Reviewerly/exHarmony huggingface dataset:
The Impact of Static Fluid E-Module by Integrating STEM on Learning Outcomes of Students

Students' success in learning can be seen from the achievement of student competencies. This means that several factors affect student learning outcomes, such as learning materials or e-module. E-module is a module packaged in electronic or digital form that contains a variety of interactive media. The situation found in the field is that the teaching materials used are still in printed form or textbooks and student learning outcomes are still in the low category of students. This study aims to analyze the effectiveness of using STEM integrated e-module on static fluid materials. This research was quasi-experimental research with a single factor independent groups design. The research method used was the development method with the Hannafin and Peck instructional development model focused on product-oriented learning design. The instruments used in the study were final tests, observations, and worksheets. The data analysis techniques used in this research were descriptive statistical analysis, normality test, homogeneity test, t-test for two unrelated samples. Data analysis showed differences in learning outcomes in the experiment group compared to the control group. This indicates the influence of the STEM-integrated static fluid E-Module on student learning outcomes. Therefore, the STEM-integrated static fluid material electronic module design can improve student learning outcomes.
Assessment Patterns during Portuguese Emergency Remote Teaching

COVID-19 certainly brought more negative aspects than positive ones to education. On the one hand, new gaps and challenges emerged from the lockdowns worldwide. On the other hand, we have been witnessing the increased relationship between technology and education, which created an opportunity for education to evolve and enhance the use of digital tools in classes. During several lockdowns worldwide, due to the pandemic crisis, millions of students and teachers were forced to continue the process of teaching and learning at home and experienced Emergency Remote Teaching (ERT), which led to new challenges on the process of students’ assessment. To understand what assessment challenges teachers face during the ERT and their patterns for evaluation, we performed a survey in Portugal where the ERT lasted several months in the last two years. The survey was validated and conducted in the first semester of 2021. We found two main patterns: (i) the group of teachers that prefer oral discussion and dialogue simulation and display disbelief towards traditional tests and educational games; and (ii) the group of teachers that tend to prefer oral simulation and display greater disbelief about educational games, dialogue simulation and peer work and review. From the survey analysis, we also found that teachers considered their students to be more distracted and less engaged in online classes. They were negatively affected both in their learning and evaluation process. Using digital tools to collect and validate data and creating patterns between collected data is essential to understand what to expect in future crises. The presented analysis should be correlated with other studies to extract patterns of knowledge from data and to be able to obtain conclusions about how to move education forward.
Intensity-Averaged Double Three-Step Phase-Shifting Algorithm with Color-Encoded Fringe Projection

Fringe projection profilometry (FPP) has been broadly employed for three-dimensional shape measurements. However, the measurement accuracy suffers from gamma nonlinearity. This paper proposes an intensity-averaged double three-step phase-shifting (IDTP) algorithm making use of color-encoded fringe projection, which does not require complex calibration processes or extra fringe patterns. Specifically, two phase maps with π/2 phase shift are encoded into the red and blue channels of color fringe patterns. The average fringe patterns of the red and blue channels are approximately in sinusoidal waveform with little harmonics, thus can be directly used for accurate phase recovery. Additionally, an adaptive weight is also estimated for average operation to suppress the effect of color crosstalk. Both simulations and experiments demonstrate that the proposed IDTP algorithm can effectively eliminate nonlinear phase errors.
An Epistemological Appraisal of Walton’s Argument Schemes

The article critically discusses Walton’s (and co-authors’) argument scheme approach to good argumentation. Four characteristics of Walton’s approach are presented: 1. Argument schemes provide normative requirements. 2. These schemata are enthymematic. 3. There are associated critical questions. 4. The method is inductive, abstracting schemata from groups of similar arguments. Four adequacy conditions are applied to these characteristics: AC1: effectiveness in achieving the epistemic goal of obtaining and communicating justified acceptable opinions; AC2: completeness in capturing the good argument types; AC3: efficiency in achieving the goals; AC4: justification of the argument schemes. The discussion reveals weaknesses in Walton’s account, including they are neither effective nor truly justified. A better alternative is an epistemological approach based on epistemological principles.
Implementation of Machine Learning Algorithm to Detect Credit Card Frauds

As the world is becoming more digitalized with every sector using the internet to flourish their businesses, online transactions have become an inevitable part of life.There has been a steady rise in the number of online transactions and this will continue to increase in the future as well.One of the major modes of online transactions is credit cards and along with its extensive use comes its major drawback, that is, credit card fraud.Machine learning plays a vital role in detecting credit card frauds as it is not possible for banks to monitor every transaction.This paper explores different machine learning algorithms used to detect credit card frauds.
Optimal Constraint Following for Fuzzy Mechanical Systems Based on a Time-Varying β-Measure and Cooperative Game Theory

This article addresses a cooperative game-oriented optimal constraint-following problem for fuzzy mechanical systems. The state of the concerned system is affected by possibly (fast) time-varying uncertainty. The fuzzy set theory is adopted to describe such uncertainty. The task is to drive the system to obey a set of prescribed constraints optimally. Since the control objective may be changing along with the system uncertainty, a time-varying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -measure is defined to gauge the constraint-following error; based on which, an adaptive robust control scheme with two tunable parameters is then proposed to render it to be uniform boundedness and uniform ultimate boundedness. For the seeking of the optimal design parameters, two cost functions, each of which is dominated by one tunable parameter, are developed with the fuzzy information, and thereout a two-player cooperative game is formulated. Finally, the optimal design problem is successfully solved: with the existence, uniqueness, and analytical expression of the Pareto optimality.
The correlation between digital literacy and parents’ roles towards elementary school students’ critical thinking

This study aimed at discovering the positive and significant relationships between digital literacy and the roles of parents together on the critical thinking skills of fifth-grade elementary school students. This study employs quantitative research, particularly correlational research. This research includes comparative causal research. This research is a quantitative research with a correlational design. The sample used was 70 students who were randomly selected. The data collection method used in this study was a survey using a questionnaire. The data analysis technique used to test the hypothesis is the product–moment correlation technique and regression multiple correlation analysis. The results of this study indicate that there is a positive and significant correlation between digital literacy and critical thinking skills. There is a positive and significant correlation between the roles of parents and critical thinking skills. There is a positive and significant correlation between digital literacy and the roles of parents together on critical thinking skills. &#x0D; &#x0D; Keywords: Digital literacy, the roles of parents, critical thinking skills.
Combined Chebyshev and logistic maps to generate pseudorandom number generator for internet of things

&lt;span&gt;Sensitive data exchanging among things over the Internet must be protected by a powerful cryptographic system. Conventional cryptographic such as advanced encryption standard (AES), and respiratory sinus arrhythmia (RSA) are not effective enough to protect internet of things (IoT) because of certain inveterate IoT properties like limited memory, computation, and bandwidth. Nowadays, chaotic maps with high sensitivity to initial conditions, strong ergodicity, and non-periodicity have been widely used in IoT security applications. So, it is suitable for IoT. Also, in a stream cipher method, the user needs to deliver the keystream to all clients in advance. Consequently, this paper proposed a method to solve the keys distribution problem based on combine both Chebyshev and logistic maps techniques as well as a master key to generate a random key. The suggested method was compared with the other stream cipher algorithms (Chacha20, RC4, Salsa20) by utilizing the same plaintext and master key as input parameters and the results were successful in the statistical national institute of standards and technology (NIST) test. Simultaneously, the suggestion was evaluated through different evaluation methods like statistical NIST test, histogram, Shannon entropy, correlation coefficient analysis, keyspace and key sensitivity, and others. All mentioned tests are passed successfully. Therefore, the suggested approach was proved it is effective in security issues.&lt;/span&gt;
Enhancing Personalized Recommendation by Transductive Support Vector Machine and Active Learning

As an important component of information service networks, personalized recommendation technology provides users with better options and enables them to obtain information anytime and anywhere. Collaborative filtering (CF) is a successful and widely used form of this technology. However, the traditional CF recommendation algorithm is ineffective in environments with frequent entry of new users and high levels of data sparsity. For new users in the system, few or no scores, labels, or other such information is available, leading to the user cold start problem. Simultaneously, data sparsity leads to the selection of unreasonable neighbors, which reduces the recommendation accuracy. In addition, the traditional CF recommendation algorithm ignores the inherent connections between users’ preferences and their basic information (such as demographics). Users with similar demographic information are likely to have similar preferences, which can serve as a good basis for finding neighbors. To address the aforementioned problems, we propose a recommendation model that combines active learning (AL) and a semi-supervised transductive support vector machine (TSVM). To enable neighbors to be found quickly and accurately, similar users are clustered together on the basis of their basic information. Then, the TSVM-based classifier is trained on each cluster. To improve the quality of sample labeling and thus the classifier performance, an active learning method based on the distance strategy and a multiclassifier voting mechanism is implemented. Finally, the TSVM-based recommendation model is trained on the labeled samples. The extensive experiments conducted using a real data set from MovieLens demonstrate that the proposed model effectively alleviates the aforementioned cold start and data sparsity problems.
An Oral English Evaluation Model Using Artificial Intelligence Method

Despite the fact that oral English classes have been available since primary school, most students’ oral English skills are poor and they struggle to communicate in simple everyday situations. This new requirement has been proposed in light of the general lack of English listening and speaking skills among Chinese college students. The current manual test for CET-4 and CET-6 will be gradually replaced by a computer test, and the CET-6 computer test will be included in the “quality engineering” project of national university teaching. The natural integration of artificial intelligence and education is assisting in the transformation of the traditional education ecology. Mind mapping has also had a significant impact on the education industry, and it has significant value in current educational applications. In order to truly realize personalized learning, we should pay more attention to the educational noumenon that the status of educators and learners has changed in education, that is, the transformation from educators to learners’ development. Artificial intelligence technology can be used effectively by junior high school English teachers to conduct oral English evaluation activities, which can more accurately judge students’ oral English expression ability and serve as a useful tool for adjusting and improving students’ learning methods.
Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data

Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.
Quantifying the entanglement of quantum channel

To describe the nonclassicality of quantum channels, it is necessary to quantify their properties. In the framework of resource theory of quantum channel, we present three new entanglement measures of channels for a quantum system of arbitrary finite dimension, based on the Choi relative entropy of channels, concurrence, and $k$-ME concurrence, respectively. Rigorous proofs show that these proposed measures fulfill all the requirements dictated by the resource theory of quantum channels, including non-negativity, weak monotonicity, strong monotone, convexity, additivity, and subadditivity. By using the measure, we describe the probability that the initial hypothesis is true in the hypothesis testing of channels. Compared with other known measures of the quantum channels, these measures have an obvious advantage in computability. According to the power of generating entanglement, we give a classification of quantum channels. Moreover, we provide several examples that characterize the properties of quantum channels and the conversions between quantum coherence states and entangled states and between fully separable states and multipartite entangled states.
Verification Control Algorithm of Data Integrity Verification in Remote Data sharing

Cloud storage's elastic expansibility not only provides flexible services for data owners to store their data remotely, but also reduces storage operation and management costs of their data sharing.The data outsourced remotely in the storage space of cloud service provider also brings data security concerns about data integrity.Data integrity verification has become an important technology for detecting the integrity of remote shared data.However, users without data access rights to verify the data integrity will cause unnecessary overhead to data owner and cloud service provider.Especially malicious users who constantly launch data integrity verification will greatly waste service resources.Since data owner is a consumer purchasing cloud services, he needs to bear both the cost of data storage and that of data verification.This paper proposes a verification control algorithm in data integrity verification for remotely outsourced data.It designs an attribute-based encryption verification control algorithm for multiple verifiers.Moreover, data owner and cloud service provider construct a common access structure together and generate a verification sentinel to verify the authority of verifiers according to the access structure.Finally, since cloud service provider cannot know the access structure and the sentry generation operation, it can only authenticate verifiers with satisfying access policy to verify the data integrity for the corresponding outsourced data.Theoretical analysis and experimental results show that the proposed algorithm achieves fine-grained access control to multiple verifiers for the data integrity verification.
Superresolution Reconstruction of Remote Sensing Image Based on Generative Adversarial Network

To recreate high-resolution, more detailed remote sensing images from existing low-resolution photos, this technique is known as remote sensing image superresolution reconstruction, and it has numerous uses. As an important research hotspot of neural networks, generative adversarial network (GAN) has made outstanding progress for image superresolution reconstruction. It solves the computational complexity and low reconstructed image quality of standard superresolution reconstruction algorithms. This research offers a superresolution reconstruction strategy with a self-attention generative adversarial network to improve the quality of reconstructed superresolution remote sensing images. The self-attention strategy as well as residual module is utilized to build a generator in this model that transforms low-resolution remote sensing images into superresolution ones. It aims to determine the discrepancy between a reconstructed picture and a true picture by using a deep convolutional network as a discriminator. For the purpose of enhancing the accuracy, content loss is used. This is done to obtain accurate detail reconstruction. According to the findings of the experiments, this approach is capable of regenerating higher-quality images.
Developing Online Czech Proofreader Tool: Achievements, Limitations and Pitfalls

This paper deals with achievements, limitations and pitfalls of developing the Online Czech Proofreader Tool (OCPT). The Tool has been developed in cooperation with the Department of Czech language of the Faculty of Arts of Masaryk University, the Institute of Theoretical and Computational Linguistics of the Faculty of Arts of Charles University, the Czech Language Institute of the Czech Academy of Sciences and Seznam.cz since 2019. The article describes the linguistic data used and tools and modules that constitute the OCPT and indicates the limitations of using an online web-based proofreader tool, especially in areas where mere application of formal rules for language error detection is not sufficient. The article also brings up the drawbacks of developing the OCPT which include occurrence of false-positives.
Qubit decoherence under two-axis coupling to low-frequency noises

Many solid-state qubit systems are afflicted by low frequency noise mechanisms that operate along two perpendicular axes of the Bloch sphere. Depending on the qubit's control fields, either noise can be longitudinal or transverse to the qubit's quantization axis, thus affecting its dynamics in distinct ways, generally contributing to decoherence that goes beyond pure dephasing. Here we present a theory that provides a unified platform to study dynamics of a qubit subjected to two perpendicular low-frequency noises (assumed to be Gaussian and uncorrelated) under dynamical decoupling pulse sequences. The theory is demonstrated by the commonly encountered case of power-law noise spectra, where approximate analytical results can be obtained.
Counteracting a Saturation Attack in Continuous-Variable Quantum Key Distribution Using an Adjustable Optical Filter Embedded in Homodyne Detector

A saturation attack can be employed for compromising the practical security of continuous-variable quantum key distribution (CVQKD). In this paper, we suggest a countermeasure approach to resisting this attack by embedding an adjustable optical filter (AOF) in the CVQKD system. Numerical simulations illustrate the effects of the AOF-enabled countermeasure on the performance in terms of the secret key rate and transmission distance. The legal participants can trace back the information that has been eavesdropped by an attacker from the imperfect receiver, which indicates that this approach can be used for defeating a saturation attack in practical quantum communications.
Recommending Reforming Trip to a Group of Users

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips.