Reviewerly/exHarmony huggingface dataset:
Comparative Study of Class Situations in Two Different Instructional Designs of Flipped Learning －Focusing on the Perception of Instructors and Learners

This study aimed to explore the effective strategies in comparison with instructional designs: the face-to-face and online in-class instructions of flipped learning. For this purpose, a survey was developed and the perceptions of 22 instructors and 203 students were examined. The data analysis were conducted using content analysis. The findings were as follows: First, the survey found that several commonalities pertaining to learning aspects were derived from the students’learning activities regardless of the instructional designs. However, the dissimilarity was found in the manner in which the instructors provided their feedback. Second, 8 satisfaction determinant factors, such as space-time convenience, improvement of learning performance, teaching-learning method, facilitation of in-class activities, facilitating of communication, enhancing students’understanding, repeated learning, and self-directed learning were derived from the learners’ perception analysis. In addition, learners perceived 4 main aspects as dissatisfaction determinant factors: environmental obstacles to learning, limit of understanding, lack of communication, and an excessive amount of learning. Irrespective of instructional designs, no major difference was found in the learners’ perceptions. Third, instructors commonly perceived that flipped learning classes involved 6 implemental characteristics: motivation of learning, problem-solving, application activities, feedback, facilitating communication, and follow-up instruction. Furthermore, 6 difficulties on were also registered: implementation, communication, class operation time, instructional environment, the training program, and regulations. However, some differences in details were found based on the class designs. In conclusion, implications and suggestions for implementing flipped learning classes and following studies have been discussed.
Qutrit state tomography via optimal unambiguous state discrimination and mutually unbiased measurements

We propose an efficient scheme for qutrit state tomography based on optimal unambiguous discrimination of three nonorthogonal pure qutrit states. In our scheme, we first introduce an ancillary qubit initially prepared in a known state and apply a joint unitary transformation on the whole system composed of a qutrit state and an ancillary qubit state. Afterwards, we make a local von Neumann measurement (VNM) on the ancillary qubit. If it is projected onto the state |0〉, we finally perform mutually unbiased measurements (MUMs) on the collapsed qutrit state arbitrarily chosen from a complete set of four mutually unbiased bases (MUBs). With only six properly-chosen projectors, the real and imaginary parts of non-diagonal elements can be entirely obtained. Together with the diagonal elements obtained directly from one VNM in the computational bases, the density matrix of an arbitrary unknown qutrit state can be fully determined. Compared with the previous work (R. Salazar and A. Delgado 2012), our scheme consumes much less resources. In comparison with MUBs-based tomography scheme (H. Yuan, et al., 2016), our scheme requires more measurements and less projectors, but pays the price of a loss of certain fraction identically prepared states. Our proposal can be extended to the case of other d-dimensional quantum states if their d MUBs are exactly known.
Gains and losses in online learning of public and business administration students during COVID 19 pandemic: the educators’ perspective

The research of the educators’ perspectives on online learning of undergraduate, graduate and doctoral students of public/ business administration conducted in the USA, Ukraine and a number of Asian countries, performed via mixed methodology combining qualitative (interviews and survey) and quantitative research methods, revealed general tendencies in the educators’ perceptions of losses and gains of online education. The prevalence of positive overall assessments, as well as numerous gains and opportunities for teachers and students show the prospects of online education development, specifically for working adults. The major benefits of learning online for the students include developing online communication and collaboration skills, teambuilding and teamworking skills, along with more opportunities to get education abroad without leaving their countries and interrupting jobs. For the faculty modern interactive and integrated online modality opens more opportunities for professional development as well as flexibility of schedules, savings on time and resources and ultimately better work/life balance. The analysis of problems in online learning revealed areas that require special attention of the educational institutions as well as educators, such as: creating the institutional basis for adapting the syllabi and developing new approaches to online teaching; providing the platforms for training and professional development; supporting the creation and development of multimodal instructional materials; introducing students’ learning outcomes testing methodologies which allow measuring the students’ progress and professional competences, skills and attitudes without close monitoring of their use of resources. With technological innovations, multimedia and interactive teaching tools, real-time learning tests and quizzes, online hides a huge potential for moving educational technologies and methodology to a new quality level, enriching onsite and hybrid modalities with new content and tools.
Designing systems for mobile collaboration

Due to the widespread use of mobile communication devices, the mobility in everyday life including mobile working habits, a variety of collaborative software applications accompany us every day. In this paper, we aim to capture and describe the essence of the design solutions implemented in these applications in the terms of design patterns. To achieve this goal, we examined relevant services and applications in literature that enable people to collaborate on the go. Furthermore, the geographic characteristics of place, space, and mobility are discussed with regard to location-based collaboration including references to the theory of relativity. As result 11 design patterns for mobile collaboration have been elaborated. The design patterns describe, in addition to basic technologies, generic map services and privacy preserving solutions. In particular the patterns Geo Fencing, Locale Me, Geo Tracing, and Substitute Position are presented in detail. These patterns are intended to guide computer science teachers and software developers on how to design mobile collaboration systems that mediate collaboration in physical space through interactions in virtual space.
Using Degree Centrality to Identify Market Manipulation on Bitcoin

In 2014, the Mt.Gox Bitcoin exchange had its internal dataset hacked and leaked. After that, some studies employ this dataset to evaluate if Mt.Gox was doing market manipulation on Bitcoin. Also, they identify patterns of this manipulation. Based on these studies, this paper analyzes the Bitcoin blockchain in the period where Mt.Gox was active. We model the transactions in the blockchain as a graph and evaluate the degree centrality of each node. We thus analyze how the ranking of nodes with the highest centrality values changes over time. Our conclusions indicate that top nodes are stable, but there is a period where it changes. To better understand this behavior, we simulate the insertion of transactions in the network and verify how the ranking changes. As a result, we provide indications that we can use ranking changes to detect malicious activities. We also show a case study using this ranking to predict abnormal behavior in the network.
NNOVATIVE FORMS OF TEACHING UKRAINIAN LITERATURE TO 7TH-GRADE STUDENTS OF SECONDARY SCHOOL

This research raises the important issues of the innovative education in general and the introduction of innovative organizational forms and techniques into the process of teaching Ukrainian literature in the 7th grade of a secondary school, in particular. In this sense, the paper aims at investigating the pedagogical essence and practical value of innovative forms of teaching in a secondary school. Thus, the authors consider the concept of «innovative lesson» in comparison to a classic lesson. The paper offers several classifications of modern innovative lessons used by teachers in the academic process and assume that there is no unified typology offered in literature. The implementation of innovative forms of teaching entails observing a range of requirements related to a lesson structure and aims that agree with every lesson stage, contextual and organizational aspects as well as teaching techniques. The paper discusses problems and difficulties that in-service teachers encounter when introducing innovative lessons in practice. Based on the assumptions made from literature and lesson observations conducted during teaching training practice (1 semester of 2020-2021 academic year) the innovative lesson-research was worked out and introduced to 7th graders. The survey on implementing the innovative lessons shows that both teachers-practitioners and students demonstrate positive perceptions of iinovative lessons and readiness to use them in practice. The conducted lesson-reseach «My Native Land Literature» with 7th graders has received positive feedbacks as well. The research results prove that innovative forms of teaching Ukrainian literature in a secondary school can be one of the effective ways to modernize educational process and motivate learners. The prospective of further research lies in creating a collection of innovative lessons in Ukrainian literature for 7 th grade students of a secondary school.
Classification of Artificial and Real Objects Using Faster Region-Based Convolutional Neural Networks

Object detection and classification tasks can be addressed effectively using machine learning (ML) methods that use convolutional neural networks (CNNs) and region-based convolutional neural networks (R-CNNs). In this study, the ability of R-CNNs to distinguish between digital images of artificial and real objects is evaluated. A single-shot detection (SSD) network is also developed to serve as a baseline approach and for comparative evaluation. Experiments are designed using several images of real and artificial leaves as inputs to the R-CNNs that are trained and tested with different proposal areas of the images. The performances of R-CNNs and SSDs are evaluated using mean average precision (mAP) measure. Results from this study indicate that trained R-CNN s perform well in classification of real and artificial leaves and are robust in performance against changes in many of the experimental factors including minimal training data and resolution of the images. R-CNNs have also performed better than SSDs in the classification tasks with higher values of mAP. The performance of R-CNNs is affected by the proposal area, or the number of subsections the R-CNNs utilizes to determine distinct characteristics of the objects (i.e., leaves) presented. Results based on limited experiments from this study indicate the R-CNNs and their variants are ideally suited for object classification tasks with numerous real-world applications.
Trends and Sentiment Analysis of Movies Dataset Using Supervised Learning

Social media has become very popular nowadays. In our research, we have analyzed the trends of movies/shows that people watch. There are two steps in our proposed work. In the first step, we have performed classification of the movie genres using multilabel classification. Further, TextBlob has been used to perform sentiment analysis on tweets. The movie dataset is used for the experimentation work. The highest accuracy (85.33%) is obtained using the binary relevance and Gaussian NB classifier. Another result that we obtained is that the comedy genre was observed to have the highest percentage of positive tweets while the horror genre was observed to have the highest percentage of negative tweets. That means, people prefer to watch comedy movies as compared to other categories.
Adaptive Integer Quantisation for Convolutional Neural Networks through Evolutionary Algorithms

State-of-the-art Convolutional Neural Networks (CNNs) have become increasingly accurate. However, hundreds or thousands of megabytes data are involved to store them, making these networks also computationally expensive. For certain applications, such as Internet-of-Things (IoT), where such CNNs are to be implemented on resource-constrained and memory-constrained platforms, including Field-Programmable Gate Arrays (FPGAs) and embedded devices, CNN architectures and parameters have to be small and efficient. In this paper, an evolutionary algorithm (EA) based adaptive integer quantisation method is proposed to reduce network size. The proposed method uses single objective rank-based evolutionary strategy to find the best quantisation bin boundary for fixed quantised bit width. The performance of the proposed method is evaluated on a small CNN, the LeNet-5 architecture, using the CIFAR-10 dataset. The aim is to devise a methodology that allows adaptive quantisation of both weights and bias from 32-bit floating point to 8-bit integer representation for LeNet-5, while retaining accuracy. The experiments compare straight-forward (linear) quantisation from 32-bits to 8-bits with the proposed adaptive quantisation method. The results show that the proposed method is capable of quantising CNNs to lower bit width representation with only a slight loss in classification accuracy.
Software Requirements Change Prediction Model

The software industry is rising gradually; in this modern era software projects are complex due to the involvement of many factors, so change is unavoidable in projects. The change in requirements predicted earlier can preserve precious resources. Therefore, factors related to stakeholders, developers, and experts having a higher impact on requirements change should be analyzed in detail to predict requirements change at the early stages of software development. To address these limitations, a change prediction process model is proposed based on the weaknesses of existing literature. The knowledge related to these factors is incorporated in the Bayesian network; the results obtained are promising.
Hardware Implementation of Classical and Bipolar Morphological Models for Convolutional Neural Network

The paper is dedicated to the convolutional neural networks hardware solutions and their implementation. We describe the hardware modules developed, which are specialized in processing of classical and bipolar morphological models of neural networks.
Towards an IT Governance of DevOps Metamodel

Bringing together the teams of developers and IT operations, the DevOps approach aims for operational efficiency, By taking into account IT production constraints from the programming phase, the software is de facto more efficient, only the governance aspect. IT is not overly discussed in this repository. The objective of this research is to complete the DevOps meta-model already proposed in a previous article by adding an IT governance layer.
Regression-Based 3D Hand Pose Estimation for Human-Robot Interaction

In shared workspaces where humans and robots interact, a significant task is to hand over objects. The process of hand over needs to be reliable, a human must not be injured during the process, hence reliable tracking of human hands is necessary. To avoid collision, we apply an encoder-decoder based 2D and 3D keypoint regression network on color images. In this paper, we introduce a complete pipeline with the idea of stacked and cascaded convolutional neural networks and tune the parameters of the network for real-time applications. Experiments are conducted on multiple datasets, with low and high occlusions and we evaluate the trained models on multiple datasets for the human-robot interaction test set.
Improving Security and Privacy for Health Information and Images

Recently, the wireless devices and information technology have been evolved greatly and used in many sectors such as health and military. Employing these technologies in health system require to transmitting the patient’s information over the Internet. Hence, there is an urgent need to provide a high degree of security and privacy for patient information. This paper presents new encryption method to protect the information that are transmitted and stored in electronic health records (EHR) with maintaining its privacy. The proposal employs AES and ECC algorithms for security purpose and HMAC-SHA3 algorithm for privacy purpose. The suggested strategy was applied in different stages. ECC algorithm had been used to provides very fast key generation, and fast key agreement. A different key is generated for each session where both sender and receiver has the ability to generate the same private key. The obtained results show high level of security in both ends(Sender &amp; receiver) as they have the role of encryption and decoding according to the query. The results were analyzed and tested according to the NIST test group and thirteen steps have been successfully passed.
A Countermeasure for Advanced Encryption Standard against Power Analysis Attack Using Chaotic System

Side-channel attacks (SCAs) pose a huge threat to the information leakage of hardware cryptographic devices, and the security of cryptographic algorithms needs to be improved urgently. A countermeasure based on Advanced Encryption Standard (AES) against the power analysis attack is proposed in this paper to improve the security of the hardware-implemented AES. A hybrid chaotic map is applied to generate pseudo-random numbers for masking the intermediate data. In the AES encryption process, results of the first and last AddRoundKey operation are XORed with the mask respectively, which can help the AES with avoiding the known-plaintext attacks. Experimental results show the AES implementation using the proposed countermeasure can resist the Correlation Power Analysis (CPA) attack, even high-order attacks. Moreover, its hardware implementation has advantages over other defense methods in terms of hardware resource consumption.
Generating Explanations for Recommendation Systems via Injective VAE

Generating explanations for recommendation systems is essential for improving its transparency since informative explanations such as generated reviews can help users comprehend the reason for receiving a specified recommendation. The generated reviews should be specific for the given user, item, and rating, however, recent works only focus on designing more and more powerful decoder, merely treating this task as a plain natural language generation process. We argue that there may exist the risk that the powerful decoder neglects the input embeddings and suffers from the biases that exist in data. In this paper, we propose a novel Injective Variational Autoencoders (InVAE) for generating high-quality reviews. Specifically, we employ a Collaborative Kullback-Leibler divergences (CKL) mechanism to building a better latent space that captures meaningful information. Base on this, the Spectral Regularization on Flow-based transformation (SRF) method is designed to backward transfer the priorities of generated latent variables to the input embeddings. Therefore, our method can construct more informative input embeddings and provides more specific explanations for different inputs. Extensive empirical experiments demonstrate that our model can construct much more meaningful feature embeddings and generate personalized reviews in high quality.
Research on the Dynamic Evaluation Mode of Advanced English Teaching

Based on the dynamic evaluation theory, combined with the characteristics of English language teaching and teaching objectives, this paper constructed a dynamic evaluation mode for the "Advanced English" course. The evaluation mode divided Advanced English teaching into four teaching sections: pre-class online self-learning, classroom partner interaction internalization, after-class study notes evaluation and feedback, and subject paper project refinement and improvement; through the establishment of a " curriculum learning portfolio", evaluation runs through the entire teaching process, promotes the organic combination of teaching and evaluation, and realizes the cultivation of students' individual cognitive strategies and the stimulation of their learning potential. The dynamic evaluation mode of Advanced English course plays a positive role in shaping the cognitive structure of students and developing other related course evaluations.
Ant colony optimization model for determining the shortest route in Madura-Indonesia tourism places

Travel planning is important, especially in areas that often-become tourist destinations. Each region must have an interesting tour, one of which is on the island of Madura. With so many tours available, it confuses tourists in determining tourist routes. In addition, on the island of Madura, many traditional markets spill onto the streets on certain days which can cause traffic jams so that tourists' journeys are hampered. In this study, a research method using Ant Colony Optimization (ACO) is proposed to determine the shortest route to tourist sites on Madura Island. Ant Colony Optimization method is one method that can solve an optimization problem. In solving the problem this method is inspired by the behavior of a collection of ants. Ants function as agents assigned to find solutions to a problem. Based on the experiments carried out, the accuracy value in finding the shortest route solution was 80%. In addition, the number of tours and the magnitude of the distance also affect the execution time of the process of determining the shortest route. The more tours that are visited and the greater the distance traveled, the longer the execution time of the process of determining the shortest route.
Plurals and Modals

Abstract How does plural logic interact with modal logic? Of particular importance is the question of whether plural inclusion is a matter of necessity. Consider some things, and choose any one of them. Is the chosen thing necessarily one of the things from which it was chosen? After explaining the broader significance of the question, this chapter defends an affirmative answer. One argument is primarily philosophical and is based on the fact that a plurality is fully specified when its members have been circumscribed. Other arguments are primarily logical: an affirmative answer follows in plural logic from minimal further assumptions.
Graph Transfer Learning

Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.