Reviewerly/exHarmony huggingface dataset:
The Reconstruction Method of SAR Image Ambiguous Area based on Deep Learning

The ambiguities of Synthetic Aperture Radar (SAR) images have a serious impact on image quality and object detection accuracy. In recent years, Generative Adversarial Network (GAN) models based on Convolution Neural Networks (CNN) have been widely used in image generation and inpainting in the field of Computer Vision, making it possible to reconstruct SAR ambiguous regions using Deep Learning technology. Based on deep learning technology, the SAR image ambiguous area reconstruction method adopts the GAN model, which can achieve an ideal reconstruction of the marked area with SAR ambiguities. Compared with the SAR image ambiguous area reconstruction method based on traditional image processing, new method based on GAN has faster processing efficiency and better reconstruction result, can fully extract the image information, and learn the reconstruction from a large scale of area within complex background. To deal with the problem that the image reconstruction model based on deep learning has limited Receptive Field and difficult to reconstruct a large area of image, this paper introduces a conditional GAN structure based on the Fast Fourier Convolution layer (FFC layer) and studies the effect of its improvement to the feature extraction ability after the introduction of FFC. Finally, using a large-mask inpainting model based on FFC structure, a SAR image ambiguous area reconstruction method based on deep learning is purposed in this paper.
Getting started procedure of a NC machine simplified by the use of a mixed-reality training scenario

To allow increased manufacturing quality and integration in Industry 4.0, machines have become increasingly complex, resulting in increasingly difficult operating procedures and therefore a longer and more expensive operatortraining period. Moreover, to be competitive in a global market where competition is sometimes distorted by local aid, European companies must be innovative and flexible. They must therefore be able to count on competent and responsive staff capable of adapting to the various workstations. The initial and continuous training of personnel is therefore a crucial need today. The arrival on the market of AR and VR technologies makes it possible to imagine new training models generally taking into account the technical possibilities, without rethinking the educational scenarios. The work carried out in this study consists of offering novice users a set of educational scenarios and an augmented reality device for handling a 3D printer. A first work carried out on a small group of students tests the autonomy of the users with this new material. A second experiment carried out on 80 first-year engineering school students made it possible to quantify usability using a standardized SUS questionnaire. The results show that the level of usability varies from good to excellent, regardless of whether the user has used a VR headset before. They also validate the transmission of technical skills. To obtain this result, the observed criterion is the effective printing of a part in an autonomous manner. The global work in progress aims at providing relevant training scenarios for the use of machine tools.
Data-Driven Decision Making as a Model to Improve in Primary Education

The digital evaluation field is a new area that arises in the core of education and studies highlight the importance of editing data as well as using ICT to drive internal school improvement. Data- Driven Decision Making (DDDM in advance) executes relatively simple models on carefully targeted data extracted through target questionnaires. This article contributes to the creation of a DDDM plan that considers the evaluation of a primary school in Greece. The research design is based on the DigCompOrg model and uses a quantitative technique through a questionnaire. The results presented include the analysis of the teaching team. Extracted data enabled the researchers to identify the requirements that the specific school must meet in order to proceed with self-evaluation in its digitalization process. The percentage results for teachers’ self-perception of ICT use in lessons, teachers’ digital competence, digital content use, pedagogical evaluation, digital communication with parents and digital support of school leadership indicated that significant changes in ICT integration continue to occur in the specific primary school, ICT culture and most of its components. For these reasons, this article presents a proposal for a DDDM theoretical model plan for primary school improvement presented at the end.
Examining Gender Bias of Convolutional Neural Networks via Facial Recognition

Image recognition technology systems have existed in the realm of computer security since nearly the inception of electronics, and have seen vast improvements in recent years. Currently implemented facial detection systems regularly achieve accuracy rates close to 100 percent. This includes even challenging environments, such as with low light or skewed images. Despite this near perfect performance, the problem of gender bias with respect to accuracy is still inherent in many current facial recognition algorithms. This bias needs to be addressed to make facial recognition a more complete and useful system. In particular, current image recognition system tend to have poor accuracy concerning underrepresented groups, including minorities and female individuals. The goal of this research is to increase the awareness of this bias issue, as well as to create a new model for image recognition that is gender independent. To achieve this goal, a variety of Convolutional Neural Networks (CNNs) will be tested for accuracy as it pertains to gender bias. In the future, the most accurate CNNs will then be implemented into a new network with the goal of creating a program which is better able to distinguish individuals with a high accuracy, but without gender bias. At present, our research has identified two specific CNNs, VGG-16 and ResNet50, which we believe will be ideal for the creation of this new CNN algorithm.
Design and development of an IoT device provided with a voice interface to improve treatment adherence in polymedicated patients

The population pyramid is inverting in the Western world, and this demographic transition brings new challenges. The search for healthy aging has made the use of new technologies for assistance, not only in specialized centers but also in the home itself. In recent years, many advances have been made in medical devices at home, with particular emphasis on devices to help take medication. The poor (or not) following of the instructions given by the medical personnel to the patients for taking medication causes thousands of avoidable deaths every year. In this work, a new approach to smart pillbox is proposed, combining the power of voice assistants with telecare devices. An smart pillbox with integrated Alexa was developed, and the foundations are laid for the use of voice assistants in healthcare.
Argumentation by Analogy and Weighing of Reasons

John Woods and Brent Hudak’s theory on arguments by analogy (1989), although correct in its meta-argumentative approach, gives rise to problems when we consider the possibility of weighing reasons. I contend that this is an outcome of construing the relationship between the premises and the conclusion of arguments compared in argumentation by analogy as inferences. An interpretation in terms of reasons is proposed here. The reasons-based approach solves these problems and allows the theory to be extended to account for a particular variant of argumentation by analogy in which the subjects of comparison are not arguments, but weighings of reasons.
Augmented reality in teaching about physics: first findings from a systematic review

Abstract Augmented reality (AR) makes it possible to overlay digital content onto our view of real-world phenomena. This potentially facilitates learning of physics by visualizing connections between concrete physics phenomena and abstract physics formalism. Here we present a part of our systematic review of earlier research on the use of augmented reality (AR) in school and university teaching physics topics. Our systematic review includes 60 articles published between 2012 and 2020, indexed in the Scopus and Eric databases. We analyzed the technological properties of AR for different content areas of physics as well as various methodological aspects of earlier AR research in physics education (e.g., educational level of participants, sample size, and research design). It has been shown that AR becomes increasingly popular in the physics education research community.
Average-Case Verification of the Quantum Fourier Transform Enables Worst-Case Phase Estimation

The quantum Fourier transform (QFT) is a key primitive for quantum computing that is typically used as a subroutine within a larger computation, for instance for phase estimation. As such, we may have little control over the state that is input to the QFT. Thus, in implementing a good QFT, we may imagine that it needs to perform well on arbitrary input states. Verifying this worst-case correct behaviour of a QFT-implementation would be exponentially hard (in the number of qubits) in general, raising the concern that this verification would be impossible in practice on any useful-sized system. In this paper we show that, in fact, we only need to have good average-case performance of the QFT to achieve good worst-case performance for key tasks – phase estimation, period finding and amplitude estimation. Further we give a very efficient procedure to verify this required average-case behaviour of the QFT.
Optimization of the Decision Tree Method using Pruning on Liver Disease Classification

The amount of data about liver disease can be used to become information that can be extracted using the decision tree data mining method. However, there is a weakness in the decision tree method, namely over-fitting the resulting tree can produce a good model in training data but normally cannot produce a good tree model when applied to unseen data. Based on experiments conducted using datasets taken from The UCI Machine Learning Repository database is the ILPD dataset which contains 583 clinical data with 10 attributes with a target output of 416 positive liver and 167 negative liver. The results show that the decision tree algorithm using pruning and without pruning has been tested showing an increase in accuracy. The results of the decision tree performance without pruning generated in the confusion matrix for the accuracy measure, which is 73.58 %. While the results of the system performance using the pruning method have an accuracy of 73.76%. Although the accuracy value is slightly adrift, it can prove that the decision tree method using the pruning method has much better accuracy. In addition, the models and rules generated by the decision tree can be used as the basis for developing a prototype application for liver disease classification.
Quantum homomorphic broadcast multi-signature based on homomorphic aggregation

Quantum multi-signature has attracted extensive attention since it was put forward. Beside its own improvement, related research is often combined with other quantum signature. However, this type of quantum signature has one thing in common, that is, the generation and verification of signature depend heavily on the shared classical secret key. In order to increase the reliability of signature, the homomorphic aggregation technique is applied to quantum multi-signature, and then we propose a quantum homomorphic multi-signature protocol. Unlike previous quantum multi-signature protocols, this protocol utilizes homomorphic properties to complete signature generation and verification. In the signature generation phase, entanglement swapping is introduced, so that the individual signatures of multiple users are aggregated into a new multi-signature. The original quantum state is signed by the shared secret key to realize the verification of the signature in the verification phase. The signature process satisfies the homomorphic property, which can improve the reliability of the signature.
ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy

Human detection is an important task in computer vision. It is one of the most important tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology. Despite modern techniques, there are very few optimal techniques to construct networks with a small size, deep architecture, and fast training time while maintaining accuracy. ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets.ReSTiNet is 10.7 MB, five times smaller than Tiny-YOLO. On Tesla k80, mAP is 27.3% for MS COCO and 63.74% for PASCAL VOC. The validation of the proposed ReSTiNet model has been done on INRIA person dataset using the Tesla K80.•All the necessary steps, algorithms, and mathematical formulas for building the net- work are provided.•The network is small in size but has a faster detection speed with high accuracy.
Implementasi Teori Belajar Behavioristik dalam Pembelajaran Matematika

The purpose of this study is to describe the advantages and disadvantages and implementation of Behavioristic learning theory in mathematics learning. The research method used is literature research and conducting studies with Behavioristic learning theorists and using secondary data. The results of the study are as follows: (1) behavioristic learning theory is good for students who are still children or at the elementary and some junior high school levels, (2) this theory is good for use in lessons that require speed, spontaneity, flexibility, etc., (3) simple and easy to apply because they do not pay much attention to elements.&#x0D;
Quantum Computing

Abstract: Quantum computing is a modern calculation method that is based on the science of quantum mechanics. These phenomena include the bizarre behavior of particles at the atomic and subatomic levels, and the way that these particles can be in multiple states simultaneously. The field of computer science is a great mix of physics, math, and information theory. This technology provides high computing power, low power consumption, and exponential speed by controlling the behavior of small physical objects, such as atoms. Atoms, electrons, photons, etc. are all elements of the physical world. We would like to introduce the basics of quantum computing, and some of the ideas behind it. This article begins with the origins of the classical computer and discusses all the improvements and transformations that have been made due to its limitations thus far, then moves on to the basic operations of quantum computing and results in quantum properties such as superposition, entanglement, and interference.
Developing the Prototype of Text-Based Learning Materials for the Teaching of Reading Skills at the Middle Schools

The objectives of this research are to find out the learning needs of students of VIII grade in improving their reading skills and to develop suitable reading learning materials using a genre-based approach for students in the second semester of the middle schools. This research is categorized into research and development (R &amp; D). The steps of this research are needs analysis, product development, evaluation by the expert, revision, and final product design. The data were in the form of quantitative data obtained using a questionnaire and qualitative in the form of open-ended questions, then the reading materials were revised based on the experts’ suggestions. This study indicated each chapter contained: chapter number and chapter title, let's start, let's learn the model, let's work together, let's create and improve, follow-up, let's reflect, let's summarize, and mini dictionary as that the appropriate supplementary reading materials concerning the text-based reading materials were successfully developed through development models, namely Define, Design, Develop, and Disseminate (Four-D Model). Finally, the developed materials are considered appropriate for the subjects of the study based on the results from the expert judgment questionnaire. The aspects of this questionnaire were content, instructional design, language, learning resources, and graphics. It can be concluded from this data that each aspect of the developed materials was considered "feasible" according to the expert.
Three-Stage Tone Mapping Algorithm

In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.
Nonadiabatic geometric quantum computation of W-state codes using invariant-based reverse engineering

Abstract In this paper, we propose a protocol to realize nonadiabatic geometric quantum computation (NGQC)of W -state codes in a spin system using invariant-based reverse engineering. The Heisenberg XY interaction of spin qubits provides a two-dimensional computational subspace spanned by a pair of W states. By applying a time-dependent magnetic on the spin qubits, we realize the effective Pauli operations for the computational subspace. Assisted by the invariant-based reverse engineering, the waveform of the control field is designed and the evolution paths for the NGQC is found. The performance of the protocol under the influence of experimental imperfections is estimated by the numerical simulations with available parameters. The results demonstrate that the protocol is robust against systematic error, random noise and decoherence. Therefore, the protocol may be promising to implement fast and robust manipulation of W states in spin systems.
A scalable, black-box hybrid genetic algorithm for continuous multimodal optimization in moderate dimensions

Optimization problems can be found in many areas of scienceand technology. Not only the global optimum, but also a(large) number of near-optima are often of interest. Thisgives rise to what are referred to as multimodal optimizationproblems. In most cases, the number and quality of the optimaare unknown and assumptions cannot be made about theobjective functions. In this paper, we focus on continuous,unconstrained optimization in moderately high-dimensionalcontinuous spaces (d ≤ 10).We present a scalablealgorithm with virtually no parameters, which performs wellfor general objective functions (non-convex, discontinuous).It is based on two well-established algorithms (CMA-ES,deterministic crowding). Novel elements of the algorithminclude the detection of seed points for local searches andcollision avoidance, both based on nearest neighbors, and astrategy for semi-sequential optimization to realize scalability.The performance of the proposed algorithm is numericallyevaluated using the CEC2013 niching benchmark suite for1 − 20 dimensional functions, and a 9 dimensional real-worldproblem from constraint optimization in climate research.The algorithm performs well in relation to the CEC2013benchmarks and only falls short on higher dimensional andstrongly inisotropic problems. In the case of the climate-related problem, the algorithm is able to find a high number(&gt; 150) of optima of relevance to climate research. Theproposed algorithm does not require special configuration forthe optimization problems considered in this paper, i.e., itshows good black-box behavior.
Convergence condition of simulated quantum annealing for closed and open systems

Simulated quantum annealing is a generic classical protocol to simulate some aspects of quantum annealing and is sometimes regarded as a classical alternative to quantum annealing in finding the ground state of a classical Ising model. We derive a generic condition for simulated quantum annealing to converge to thermal equilibrium at a given, typically low, temperature. Both closed and open systems are treated. We rewrite the classical master equation for simulated quantum annealing into an imaginary-time Schr\"odinger equation, to which we apply the imaginary-time variant of the asymptotic adiabatic condition to deduce the convergence condition. The result agrees qualitatively with a rigorous convergence condition of simulated quantum annealing for closed systems, which was derived from the theory of inhomogeneous Markov processes. Also observed is qualitative agreement with a rigorous convergence condition of quantum annealing for closed systems under the real-time Schr\"odinger dynamics. This coincidence of convergence conditions for classical stochastic processes for simulated quantum annealing and the real-time quantum dynamics for quantum annealing is highly nontrivial and calls for further scrutiny.
Strategies for Collaboration: ICOLC Open Collaboration in Library Consortia Recommendations

Library consortia possess unique opportunities to marshal resources of a library collective toward a common goal. The report of the International Coalition of Library Consortia (ICOLC) task force outlines strategies to reframe what libraries should be doing differently with commercial vendors and lays out a vision where libraries focus on community-owned efforts within a framework for supporting these projects long-term. ICOLC formed a task force for Open Collaboration in Library Consortia as part of a reset of its effort to find tangible recommendations for libraries to change the environment of its infrastructure, software, and content within a library services platform ecosystem.