Reviewerly/exHarmony huggingface dataset:
Promotion and Influence of Big Data and Artificial Intelligence in Field of Drama and Film

As a member of the oldest art family of human beings, drama is also a very important part of human civilization. With the development of technology, more and more types of drama film and television appear. In today’s society, bad films and commercial films are emerging one after another. The intervention of capital has brought a very poor experience to moviegoers and is not conducive to the development of the entire field of drama and film. The further development and scientific promotion of the field of drama film and television has become a relatively important issue. In this context, this paper takes this as the starting point, integrates big data and artificial intelligence algorithms, and aims to analyze the promotion and influence in the field of drama and film. It has existed for a long time in the fields of physics, biology, environmental ecology, and other industries such as military, finance, and communication, but it has attracted people’s attention due to the development of the Internet and information industry in recent years. The article introduces the transition of the mainland film system and the manifestations of conflict in plot art and film and television plots. It uses the convolutional neural network algorithm in deep learning to build the system. This paper adopts an online survey, distributes it to moviegoers nationwide, and collects 4,000 valid samples for analysis of moviegoers’ occupation and monthly income. In terms of the occupational distribution of the audience, the proportion of white-collar workers in the company is the highest at 40%, the student group only accounts for 17.4%, and the retired worker group accounts for the smallest proportion, at 0.6%. To sum up, the structure of Chinese films has shifted from a younger, student-dominated, middle-income group to a mature, workplace-oriented, middle- and high-income group. It has successfully completed the analysis of the promotion and impact of big data and artificial intelligence in the field of drama, film, and television.
Adaptive particle filter for state estimation with application to non‐linear system

Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selecting an appropriate important density functions, in this filter, the particles are first updated using the Spherical Simplex Unscented Kalman Filter algorithm, and then the particles are updated using the Adaptive Extended Kalman filter algorithm. Simultaneously, from the perspective of improving the resampling method, a new resampling technique based on the random resampling method has been designed and fused to this filter. The comparison and analysis of two simulation schemes have been conducted to assess the performance of the designed filtering algorithm. The simulation results show the effectiveness of the proposed approach.
Cross‐organisational data sharing framework based on blockchain‐probes

With the development of information technology, various social fields have gradually realised informatisation and digitisation, and today's society has entered the era of big data. A large amount of data exists in different departments and systems. At present, cross-organisational data sharing is often realised by point-to-point authorisation. However, there are still some threats in the management and control schedule for the whole region. To realise secure and credible data sharing, this paper proposes a cross-organisational data-sharing framework based on Blockchain-Probes using distributed digital identity technology. By using a star data link of the complex network, our system realises the synchronisation of heterogeneous data sources by using a data probe as the inter-central transmission carrier to link different data sources. The proposed framework is applied to the real scenario; compared with the traditional method, the efficiency and accuracy are improved, and the total cost is reduced by 60%. The framework could be employed in multiple complex system application scenarios such as transportation, finance and urban management.
TEACHER STRATEGIES IN DIRECTIVE SPEECH ACT ON THE GROWTH OF PANCASILA CHARACTER IN STUDENTS

This study aims to find the form and function of teacher speech strategies in the learning process in the classroom in Indonesian. This research uses a qualitative descriptive approach with a pragmatic approach. The research data were obtained through observation and in-depth interviews. Data analysis is carried out by using qualitative techniques by reducing data, presenting data, and drawing conclusions from existing data. The results showed that the use of directive speech acts was of the type of ordering, asking, advising, warning, and solicitation. The strategy used is to speak frankly without further ado, positive politeness, negative polite preamble, and vague strategy. The functions used are the competitive function of 'competing', the friendly function of 'fun', and the collaborative function of 'cooperating' in the teacher's speech act in the teaching and learning process
A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques

Agile software development is a way of frequent or continuous delivery of software.Nowadays many software industries have adopted agile for software development.The predictability and stability of traditional methods were replaced with flexibility, adaptability and agility to generate maximum value with collaboration and interaction, as quickly as possible.Effort estimation is the focused area in agile software development to achieve customer collaboration, respond to change and deliver a working software on time.Machine learning is an advanced tool to obtain effort estimation with available project data and widely used in IT industries to get accurate estimations.In this paper, the findings are reported through systematic literature review that aimed at identifying the applicability, limitations and individual result of most used machine learning techniques for effort estimation in agile software development with the help of 3 research questions.Also, suggested attributes of a robust machine learning model are discussed to achieve more accurate effort estimation.Conclusion of paper can help researchers and IT consultants in building a ML model considering the applicability, results and limitations of ML techniques.
Efficient Encrypted Range Query on Cloud Platforms

In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.
Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach

Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature.
Quantum walks in two-dimension arrays of waveguides

Quantum walks (QWs) have emerged from fundamental research to be one of key processes in quantum information technology including quantum computing and quantum communication. In this paper, we present results of our numerical investigation of quantum walks (QWs) in different types of 2D arrays of waveguides. We show (i) the localized QWs in quasiperiodic arrays based on Fibonacci sequences are highly controllable due to the deterministically disordered nature of quasiperiodic photonic lattices, (ii) 2D arrays of waveguides can also be used as platforms to realize important effects for special applications such as exponential speedup in quantum search and quantum topology photonics and finally, (iii) results of multiple photons QWs in 2D programable directional couplers can be useful for designing quantum photonics gates.
Square-free smooth polynomials in residue classes and generators of irreducible polynomials

Building upon the work of A. Booker and C. Pomerance [Proc. Amer. Math. Soc. 145 (2017), pp. 5035–5042], we prove that for a prime power <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="q greater-than-or-equal-to 7"> <mml:semantics> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>≥<!-- ≥ --></mml:mo> <mml:mn>7</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">q \geq 7</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, every residue class modulo an irreducible polynomial <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper F element-of double-struck upper F Subscript q Baseline left-bracket upper X right-bracket"> <mml:semantics> <mml:mrow> <mml:mi>F</mml:mi> <mml:mo>∈<!-- ∈ --></mml:mo> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="double-struck">F</mml:mi> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> <mml:mo stretchy="false">[</mml:mo> <mml:mi>X</mml:mi> <mml:mo stretchy="false">]</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">F \in \mathbb {F}_q[X]</mml:annotation> </mml:semantics> </mml:math> </inline-formula> has a non-constant, square-free representative which has no irreducible factors of degree exceeding <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="degree upper F minus 1"> <mml:semantics> <mml:mrow> <mml:mi>deg</mml:mi> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>F</mml:mi> <mml:mo>−<!-- − --></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">\deg F -1</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. We also give applications to generating sequences of irreducible polynomials.
Assessing the RPKI Validator Ecosystem

In this work we compare seven currently available Resource Public Key Infrastructure (RPKI) validators regarding their ease of installation, performance, consistency in results, code quality, applicability, and feature-richness. We weigh the different characteristics and rank each validator based on the scores it received during our tests. We find Routinator to perform the best and recommend using this RPKI validator in a production environment. Additionally, we uncover inconsistencies in the validation results between different validators and investigate possible causes. Moreover, we also look at the RPKI repository infrastructure and discuss recent outages.
Enhanced 3D Action Recognition Based on Deep Neural Network

In the video surveillance system operating in the real environment, the recognition of detailed behavior of objects has important meaning in terms of understanding whether security events have occurred. In particular, the perception of behavior in a poor environment such as low light and overlapped objects is recognized as an important technical factor that must be overcome in the existing 2D image-based surveillance system. Action recognition of objects using 3D depth map provides a way to solve these issues. In this paper, we propose the enhanced 3D action recognition method based on convolution neural network (CNN) for the video surveillance system. And we evaluated the action recognition performance using the real environment DB, and the recognition result for 6 detailed behaviors was confirmed to be an average of 68.56%.
Visual Display Method of Enterprise Internal Control Information Based on Cluster Analysis Algorithm

In the face of today’s fast updating and wide variety of word segmentation, it is difficult to accurately identify the keyword segmentation in the enterprise’s internal control information. Therefore, a visual display method of enterprise internal control information based on cluster analysis algorithm is proposed. Fully considering the characteristics of enterprise internal control, on the basis of clarifying the visual display process of enterprise internal control information, the visual display coefficient of enterprise internal control information is optimized based on cluster analysis algorithm. By optimizing the visual display value, the visual display parameter fusion of enterprise internal control information is realized. The dimension of enterprise internal control information is reduced, and the mean value of color difference between pixels is used to ensure the separability of visual display images, so as to realize the visual display of enterprise internal control information. The experimental results show that the proposed method can accurately identify the enterprise internal control information, which shows that the practical application ability of this method has been improved.
Learning Based Clinical Diagnosis Framework for Inconsistently and Partially Labeled Dataset

This paper presents a novel deep learning framework for autonomous clinical diagnosis by dealing with the training with poorly labeled clinical dataset. Partially labeled data and inconsistent labels from multiple annotators make the model hard to learn accurate diagnosis in frequently and drastically updated clinical dataset. Motivated by such difficulties, the proposed framework introduces the weighted combination of inconsistent labels by considering multiple annotators' expertise and adapt meta-learning approach for the quick adaptation to the updated dataset. Experimental results on the posterior pelvic tilt detection in a squat motion show the proposed approach outperforms the conventional learning approaches in terms of the convergence speed and the converged mean squared error.
Evaluating Correctness of Reinforcement Learning based on Actor-Critic Algorithm

Deep learning is used for decision making and functional control in various fields, such as autonomous systems. However, rather than being developed by logical design, deep learning models are trained by itself through learning data. Moreover, only reward values are used to evaluate its performance, which does not provide enough information that the model learned properly. This paper proposes a new method to assess the correctness of reinforcement learning, considering other properties of the learning algorithm. The proposed method is applied for the evaluation of Actor-Critic Algorithms, and correctness-related insights of the algorithm are confirmed through experiments.
Object Tracking of Aerial Imaging Device Image Using Variational Autoencoder and External Memory

Object tracking is a fundamental problem in the field of computer vision. The object tracking methods proposed so far can be divided into a ‘discriminative correlation filter’ and a ‘deep learning’ based methods with a complex structure and a lot of computation. In this paper, we propose an algorithm for tracking objects with a simple structure while maintaining tracking performance using a convolutional variational auto-encoder, external memory, and a Siamese network. As a result of an experiment with an RT (real-time) data set to measure real-time, the result was a precision of 0.546 and a success rate of 0.527.
Evaluating the level of primary school students in the light of reading skills

The aim of the current research is to (evaluating the level of primary school students in the light of reading skills), and to achieve this, the researcher relied on the descriptive approach, and a research tool was built, which is the questionnaire, which consisted of (22) paragraphs distributed on six main skills: recognition, pronunciation, understanding, taste, criticism. , the evaluation and put three alternatives for each paragraph (available, available to some extent, not available), and the researcher made sure of the validity and reliability of the tool. , Hashemite District, amounted to (382) male and female students for the academic year (2021-2022 AD) and after applying the tool and unloading the data, analyzing it and treating it statistically with the Statistical Package for Social Sciences (SPSS).The researcher reached a number of results, the most important of which are: The level of availability of reading skills for primary school students (fifth grade of primary school) was very poor
V - Channel magnification enabled by hybrid optimization algorithm: Enhancement of video super resolution

Although being a really active area of research, television super-resolution remains a difficult problem. Additionally, it is noted that the blur motion and computational crisis hinder the enhancement. As a result, the goal of this research is to present a brand-new smart SR framework for the camera shot. To create High Resolution (HR) videos, first frames in RGB format are converted to HSV and then the V-channel is enhanced. In order to create enriched video frames, a high - dimension grid with enhanced pixel intensity is then created. This paper introduces a particular progression to enable this: Motion estimation, Cubic Spline Interpolation, and Deblurring or Sharpening are the three methods. By carefully adjusting the parameters, the cubic spline interpolation is improved during operation. A brand-new hybrid technique dubbed Lion with Particle Swarm Velocity Update (LPSO-VU), which combines the principles of the Lion Algorithm (LA) and Particle Swarm Optimization (PSO) algorithms, is presented for this optimal tuning purpose. Finally, using the BRISQUE, SDME, and ESSIM metrics, the adequacy of the method is contrasted to other traditional models, and its superiority is demonstrated. Accordingly, the analysis shows that the suggested LPSO-VU model for video frame 1 is 16.6%, 25.56%, 26.2%, 26.2%, and 27.2% superior to the previous systems like PSO, GWO, WOA, ROA, MF-ROA, and LA, respectively, in terms of BRISQUE.
Formal analysis of the application programming interface of the PVS verification system

A formal specification of the Application Programming Interface provided by the PVS verification system is developed. The specification documents the modes of operation and the communication patterns necessary to interact with PVS to trigger parsing, type-checking, and interactive theorem proving. The primary goal is to explore the use of formal methods as part of a toolchain for rigorous documentation of the functionalities of PVS. The developed specification can be used to track API design changes and to inform possible enhancements that can be introduced in future versions of PVS. The same specification can also be used as a reference by developers of client applications that use PVS as a back-end, to verify the correctness of communication patterns used by the client to interact with PVS. It is a first step towards a possible tool qualification strategy for PVS.
Virtual Reality in Tourism : A Research Note

Technology inclusions have been deriving disruptive changes in the tourism industry. The most sought-after digital tool – virtual reality (VR) – has been highly preferred by destination marketers in formulating their marketing strategies and executions and branding their destinations. Though VR inclusion started early, the pandemic crisis has added effectiveness to the vibrance of the industry’s offerings. However, the research of VR inclusions in tourism marketing needs wider evidence that would eventually facilitate the thereof in identifying and understanding of the emerging tourists’ micro-segments based on their motives, duality of destination management and branding, and the relevance for conceiving memorable experiences. This research note indicated three such critical aspects – VR in upscaling tourism, VR as a marketing strategy for sustainability, and VR as reality or reality for tourists. The note also indicated the major areas that can be researched from the given perspectives of VR for tourism marketing and destination branding.
Optical-guided residual learning network for synthetic aperture radar image super-resolution

Synthetic aperture radar (SAR) image super-resolution (SR) has broad application prospects in military reconnaissance, marine monitoring, crop monitoring, and other fields. However, when the scale factor is large, current SAR image SR has limitations, such as insufficient feature extraction, difficulty of feature representation, and modeling difficulty of SR reconstruction. These problems have severely restricted the SR performance of SAR images. In addition, optical remote sensing images share higher resolution and richer high-frequency information than SAR remote sensing images. Therefore, we adopt high-resolution (HR) optical images as auxiliary images of SAR image SR to improve the performance of SAR image SR. Our research is conducted from two aspects: the extraction efficiency of HR optical image-guided information and the representation ability of low-resolution (LR) SAR image features. We propose an optical guidance residual networks (OGRNs) based on residual learning for SAR image SR. The OGRN uses optical images during network training, which enhances the performance of SAR image SR, even when optical images are missing during network testing. First, we extract the texture feature of SAR images through the designed residual learning module. Second, we extract the weight map of feature information for SAR image SR from HR optical images by the convolutional layer. Finally, the weight map is used to guide the weighted supervised SR reconstruction for SAR images. Extensive experimental results demonstrated that, under the guidance of optical images, OGRN achieves excellent performance in terms of both quantitative assessment metrics and visual quality.