Reviewerly/exHarmony huggingface dataset:
Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives

Answering questions is an essential learning activity on online courseware. It has been shown that merely answering questions facilitates learning. However, generating pedagogically effective questions is challenging. Although there have been studies on automated question generation, the primary research concern thus far is about if and how those question generation techniques can generate answerable questions and their anticipated effectiveness. We propose Quadl, a pragmatic method for generating questions that are aligned with specific learning objectives. We applied Quadl to an existing online course and conducted an evaluation study with in-service instructors. The results showed that questions generated by Quadl were evaluated as on-par with human-generated questions in terms of their relevance to the learning objectives. The instructors also expressed that they would be equally likely to adapt Quadl-generated questions to their course as they would human-generated questions. The results further showed that Quadl-generated questions were better than those generated by a state-of-the-art question generation model that generates questions without taking learning objectives into account.
DeepGan-Privacy Preserving of HealthCare System Using DL

The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation is a technique that is expensive to compute and does not handle the huge variety of different assaults that may be made on pictures. The primary objective of the system that has been described is to provide evidence of a complex framework in which deep neural networks have been applied to improve the efficiency of basic encryption techniques. Our research has led to the development and proposal of an enhanced version of methods that have previously been used to encrypt pictures. Instead, the generative adversarial network (GAN), commonly known as GAN, will serve as the learning network that generates the private key. The transformation domain, which reflects the one-of-a-kind fashion of the private key that is to be formed, is also meant to lead the learning network in the process of actually accomplishing the private key creation procedure. This scheme may be utilized to train an excellent Deep Neural Networks (DNN) model while instantaneously maintaining the confidentiality of training medical images. It was tested by the proposed approach DeepGAN on open-source medical datasets, and three sets of data: The Ultrasonic Brachial Plexus, the Montgomery County Chest X-ray, and the BraTS18. The findings indicate that it is successful in maintaining both performance and privacy, and the findings of the assessment and the findings of the security investigation suggest that the development of suitable generation technologies is capable of generating private keys with a high level of security.
Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey–Particle Swarm Optimization

Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications. Owing to severe air dispersion, fog, and haze over the environment, hazy images pose specific challenges during information retrieval. With the advances in the learning theory, most of the learning-based techniques, in particular, deep neural networks are used for single-image dehazing. The existing approaches are extremely computationally complex, and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon. However, the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing networks. This article proposes a new architecture “Atrous Convolution-based Residual Deep Convolutional Neural Network (CNN)” method with hybrid Spider Monkey-Particle Swarm Optimization for image dehazing. The large receptive field of atrous convolution extracts the global contextual information. The swarm based hybrid optimization is designed for tuning the neural network parameters during training. The experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo effects. It is observed from the statistical analysis that Mean Square Error (MSE) decreases from 74.42 to 62.03 and Peak Signal to Noise Ratio (PSNR) increases from 22.53 to 28.82. The proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.
A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval (CBMIR) is a technique for retrieving medical images based on automatically derived image features. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. Several methods are applied to enhance the retrieval performance of CBMIR systems. Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems. This study proposes the relative difference-based similarity measure (RDBSM) for CBMIR. The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features. Furthermore, the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks (CNNs) models. Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets, Kvasir and PH2, in terms of recall and precision retrieval measures. The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.
SFSDA: Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems proving and experimentative verification is launched. We find that in a specific scenario where the encrypted data constructed by a smart meter (SM) exceeds the size of one Paillier ciphertext, the malicious fog node (FN) may use the received ciphertext to obtain the reading of the SM. Secondly, to avoid the possibility of privacy disclosure under certain circumstances, additional hash functions are added to the individual encryption process. In addition, fault tolerance is very important to aggregation schemes in practical scenarios. In most of the current schemes, once some SMs failed, then they will not work. As far as we know, there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance. Finally, we construct the first secure flexible subset data aggregation (SFSDA) scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation, where FN enables the control server (CS) to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts. Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function, and only has a small delay in implementing fault-tolerant data aggregation.
Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models. The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video. The performance of the proposed method was evaluated using varying parameter settings, such as the size of the action clip and interval between action clips. The model achieved an accuracy of 0.9817, indicating excellent performance. Therefore, we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.
Learning When to Defer to Humans for Short Answer Grading

To assess student knowledge, educators face a tradeoff between open-ended versus fixed-response questions. Open-ended questions are easier to formulate, and provide greater insight into student learning, but are burdensome. Machine learning methods that could reduce the assessment burden also have a cost, given that large datasets of reliably assessed examples (labeled data) are required for training and testing. We address the human costs of assessment and data labeling using selective prediction, where the output of a machine learned model is used when the model makes a confident decision, but otherwise the model defers to a human decision-maker. The goal is to defer less often while maintaining human assessment quality on the total output. We refer to the deferral criteria as a deferral policy, and we show it is possible to learn when to defer. We first trained an autograder on a combination of historical data and a small amount of newly labeled data, achieving moderate performance. We then used the autograder output as input to a logistic regression to learn when to defer. The learned logistic regression equation constitutes a deferral policy. Tests of the selective prediction method on a held out test set showed that human-level assessment quality can be achieved with a major reduction of human effort.
A Low-Cost and High-Performance Cryptosystem Using Tripling-Oriented Elliptic Curve

Developing a high-performance public key cryptosystem is crucial for numerous modern security applications. The Elliptic Curve Cryptosystem (ECC) has performance and resource-saving advantages compared to other types of asymmetric ciphers. However, the sequential design implementation for ECC does not satisfy the current applications’ performance requirements. Therefore, several factors should be considered to boost the cryptosystem performance, including the coordinate system, the scalar multiplication algorithm, and the elliptic curve form. The tripling-oriented (3DIK) form is implemented in this work due to its minimal computational complexity compared to other elliptic curves forms. This experimental study explores the factors playing an important role in ECC performance to determine the best combination that leads to developing high-speed ECC. The proposed cryptosystem uses parallel software implementation to speed up ECC performance. To our knowledge, previous studies have no similar software implementation for 3DIK ECC. Supported by using parallel design, projective coordinates, and a fast scalar multiplication algorithm, the proposed 3DIK ECC improved the speed of the encryption process compared with other counterparts and the usual sequential implementation. The highest performance level for 3DIK ECC was achieved when it was implemented using the Non-Adjacent Form algorithm and homogenous projection. Compared to the costly hardware implementations, the proposed software implementation is cost effective and can be easily adapted to other environments. In addition, the power consumption of the proposed ECC is analyzed and compared with other known cryptosystems. thus, the current study presents a detailed overview of the design and implementation of 3DIK ECC.
Eliciting Proactive and Reactive Control During Use of an Interactive Learning Environment

The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Although these mechanisms are relevant to learner behaviors during interaction with intelligent tutoring systems (ITS), their relation to ITSs is under-researched. We propose a manipulation to induce proactive or reactive control during interaction with an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either mode impacted learning. Study 2 investigates if alternating between control modes during problem solving affects student performance.
Constant-Factor Approximation Algorithms for Some Maximin Multi-clustering Problems

We consider several problems of searching for a family of non-intersecting subsets in a finite set of points of Euclidean space. In these problems, it is required to maximize the minimum cluster’s cardinality under constraint on each cluster’s scatter. The scatter is the sum of the distances from the cluster elements to the center, which is defined differently in each of the problems. We show that these problems are NP-hard in the case of an arbitrary fixed number of clusters and propose polynomial constant-factor approximation algorithms for this case.
Developing an integrated Local Culture Padlet Based Writing Course Using Systemic Functional Linguistics Genre Based Pedagogy

Abstract. Due to the implementation of distance learning policy in Indonesia, the usual class is suspended as a response to the COVID-19 pandemic outbreak. Since then, the demand of innovative teaching involving the use of computer assisted learning tools increases progressively. Undeniable, a distance learning scheme has not been able to outperform a conventional face-to-face classroom learning. One of the problems was encountered in Production Écrite Elimentaire (hereafter PEE) class, where lecturers could not monitor students’ writing progress directly. This study aims at developing a Padlet based writing course by integrating local culture using Systemic Functional Linguistic Genre Based pedagogy (SFL-GBP) adopting ADDIE approach. In order to achieve this objective, three lecturers and 32 students were involved to validate the result of Padlet product. This research used two kinds of instruments i.e., an open-ended questionnaire and an experts’ rubric. The open-ended questionnaire was used to identify students’ needs in PEE course, and the experts’ rubric was used to examine Padlet by regarding the appropriateness of material and language, the procedure of SFL-GPB, and the media. The overall mean scores of the experts' judgement towards the Padlet feasibility shows significant figures, i.e., the material aspects gained 3.7, the appropriateness was 3.8, and the media reached 3.7. On the other hand, the results of the students' opinion analysis also show a positive figure where the mean score for material as well as language aspects were 3.9, and media aspect had 3.75 mean score. The findings. The findings of this study were expected to have a further contribution to the development of comprehensive teaching syllabuses, materials, as well as lesson plans for French-language classrooms in the university, especially integrating local culture and the SFL Pedagogy approach. Keywords: Padlet, Writing Course, Local Culture, SFL-GBP
SACREDNESS IN CONTEMPORARY ART: RELIGIOUS INFLUENCES AND FORMS OF INTERPRETATION

The article analyzes the worldview and social contexts of a number of significant works of modern art (literature, painting, cinematography, theater art, architecture), which in one form or another cultivate sacred symbolism. The researcher also interprets the forms of cultivation of traditional meanings of world religions (Buddhism, Christianity, Islam) in modern art.
CREATIVE ACTIVITY AS A MEANS OF SELF-REALIZATION OF PERSONALITY IN SOCIETY

Studies of creativity originate in ancient philosophy, where Aristotle in "Metaphysics" has determined that experience is "Emperia", it is associated with activity, activity is "praxis", "Froneseis" - prudence, and creativity in the philosopher appears as a philosopher "Techne", the art or skill through which "Evdomonia" occurs, that is, self-realization of a person that is possible through the realization of one's own abilities [2]. Creativity is often considered in the context of activity, as a result of which a person creates qualitatively new material and spiritual values, as a person's ability to create a new socio-cultural space. Man, through activity, transforms the world, adapts the environment to their needs, forms a culture. As a result of the activity, the creative potential of a person is realized. Thanks to creative activity, the process of realizing the needs of both individuals and society as a whole. As a form of culture, creative activity is a higher activity. Creativity as a separate activity is characterized by certain features, the presence of a problematic situation, original and new approaches to its solution, creativity, social significance, uniqueness of the process of activity itself and the novelty of the results. The abilities and properties of the individual are the source of creativity, their interaction is the driving force of the creative process. Activities “Only then can scientific philosophical comprehensive when it - activity - is regarded as amateur activity” [7, p. 109]. Self-realization of personality occurs through the disclosure of creative potential, which has such traits as giftedness, creativity, readiness for various forms of activity and productivity. According to V. Karpenko, creative potential is "needs, value orientations, personality quality, inclinations, hidden structured resources, reserves, creative impulses, internal energy, productive forces, needs of knowing oneself and others" [4, p. 192].
THE PHENOMENON OF THE UNIVERSITY IN THE EDUCATIONAL SPACE: TRADITIONS BEFORE CONTEMPORARY CHALLENGES

The article is devoted to the study conceptual aspects of socially responsible management as a factor overcoming the crisis effectiveness of the COVID-19 pandemic. Socially responsible manage-ment permeates all social spheres: communication, behaviour, subordination of managers with subordinates, commu-nication, information and communication process, information production, health care, human existence of an individual.
Feature Extraction Based on Cellular Particle Swarm Optimization Algorithm for American Sign Language Images

Image Recognition is becoming a critical task and many problem-solving systems and approaches for image detection, analysis and classification are introduced by many modern researcher. The techniques should be user friendly and ease to interpret for both normal people and special people, it they should be analysed. The sign language act as a mode to transfer and exchange the message, information, knowledge and ideas from deaf to common people. The gaining information and responses to the pattern or gesture is called as sign. Sign Language is only mode to communication between the hearing-impaired people and common people. In this method each individual gesture is called as sign. In this system the American Sign Language recognition system which is used for visually impaired people to access and educate the special children’s. American Sign Language (ASL) images of a special people is collected with some constraints are taken as the dataset. The objective of the research work is done with three different Phase. Phase1 is pre-processing, Phase 2 is segmentation and Phase 3 is feature extraction for static hand gestures with the maximum possible accuracy rate. A novel approach for the proposed system is analysed with its structural feature extraction and compared with its parameters. The novel feature extraction is made with 8 different structural features they are Bounding Box, Area, Perimeter, Centroid, Roundness, EquiDiameter, Number of Boundaries and Angle. F-Measure, Recall and Precision based on this Accuracy, Sensitivity and Specificity are measured for features are extracted images. The accuracy of the new Phase has been found significantly higher.
Fuzzy Logic

Fuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. This paper defines fuzzy logic and discusses its key concepts, mathematical underpinnings, and applications. We look at the benefits and drawbacks.
Implementing Virtual Private Cloud Service Controls

In computer engineering, networking and security go hand in hand. There is no way to effectively design a network architecture without considering security. The opposite holds true as well.
Importing an Image into a Button

Chapter Goal: You’ll create a button that will import an icon image using a script. This could be useful for clients who want to attach an image or photo to the form.
Working with List Boxes

Chapter Goal: The last main type of form field you are going to explore is list boxes. In my experience, list boxes are the most underused form field. While they may not be as compact as drop-downs, they are great for helping organize a list of priorities. Let’s explore some ideas for working with list boxes in various projects. Refer to Figure 13-1.
Various JavaScript Alerts, Notes, and Time Stamps

Chapter Goal: In this chapter you’ll work with alerts that may or may not be part of a form. You’ll also create comment notes and add a signature and time stamp.
ANOVA and Mixed Models: A Short Introduction Using R

"ANOVA and Mixed Models: A Short Introduction Using R." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–2
Fractals and Tomographs

Fractals represent patterns which can be visualized with an artist’s eye. MWs and voltages of circuit elements are numbers which have no physical shapes but, like an artist, one can, assign shapes to these variables and scale them as well. The shapes may not be useful for numeric manipulation; nevertheless, they lend themselves to comparison with natural fractals, especially when several of them come together to form a new one. Theory of fractals by Mandelbrot was inspired by natural repetitive shapes, but the theoretical development employed functions of a complex number which makes the interpretation more difficult. Natural reality is lost. Power system performance consists of real flows and real voltage magnitudes possess visibility and are meaningful to grid operators.