as preparing the metadata; and (iii) agree with the authorities on the timeline to begin regular reporting of the FSIs for deposit-takers to STA. The mission also provided technical assistance to the NBE on the compilation of net open positions in foreign currencies.
Exploration on Teaching Practice of Material Mechanics Course for the Run-through External Cultivation in Vocational College

The paper expounds the construction background of the run-through external cultivation, and analyzes the common problems in the teaching of material mechanics in traditional vocational colleges.Combined with the curriculum characteristics of Conestoga College, a connecting school for the class of run-through external cultivation, this paper puts forward the teaching methods of "Pre-class Suspense" (PCS), and has achieved good results in teaching practice.The results show that the setting of PCS can obviously enhance students' interest in learning, activate class atmosphere, and improve the effect of listening to class.The PCS teaching method should be applied in more teaching courses.
Tilt sensitivity for a scalable one-hectare photovoltaic power plant composed of parallel racks in Muscat

Using the desktop tool (Energy3D) for modelling sustainable buildings and solar power generation, this work predicts the performance of a scalable photovoltaic (PV) power plant composed of south-facing parallel racks in Muscat (the capital of the Sultanate of Oman, latitude 23.6 degrees North). The pilot PV power plant has 3,535 PV panels of peak capacity 300 Wp, leading to a total theoretical peak capacity of 1,060.5 kWp. It covers a land area of 10,000 square meters (1 hectare). The work focuses on the sensitivity of the output electricity to the tilt of the PV panels. The analysis shows that a fixed year-round optimum tilt angle offers 6.6% less annual yield than the single-axis-horizontal tracking system (continuously varying tilt). Adopting a dual-tilt system (tilt of 8.6 degrees during April–September and 38.6 degrees during October–March) reduces this gap to 2.5%. The PV power plant can provide an annual yield of 1.794 GWh under a dual-tilt setting, and 1.92 GWh with single-axis-horizontal solar tracking. Some results are provided on a per-square-meter basis, for easy estimation of land needs. The accuracy of estimating electric annual yield was tested by benchmarking with the Global Solar Atlas (GSA) online tool by the World Bank, for the case of fixed optimum tilt.
Hybrid CV-DV Quantum Communications and Quantum Networks

Quantum information processing (QIP) opens new opportunities for high-performance computing, high-precision sensing, and secure communications. Among various QIP features, the entanglement is a unique one. To take full advantage of quantum resources, it will be necessary to interface quantum systems based on different encodings of information both discrete and continuous. The goal of this paper is to lay the groundwork for the development of a robust and efficient hybrid continuous variable-discrete variable (CV-DV) quantum network, enabling the distribution of a large number of entangled states over hybrid DV-CV multi-hop nodes in an arbitrary topology. The proposed hybrid quantum communication network (QCN) can serve as the backbone for a future quantum Internet, thus providing extensive long-term impacts on the economy and national security through QIP, distributed quantum computing, quantum networking, and distributed quantum sensing. By employing the photon addition and photon subtraction modules we describe how to generate the hybrid DV-CV entangled states and how to implement their teleportation and entanglement swapping through entangling measurements. We then describe how to extend the transmission distance between nodes in hybrid QCN by employing macroscopic light states, noiseless amplification, and reconfigurable quantum LDPC coding. We further describe how to enable quantum networking and distributed quantum computing by employing the deterministic cluster state concept introduced here. Finally, we describe how the proposed hybrid CV-DV states can be used in an entanglement-based hybrid QKD.
Technogenic ideal in digital culture: building a research model

Introduction. In the context of the development of digitalization processes in all spheres of public life, high technologies generate technical objects and systems capable of independent development and existence, a new structural element of the cultural ideal is formed – technogenic. In the literature, the technogenic ideal is most often considered as an element of a social or anthropological ideal in the cultural system. Due to the fact that the technogenic ideal enters into contradictory relations with the anthropological and social component of the cultural ideal, there is a need to build a model for studying the laws of its development and existence. Materials and Methods. In the course of the research, the methods of analytical review of scientific literature, comparison, analysis, the method of dialectics and its principles, the structural and functional approach were used. Results. The conducted research has shown that the most urgent issues in the study of the cultural ideal of modern society are the problems caused by the introduction of new technical forms of subjectivity into public relations. There are particularly acute discussions about the role and consequences of human interaction with them, which can lead to both its improvement and destruction. Discussion and Conclusions. As a result of the conducted research, the author substantiates the possibility of constructing a model for studying the technogenic ideal at two main levels. The first level will allow determining the nature, essence and forms of being of the technogenic ideal in the digital culture system from the position of philosophical and cultural knowledge: ontology, epistemology, axiology and praxiology. The next level in the proposed model is the structure of the cultural ideal, where the main elements are represented as an anthropological, social and technogenic ideal. This model allows us to build a methodology and logic for the study of the technogenic ideal based on the study of the theoretical foundations (ontological, epistemological, axiological and praxiological aspects) and the structural and functional foundations (typological and functional analysis) of this phenomenon in the digital culture system. This approach will make it possible to systematize and deepen the existing knowledge about the role and place of the technogenic ideal in the system of digital culture, to reveal the patterns and principles of its further evolution.
Phishing website analysis and detection using Machine Learning

Cybersecurity has become an essential part of this new digital age with more than 820 million users of internet by the year 2022 there is need of security systems to protect public from phishing scams as it not only effects the wealth but also effects the mental health of public, making people afraid to surf or use the internet services which motivates me to work on this problem to develop efficient solution.Objective of this paper is to analyse some common attributes shown by phishing websites and develop a model to detect these websites.Various models were trained on the dataset like Random Forest Classifier, Decision Tree Classifier, Logistic Regression, K Nearest Neighbours, Artificial Neural Networks and Max Vote Classifier of Random Forest, Artificial Neural Networks and K Nearest Neighbours.Highest accuracy was achieved by Max Vote Classifier of Random Forest (max depth 16), Decision Tree (max depth 18) and Artificial Neural Network of 97.73%.This research can be used in real life by implementing a web application in which user can enter the website link and using the link the application will get values for various factor on which model was trained and it will detect whether a website is phishing website or not.
Cooperative Obstacle Avoidance for Multiple UAVs Using Spline_VO Method

In order to solve multiple unmanned aerial vehicle (UAV) dynamic collision avoidance, a cooperative obstacle avoidance algorithm considering UAV's kinematic constraints has been developed. In the proposed algorithm, the useful information of UAVs is screened out by a Heartbeat information filtering mechanism and fused by the user datagram protocol (UDP) communication method, which improves communication performance among UAVs. In addition, the velocity obstacle (VO) method combined with cubic uniform B-spline curve is used to avoid obstacles and generate smooth paths, which can be applied to practical scenes. Finally, dynamic and static obstacle avoidance simulations are carried out to verify the effectiveness of the proposed algorithm.
The Psychological and Cognitive Processes Underlying College Students’ Demotivation to Learn English

This study aims to construct a model to explain Chinese college students’ psychological and cognitive processes underlying their demotivation to learn English. Grounded theory method and cognitive maps were creatively combined to analyze the interview data from 21 college students in China’s universities. This study found three paths underlying students’ demotivation to learn English: 1) the large discrepancy between students’ actual selves and their possible selves might firstly produce motivation for a short period but then could cause low self-efficacy and negative affects. The large discrepancies might also directly generate negative affects among college students; those different negative affects could lead to demotivation of English learning. 2) college students’ low ought-to selves could cause their low ideal selves and then demotivation of English learning; 3) demotivation to learn English might also associate with low value of English learning in college students’ minds. At the end of this research, some pedagogical implications are given based on those findings.
Policy-based optimization: single-step policy gradient method seen as an evolution strategy

This research reports on the recent development of black-box optimization methods based on single-step deep reinforcement learning and their conceptual similarity to evolution strategy (ES) techniques. It formally introduces policy-based optimization (PBO), a policy-gradient-based optimization algorithm that relies on a policy network to describe the density function of its forthcoming evaluations, and uses covariance estimation to steer the policy improvement process in the right direction. The specifics of the PBO algorithm are detailed, and the connections to evolutionary strategies are discussed. Relevance is assessed by benchmarking PBO against classical ES techniques on analytic functions minimization problems, and by optimizing various parametric control laws intended for the Lorenz attractor and the classical cartpole problem. Given the scarce existing literature on the topic, this contribution definitely establishes PBO as a valid, versatile black-box optimization technique, and opens the way to multiple future improvements building on the inherent flexibility of the neural networks approach.
Bayesian Image Super-Resolution With Deep Modeling of Image Statistics

Modeling statistics of image priors is useful for image super-resolution, but little attention has been paid from the massive works of deep learning-based methods. In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors. Concretely, first we consider an ideal image as the sum of a smoothness component and a sparsity residual, and model real image degradation including blurring, downscaling, and noise corruption. Then, we develop a variational Bayesian approach to infer their posteriors. Finally, we implement the variational approach for single image super-resolution (SISR) using deep neural networks, and propose an unsupervised training strategy. The experiments on three image restoration tasks, i.e., ideal SISR, realistic SISR, and real-world SISR, demonstrate that our method has superior model generalizability against varying noise levels and degradation kernels and is effective in unsupervised SISR. The code and resulting models are released via https://zmiclab.github.io/projects.html.
Thermal Performance of an Evacuated-Tube Solar Collector Using Nanofluids and an Electrical Curtain Controlled by an Artificial Intelligence Technique

 Increasing ETSC's efficiency by using Finned electronic curtain and Nano fluids . Using of finned electronic curtain increased sun rays on the ETSC's that increases the system efficiency. Efficiency improvement with TiO2 are 7.03%, 9.16%, and 11.89%, respectively. using artificial intelligent (FLC-ANN) to predict the thermal parameters of ETSCs.This paper studies the improvement of an evacuated tube solar collectors(ETSCs) performance in two way.The first is by adding a finned electronic curtain in front of the solar collector.While the second is by using a nanofluid instead of pure water.The purpose of the curtain is to increase the amount of solar radiation reflected toward the collector.The curtain is distinguished by its self-ability to track the sun's rays automatically.The electronic curtain is also closed to shade the tubes depending on the movement of the electronic curtain's fins and the nanofluid's temperatures.MATLAB algorithm has been used to design the Simulink model and control the system using Fuzzy Logic Control (FLC) and Artificial Neural Network (ANN).The results showed that the system performance improved using TiO2(50nm)+PW) as a working fluid without the curtain are (3.906%,5.34%,and7.407%), while the rate of improvement in the case of distilled water only was 2.34%and3.81%.Finally, by adding the finned electronic curtain to the system and use of TiO2(50nm)+PW) as a working fluid, the efficiency increased by 7.03%,9.16%,and 11.89%.The results showed that the performance of evacuated tubes solar collectors increased by using a nanofluid and the finned electronic curtain.
An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery

Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors' handwriting to create digital prescriptions. A 'Handwritten Medical Term Corpus' dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional Long Short-Term Memory (LSTM) network. Data augmentation includes pattern Rotating, Shifting, and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.
Error-Correction Coding Using Polynomial Residue Number System

There has been a tendency to use the theory of finite Galois fields, or GF(2n), in cryptographic ciphers (AES, Kuznyechik) and digital signal processing (DSP) systems. It is advisable to use modular codes of the polynomial residue number system (PRNS). Modular codes of PRNS are arithmetic codes in which addition, subtraction and multiplication operations are performed in parallel on the bases of the code, which are irreducible polynomials. In this case, the operands are small-bit residues. However, the independence of calculations on the bases of the code and the lack of data exchange between the residues can serve as the basis for constructing codes of PRNS capable of detecting and correcting errors that occur during calculations. The article will consider the principles of constructing redundant codes of the polynomial residue number system. The results of the study of codes of PRNS with minimal redundancy are presented. It is shown that these codes are only able to detect an error in the code combination of PRNS. It is proposed to use two control bases, the use of which allows us to correct an error in any residue of the code combination, in order to increase the error-correction abilities of the code of the polynomial residue number system. Therefore, the development of an algorithm for detecting and correcting errors in the code of the polynomial residue number system, which allows for performing this procedure based on modular operations that are effectively implemented in codes of PRNS, is an urgent task.