Reviewerly/exHarmony huggingface dataset:
Exploring the instructions and evaluation methods for improving creative thinking competency

The purposes of this study were confirmed the student's creative thinking competency profile and the instructions and evaluation methods that affect the profile. LPA was performed using divergent thinking, creative flexibility, curiosity and adventure, and inquiry as index variables, and the degree of individualization, interaction, and students' classroom engagement was used as the instruction method, and process fortified assessment and evaluation feedback were used as evaluation methods. For the analysis, the 6th year of the Korean Education Longitudinal Study 2013 were used. As a result of the study, first, creative thinking competency was classified into three latent profiles. Second, individualization was an influential variable that differentiated the ‘higher competency’ profile. Third, interaction did not affect creative thinking competency profile differentiation. Fourth, students' classroom engagement was the most important variable affecting the differentiation of all profiles. Fifth, process fortified assessment differentiated the ‘general competency’ and ‘higher competency’ profiles. Sixth, evaluation feedback affected the differentiation of all profiles. In order to improve creative thinking competency, it is necessary to create an environment that can foster learners' activeness and initiative attitude for student engagement classes. In addition, it should support process fortified assessment and evaluation feedback that can check the process as well as the result of learning.
A novel pipelining encryption hardware system with high throughput and high integration for 5G

This paper presents a ZUC-256 stream cipher algorithm hardware system in order to prevent the advanced security threats for 5G wireless network. The main innovation of the hardware system is that a six-stage pipeline scheme comprised of initialization and work stage is employed to enhance the solving speed of the critical logical paths. Moreover, the pipeline scheme adopts a novel optimized hardware structure to fast complete the Mod(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">31</sup> −1) calculation. The function of the hardware system has been validated experimentally in detail. The hardware system shows great superiorities. Compared with the same type system in recent literatures, the logic delay reduces by 47% with an additional hardware resources of only 4 multiplexers, the throughput rate reaches 5.26 Gbps and yields at least 45% better performance, the throughput rate per unit area increases 14.8%. The hardware system provides a faster and safer encryption module for the 5G wireless network.
25‐4: Contact Lens Embedded MicroLED displays

Existing AR/VR headsets tether us to a display that creates a physical barrier to important human interactions. Mojo Vision's Contact Lens embedded AR/VR solution will fundamentally alter the way people receive and share information enabling information to be available during activities that require full use of the human body, a concept Mojo Vision calls “Invisible Computing.” MicroLEDs play a critical role in Invisible Computing because of their ability to create the pixel densities, brightness and foveal coverage required to augment images at the full visual acuity of the human visual system. Contact lens displays and their use on‐eye will be presented.
PUMTD: Privacy-preserving user-profile matching protocol in social networks

User profile matching can establish social relationships between different users in the social network. If the user profile is matched in plaintext, the user's privacy might face a security challenge. Although there exist some schemes realizing privacy-preserving user profile matching, the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data. To overcome the problems, a novel privacy-preserving user profile matching protocol in social networks is proposed by using t-out-of n servers and the bloom filter technique, in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem, the matching users can be found with the help of any t matching servers, and the privacy of the user profile is not compromised. Furthermore, if at most t − 1 servers are allowed to collude, our scheme can still fulfill user profile privacy and user query privacy. Finally, the performance of the proposed scheme is compared with the other two schemes, and the results show that our scheme is superior to them.
Fisher-HHT: A Feature Extraction Approach For Hand Gesture Recognition With a Leap Motion Controller

In the last decades, Hand Gesture Recognition (HGR) has become one of the most prominent research topics, due to its wide range of applications in computer vision. Various approaches and techniques were suggested and evaluated to conduct significant results on HGR. However, even with the triumph reached on state-of-the-art methods, they have not considered the non-linearity and non-stationarity existing on time-series data including the Leap Motion Controller (LMC) raw data. In this paper, we propose a novel method for extracting and selecting pertinent and discriminant features using Hilbert Huang Transform (HHT) and fisher discriminant analysis. Therefore, the time-series signals of LMC are decomposed through an empirical mode decomposition. Then, the HHT is applied to generate the resultant Hilbert marginal spectrum. Next, the fisher discriminant analysis is proceeded for an efficient feature selection. Experimental results demonstrate the effectiveness of the proposed approach in term of hand gesture classification accuracy on the challenging LeapGestureDB dataset.
PolygloNet: Multilingual Approach for Scene Text Recognition Without Language Constraints

The detection and recognition of text instances in camera-captured images or videos generate rich and precise semantic information for interpreting and describing the scene. However, recognizing text in the wild remains a challenging problem despite its value. Apart from the inherent problems in text detection and recognition tasks, knowing the language to be recognized is required in unsupervised forensic applications where multilingual information is frequently employed. This work proposes an unconstrained multilingual text recognition pipeline for scene text detection and recognition. The pipeline consists of multiple text recognition experts contributing to determining the output text sentence. Each expert translates the visual information into a candidate text sentence. Finally, our post-processing model, named PolygloNet, encodes and aggregates all text sentences to generate the optimal text sequence. This model can select the most likely text sequence and correct spelling errors produced in the recognition stage. Experimental results show that our model can produce accurate recognition results on relevant datasets (MLT2017 [2] and MLT2019 [1]).
Sequence-Level Speaker Change Detection With Difference-Based Continuous Integrate-and-Fire

Speaker change detection is an important task in multi-party interactions such as meetings and conversations. In this paper, we address the speaker change detection task from the perspective of sequence transduction. Specifically, we propose a novel encoder-decoder framework that directly converts the input feature sequence to the speaker identity sequence. The difference-based continuous integrate-and-fire mechanism is designed to support this framework. It detects speaker changes by integrating the speaker difference between the encoder outputs frame-by-frame and transfers encoder outputs to segment-level speaker embeddings according to the detected speaker changes. The whole framework is supervised by the speaker identity sequence, a weaker label than the precise speaker change points. The experiments on the AMI and DIHARD-I corpora show that our sequence-level method consistently outperforms a strong frame-level baseline that uses the precise speaker change labels.
Research on the actuality of GPS technology in deformation monitoring technology

GPS technology has the characteristics of high precision, high sampling, real-time, and simultaneous determination of three-dimensional coordinates of points, which can not be compared with other monitoring technologies. It plays a very important role in deformation monitoring. Starting from the composition of GPS positioning system, this paper expounds the three components of GPS positioning system, as well as the association and coordination between each component of the work; then the GPS deformation monitoring mode and several error sources in the monitoring process are introduced. The advantages and disadvantages of GPS technique in deformation monitoring are analyzed and its application trend is predicted. GPS positioning technology is applied in all aspects of our life, creating a lot of social and economic value for us.
Progressive Compression and Encryption of Hyperspectral Images Based on Walsh Transform and Fingerprint Recognition Discrete Multiple-parameter Fractional Fourier Transform

A hyperspectral images(HI) compression and encryption scheme based on Walsh transform and fingerprint identification discrete multiple-parameter fractional Fourier transform (FRdmpFrFT). In the Walsh Transform Compression Module (WTCM), all single-band images are compressed using the Walsh Transform defined by the Hadamard permutation to divide them into several small blocks. The compressed image further expands the key space through discrete multi-parameter fractional Fourier transform and fingerprint identification method. This encryption method realizes the supplementary encryption of hyperspectral images. Compared with the existing hyperspectral compression encryption method, this method not only further improves the security of encryption, but also reduces the calculation and storage requirements of encrypted data. Only when all the keys are used correctly can the original hyperspectral image information be successfully decrypted. Numerical simulation verifies the performance of this method.
The Use of Literature in EFL Classroom: Italian Canadian Short Stories as a Teaching Innovation Experience Through Flipped Classroom Strategy

The aim of this article consists of showing that motivation is necessary for the success of a teaching and learning experience in an online classroom. By using the flipped learning method, the student becomes the central part of learning, being more active than in the traditional class and taking responsibility for his/her learning. He/she goes from being a mere spectator who only listens to the teacher's speech to someone with a voice in the classroom structured to allow student to acquire knowledge and build different competencies. The experience described below by implementing the flipped learning in the subject Contemporary Culture and Society of the Anglophone Communities, of the online Bachelor's Degree in Spanish Language and Literature, at the University of Burgos, excitement and self-expression replaced fear and dread. This awareness helped students in the construction of critical thinking and, in this specific case, culture and literature represented a better way for students to understand and know the society in which the language used in the texts is the language spoken (Valdes, 1986). During the first phase of this experience, the 56 students were divided into 14 groups of four members in each one; they were given instructions by different videos recorded by the teacher and during which she explained what to do, how to develop the project and how to proceed and deliver the final task. She also introduced the general topic of the Unit “Acculturation, adaptation and sense of belonging in women emigrants through Italian Canadian short stories” by uploading different readings by different women writers who had migrated from Italy to Canada for different reasons and in different periods since XX century until today. Mostly based on real events, these writings unveil the different coping strategies deployed by Italian Canadian women and how they dealt with the complex process of settling into life in a new country. After having chosen the writer to work on the 14 groups proceeded to research and investigate them. They also kept regular contact with the teacher by fora or tutorials so the teacher could track and monitor their learning progress and needs. The project consisted of preparing a presentation on the woman writer selected including the context she lived in and her work. During the last phase, after the task delivery, she asked all groups to share the posters and presentations and to ask questions and comments about them in a forum opened for that purpose. Finally, a questionnaire on their perception of the flipped learning approach in the course was prepared and distributed to the students. The result of this experience has shown that the flipped learning strategy succeeded in motivating the students to build and acquire knowledge and to reach beneficial outcomes.
Interpretable Analysis and Pruning of Modulation Recognition Network Based on Deep Learning

Concerning poor interpretability and complexity of deep model in modulation recognition (MR) based on deep learning, an interpretable analysis and pruning framework of MR network based on Gradient-weighted Class Activation Mapping (Grad-CAM) is accordingly proposed in this paper. The framework first extracts the amplitude, phase and spectrum from the original modulated signal, and it uses the Smoothed Pseudo Wigner-Ville Distribution (SPWVD) to obtain the two-dimensional time-frequency spectrum of the modulated signal. Then, the key features in the deep model are visualized from the perspective of one-dimensional features and two-dimensional features at the input respectively. The framework visually displays and compares the differences and commonalities of the depth features of hidden layer with different models, extract the values of different filters of each layer in the deep neural network (DNN), and prune the network according to the values. The experiment results show that the interpretable and pruning framework of MR network based on Grad-CAM in this paper can achieve effective explanation and analysis on the MR network, and can greatly reduce the redundancy of the network. The running speed of the pruned network is 3.83 times higher than that of the original network. The size of the pruned network is 72% lower than that of the original network. Besides, the accuracy of the pruned network is 0.3% higher than that of the original network.
An Analysis of the Scaffolding of Interactions in Online and Face-to-Face University Classes

The purpose of this study is to analyze interactions between learners and the instructor (ILI) and interactions among the learners (IAL) themselves in a single university reading class with a focus on scaffolding. Thirty-three learners majoring in computer engineering and public administration participated in the class. Twenty-six out of 33 learners took a non-face-to-face online class (NFTFOC) for a 15-week semester, and the remaining seven learners chose to take a face-to-face class (FTFC) for the five weeks after the mid-term exam. Data from both the NFTFOC and FTFC were collected for 15 weeks from September to December 2021 including an automatic video recording system, quizzes, assignments, etc. through chatting boxes, bulletin boards, and emails. The obtained data were analyzed into both qualitative descriptions and quantitative statistical measures. The results showed that interactions in the low and mid-level of higher-order thinking from both the linguistic and cognitive perspectives occurred similarly, and both amplifying and simplifying of scaffolding functions in NFTFOC were more frequently observed than those in FTFC. In addition, both NFTFOC and FTFC got better scores on the post-test. As development occurs in language learning, a longitudinal and in-depth analysis of the language learning processes is required in future studies.
A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.
Effective and Fast Estimation for Multi-Source Navigation Sensor Reliability

As a new navigation technology, the idea of all source navigation is using any available sensor to achieve high-precision location service. However, navigation devices and data are susceptible to various environments and attacks, which emphasizes the need for pervasive security measures like data reliability evaluation on the fly. There exist several sensor data reliability measures which are based on the Internet of things devices, but these methods are weak for highly dynamic navigation data. This paper proposes a real-time navigation sensor data reliability evaluation method, which combined self-evaluation and mutual evaluation. Given the strong dynamics of navigation sensors, we improve the classical sensor anomaly judgment criteria and realize the data reliability judgment in high dynamic scenarios. Then, the spatial-temporal correlation and probability distribution of multi-sensor data are aggregated, and the aggregation results are combined with the self-evaluation reliability to realize the real-time evaluation of sensor data reliability. The performance of our method is evaluated using both trustworthy and untrustworthy data. The data of the former is collected by unmanned vehicles in different scenarios. The latter consists of two parts: the data simulated according to the anomaly model and the data collected when the actual sensor is blocked. Experiments demonstrate proposed method can effectively evaluate the reliability of sensors data.
Redefining the human factors approach to 3D gestural HCI by exploring the usability-accuracy tradeoff in gestural computer systems

3D gestural technology for HCI could transform the way people interact with computing systems. There are traditionally two approaches to developing gestural technology systems: a human-based approach where usability is maximized and a technology-based approach where system accuracy is maximized. The tradeoff between usability and accuracy may negatively affect the overall trust and reliability in the system. Therefore, this study seeks to redefine the human-based approach to gestural system development by introducing a bottom-up approach to identifying the lower-level features that produce a gesture, thus allowing the technology to accurately recognize features. A user elicitation study was performed, and gestures were classified according to a novel feature extraction gesture taxonomy and a traditional taxonomy of classifying gestures as a unit. The feature-extraction approach revealed several advantages because it fosters a bottom-up approach to identifying gesture features. Using this approach may mitigate the effects of the usability-accuracy tradeoff in gestural system development.
Self-attention Mechanism Fusion for Recommendation in Heterogeneous Information Network

In recent years, lots of recommendation methods based on heterogeneous information network has been proposed. However, traditional heterogeneous information network mainly relies on the similarity of meta-path to recommend, which cannot fully mine the user’s potential preference features. We propose a heterogeneous information network model using self-attention mechanism for recommendation (HINS). In our method, local neighbor information is modeled for users and items respectively via collaborative attention mechanism. Moreover, by optimizing the relationship representation between learning nodes of multi label classification problem based on meta-path, self-attention mechanism is added to the relationship representation to make the model personalized. Finally, the two parts are unified into one model for top-N recommendation. We do evaluation experiments on four different datasets, and our model achieves the best results.
Industrial Rod Size Diameter and Size Detection

Thermo-mechanical treatment (TMT) rods are the walloping production of the steel industries, and there are giant machines that will make the task of cutting TMT rods easier for industries. While cutting the rods, photoelectric sensor, manual labor, and complex computing machine are used that need huge maintenance of the machine, and it is a time-consuming process. In the last decade, research on digital image processing and computer vision has seen much progress. In this paper, we propose an adaptable methodology for the industries in measuring the TMT rods much more efficiently, maximizing the efficiency, robust toward cram-full of rods and minimizing the error rate. The captured digital image first undergoes the preprocessing phase, where the first step is image enhancement and then edge detection, which extracts the TMT rods edges then followed by the diameter calculations (pixels per metric). An experiment has been conducted with various challenging conditions to demonstrate the capability of our approach to a good measure of success.
Mobile English Learning Platform Based on Collaborative Filtering Algorithm

With a large number of digital resources as the carrier, mobile learning breaks through the shortage of resources and the limitation of time and space under the current learning mode. However, its rich resources also bring information overload, which greatly affects the learning efficiency. The mobile English learning platform based on collaborative filtering algorithm not only makes full use of the advantages of mobile learning, but also recommends learning resources according to the learning needs of different learners to meet the learning needs of different learners, saving learners’ time and effort to a certain extent all. It has certain practical significance.
Analisis Dampak COVID-19 dan Faktor-Faktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series

Stock is one of the instruments that can be used to invest. The factors suspected of influencing the fluctuations in the stock price index are the Jakarta Composite Index (JCI), the rupiah exchange rate, and the Covid-19. The purpose of this study is to analyze the effect of the JCI, the rupiah exchange rate, and the Covid-19 on the Jatim Bank stock price index. To analyze the effect of the JCI, the rupiah exchange rate, and the Covid-19 on the Jatim Bank stock price index, time series regression analysis was used. The results of the analysis show that the JCI, the rupiah exchange rate, and the Covid-19 have a significant effect on the Jatim Bank stock price index. The p-value of the three variables is less than 0.05. The value of the determination coefficient is 65.56%, so that the diversity of the dependent variable that can be explained by the model is 65.56%.