Reviewerly/exHarmony huggingface dataset:
Analysis and Application of Access Control List (ACL) Methods on Computer Networks

Advances in information technology are currently growing to meet the needs of humans who want to obtain information easily, quickly and precisely. Therefore, we must continue to pursue and improve the quality and quantity of information technology. The purpose of this study is to find out how to perform the analysis and application of the Access Control List (ACL) method in a computer network. Mikrotik RB750 router is good to use for network security both in terms of reliability and in terms of cost. The method of network security using a firewall and address list is good because it can perform network security and restrictions on addresses that can be accessed and cannot be accessed. With this computer network security, computer network users can get internet access and local network access that is stable and smooth.
Analysis in the Field of Commercial Business

Entirely new business sectors are thought to be essential for implementing innovative ideas and anticipating the near-term business climate since they are early warning signs of possible business possibilities.This study paper aims to assess the emerging business potential in the commercial sector in the contemporary Internet era.In order to illustrate the importance of studying business opportunities, this paper systematically reviews the academic research in the business field in the past 12 years (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022), thus revealing the evolution process of innovation in the business field.This study emphasizes that the development of science and technology and the diversity of the business sector have also increased business opportunities in the Internet era.The reasons for the developments in the global climate in the Internet era are also explained in this essay.It also demonstrates how the quantitative outcomes and methodical approach are anticipated to be used in reality as a supporting tool and foundation for business opportunity analysis.
Design of Artificial Intelligence-based Multimedia Resource Search Service System for Preschool Education

Under the conditions of modern society, if preschool educational institutions cannot effectively integrate network resources into the network, their development will be severely restricted. Similarly, if parents cannot effectively guarantee the preschool education resources they want, the preschool education level of the family cannot fully obtain the convenience of information-based education. This paper aims to study the design of artificial intelligence (AI)-based Multimedia Resource (MR) search service system for preschool education. This paper analyzes the overview of AI, the methods of AI algorithms and the impact of AI on education and teaching, and finally analyzes the types of MR. The empirical analysis results show that the performance of the traditional preschool education MR search service system has been greatly improved after the adoption of AI technology, and we conducted a survey and found that more than 81% of people felt that designing a preschool education MR search service system applying artificial intelligence algorithms, it is found that the design efficiency has become higher.
Use Persona and Semantic Differential Method to Assist Undergraduate Students Learning Product Design

Currently in design education in Taiwan, students may have the basic operational skills of design analysis, as well as good design practice capabilities, but lack the ability to apply design analysis and design practice together. This often makes design results unsuitable for user needs or consumer markets. To improve this problem, this study establishes a teaching mode that integrates design analysis into the design practice curriculum. This study mainly uses the Persona and Semantic Difference methods as the learning goal of design analysis, and we teach students how to use the two methods to produce a design that meets the user's needs. The research results show that integrating the Persona and Semantic Difference methods into the product design process can effectively help students get closer to the design goals, and help maintain students on their design paths. Moreover, students’ self-evaluations have good learning effectiveness in the curriculums.
Formative assessment method of English language application ability based on consistency assessment

Due to the fact that learners' mastery of English language is not taken into account in the current methods, resulting in higher error and lower credibility of the assessment results, a formative assessment method of English language application ability based on consistency assessment is proposed. According to the principles of formative assessment, the implementation conditions of formative assessment include clear assessment objectives, diversified assessment means, determination of assessment criteria and timely feedback of information. Specific assessment tasks are defined and a theoretical model of autonomous learning is constructed to realise the assessment of English language application ability. Taking the students of two classes of automobile maintenance major in college as the test objects; the final assessment scheme is set. Experimental results show that the proposed method can effectively reduce the assessment error and improve the credibility of the assessment results.
Efficient Attribute-Based Proxy Re-encryption for Secure Deduplication

More and more data files are stored in cloud servers because of storage space. At the same time, many cryptographic techniques have been proposed to protect data privacy. However, a large amount of duplicate data is stored in the cloud server, resulting in a waste of resources. Most of the existing solutions rarely consider the problem of wasting storage resources. In this paper, we propose an attribute-based encryption (ABE) scheme that supports deduplication. What’s more, we implemented the verification of data files using blockchain. Finally, experimental simulations show that the proposed mechanism can improve the efficiency of processing duplicate data and reduce latency.
Communication Skills and Empathy of First-Year Students

The work is aimed at determining differences in the distribution of first-year students by levels of empathy in relation to communicative skills, since in the process of developing personal professional qualities in the process of preparing for educational and professional communication, communicative skills and empathy also become relevant. A brief theoretical review of research on communicative skills and emotional experiences as components of personal professional qualities is presented. The results of a mixed empirical study were obtained on a normal sample of defectology students (A&lt;1; E&lt;1). The study involved respondents (N=49) aged 17 to 23 years (M=19,20; SD=1,51). The methods of L. Mikhelson “Test of communicative skills”, A. Mehrabyan and N. Epstein “Diagnostics of the level of polycommunicative empathy” were used as a research tool. The research procedure was carried out through a voluntary online questionnaire. The results indicate the severity of very high and high levels of polycommunicative empathy and competence-based type of communicative behavior in the process of teaching students-defectologists. Intergroup differences in the distribution of respondents with competence-based, dependent and aggressive types of communicative behavior in the process of preparing for educational and professional communication were obtained. It is noted that the manifestation of the competence type of communicative behavior (71,4%) in educational and professional communication and very high (53,1%), high levels (42,9%) of polycommunicative empathy allow us to assert individual differences in the formation of personal professional qualities of students. The empirical data obtained are presented in the light of the communicative-activity approach and explain the statistical differences in the levels of empathy of students in relation to various types of communicative behavior.
Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment

In the academic area, teaching process assessment conducted by students can be used as the main information to improve the teaching and learning process. However, when examination or consideration of the student feedback is conducted by teachers, the outcome may be a biased analysis. In the last decade, sentiment analysis has been applied to automatically evaluate the teaching process because it may help to reduce the problem of biased analysis when the sentiment analysis is performed by humans. This work presents a method of automatically analyzing student feedback relevant to teaching process assessment. The proposed method is called sentence-level sentiment analysis, and it is driven by processing steps such as pre-processing student comments and text representation, identifying aspect class for each sentence using the aspect analyzer, assigning sentence polarity for each sentence using the sentiment analyzer, and summarizing the overall sentiment polarity by considering student comments, respectively. The proposed method returns the recall, precision, F1, and accuracy scores of 0.835, 0.825, 0.825, and 0.825, respectively. These were satisfactory results.
Enhanced True Random Number Generator (TRNG) using Sensors for IoT Security Applications

This paper proposes a secure True Random Number Generator (TRNG) based on sensors (CMOS image, sound and temperature). Arduino Uno Microcontroller is used to extract dark noise from CMOS image sensor and environmental noise is extracted by sound and temperature sensors. Methods used in the proposed design are Blum-Blum-Shub (BBS) and linear congruence equation to process the data collected by microcontroller from sensors and generates a highly secure TRNG using proposed algorithm. There are few advantages of TRNG over PRNG is that, unpredictability of the random numbers generated through TRNG is higher as compared to PRNG and its implementation is quite simple. Random numbers generated through TRNG can be used as highly secure cryptographic key generation for the data security in IoT devices during transmission and reception. NIST SP 800-22 statistical test suite validates the randomness of the generated random numbers.
Optimized complex object classification model: reconstructing the ISAR image of a hypersonic vehicle covered with a plasma sheath using a U-WGAN-GP framework

This study aims to apply generative adversarial networks (GANs) to the effective classification of high/low resolution (HR/LR) image pairs obtained via inverse synthetic aperture radar (ISAR) for hypersonic objects covered with a plasma sheath. We propose a classification training model based on a Wasserstein GAN with a gradient penalty (U-WGAN-GP) framework, wherein a U-Net with an excellent jump connection structure is used as a generator, and a VGG-Net with high robustness is used as a discriminator, to support the reliable classification of HR/LR ISAR image pairs for the enhancement of ISAR image resolution. The WGAN-GP provides a shortcut for the gradient propagation of network parameters during the training stage through the stable encoder and decoder structure of U-Net. It inherits the echo intensity variation and position distribution characteristics of the scattering points between hypersonic objects in LR images and effectively transplants them into the generated super-resolution ISAR images, and it establishes end-to-end mapping between the HR/LR ISAR images. Moreover, the VGG-Net ensures that the generated super-resolution images are stable, controllable, and undistorted. In addition, the WGAN-GP structure combines the content and adversarial losses, optimizes the generator loss function, and uses the GP method to provide a stable and continuous training process. Finally, a traditional VGG-16 network classifier is added at the end of the training model. The proposed model is applied to an HR/LR image pair dataset of hypersonic objects. Compared with existing methods, the proposed method improved the classification and recognition accuracy from 54.4% to 81.8%.
A Multi-task Learning Approach for Predicting Intentions Using Smart Home IoT Services

We propose a novel approach for predicting a resident’s future intentions in terms of what, how, when, and where he will do next. The intention model is learned from his previous interactions with various types of IoT services. In particular, we propose a multi-task learning approach for predicting resident’s future intention. The multi-task learning approach jointly learns the tasks of what, how, when, and where to boost the overall performance of the four tasks. We demonstrate the effectiveness and performance of our approach by conducting experiments on real-world datasets.
Sancus: an Anonymous and Trustworthy Blockchain-based Electronic Voting Architecture

The high cost of contemporary in-person voting, prolonged ballot counting, stringent voter availability requirements, and result unreliability have motivated the design and development of the new breed of electronic voting (e-voting) systems. The emerging e-voting systems, ranging from standalone electronic voting machines to automated Internet-based systems, aim at promoting better accessibility for disabled voters, faster ballot counting, and voting verifiability. However, the majority of the existing e-voting systems cannot satisfy all the aforementioned properties and are often vulnerable to breaching voters' anonymity. To address this problem, in this paper, we propose Sancus -a blockchain-based electronic voting architecture. Sancus meets the primary criteria of fair voting by providing features, such as voter authentication and anonymity, along with voting reliability and result verifiability. We will discuss Sancus's design and its proof-of-concept implementation in BigchainDB. We will further present its throughput measurements in a small testbed. In the future, we plan to augment Sancus's design and further implementing it on a more prominent blockchain and conduct a large-scale evaluation.
The effect of internal marketing on employee job satisfaction: evidence from a Jordanian tourism and hospitality setting

The main aim of this study is to achieve an in-depth understanding of the mechanism by which internal marketing affects job satisfaction. A theoretical framework was developed basing on related marketing literature. The study model was intended to determine the influence of internal marketing dimensions (IMD) in tourism and hospitality industry, particularly in the Dead Sea Region in Jordan. 378 valid questionnaires were retrieved from respondents representing different departments in targeted hotels and resorts. SPSS statistical package and its related module Amos were used to test the relationship between the independent variables of (IMD) and dependent variables related to employees' satisfaction (ES). The findings of the study have indicated the existence of significant positive correlations between three main dimensions of internal marketing (service culture, human resources management, and marketing information) on employees' job satisfaction. However, results have shown a less motivation effect on employee's satisfaction (ES).
HCL: Improving Graph Representation with Hierarchical Contrastive Learning

Contrastive learning has emerged as a powerful tool for graph representation learning. However, most contrastive learning methods learn features of graphs with fixed coarse-grained scale, which might underestimate either local or global information. To capture more hierarchical and richer representation, we propose a novel Hierarchical Contrastive Learning (HCL) framework that explicitly learns graph representation in a hierarchical manner. Specifically, HCL includes two key components: a novel adaptive Learning to Pool (L2Pool) method to construct more reasonable multi-scale graph topology for more comprehensive contrastive objective, a novel multi-channel pseudo-siamese network to further enable more expressive learning of mutual information within each scale. Comprehensive experimental results show HCL achieves competitive performance on 12 datasets involving node classification, node clustering and graph classification. In addition, the visualization of learned representation reveals that HCL successfully captures meaningful characteristics of graphs.
Does Wikidata Support Analogical Reasoning?

Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination. While the wide coverage of knowledge about entities and events make Wikidata a promising resource for analogical reasoning across situations and domains, Wikidata has not been employed for this task yet. In this paper, we investigate whether the knowledge in Wikidata supports analogical reasoning. Specifically, we study whether relational knowledge is modeled consistently in Wikidata, observing that relevant relational information is typically missing or modeled in an inconsistent way. Our further experiments show that Wikidata can be used to create data for analogy classification, but this requires much manual effort. To facilitate future work that can support analogies, we discuss key desiderata, and devise a set of metrics to guide an automatic method for extracting analogies from Wikidata.
Variability in image quality assessment metrics with different image capturing devices

Occupants’ subjective evaluations of the visual environment can be estimated using image quality assessment metrics. Images captured by a webcam, iPhone, and HDR camera resulted in only small differences in image quality metrics.
Finite-Time Convergent Modified Davidenko Method for Dynamic Nonlinear Equations

Neurodynamic methods, especially zeroing neural networks (ZNN), are widely used for solving nonlinear equations. Being simpler than the ZNN, the Davidenko method is efficient for static nonlinear equations. However, it has been shown that there are large lagging errors when the Davidenko method is applied to dynamic nonlinear equations (DNEs). In this brief, we address this issue by proposing a modified Davidenko method via normalization, making it suitable for solving DNEs with unknown derivatives. The proposed method converges in finite time for DNEs, regardless of additive noise. Both theoretical guarantees and numerical examples of the proposed method are provided.
Faster and Better Instance Segmentation for Large Scene Remote Sensing Imagery

Instance segmentation is difficult to apply into high-precision scenes with limited computational resources, such as unmanned aerial vehicles (UAV), compared to object detection. In this article, a faster and better instance segmentation network (FB-ISNet) based on CondInst is proposed in large scene remote sensing imagery. The FB-ISN et aims to improve efficiency without sacrificing accuracy. First, we adopt the deep layer aggregation as the backbone network to extract feature. Then, the BiFPN is used to overcome the limitation of one-way information flow in FPN and obtain effective multi-scale feature fusion. Next, the detection and instance segmentation heads are optimized to further reduce the amounts of parameters. Finally, the experimental comparisons on the SSDD, NWPU VHR-10 dataset and a large scene remote sensing imagery show that our FB-ISNet can strike a good balance between accuracy and speed, and the comparison with the existing algorithms also demonstrates the superiority of our approach.
Decomposition Analysis of Quantum Native Gates on Various Quantum Computers

The quantum compiler reconstructs the quantum circuit written at a high level into native gates operating in quantum hardware. In general, since one quantum gate is replaced by one or more native gates, the circuit will contain significantly more gates than the original quantum circuit. In this paper, we investigate the native gates according to each quantum hardware and analyze how many native gates are added compared to the original quantum circuit. We confirm that IBM, IonQ, Rigetti, and Google generated more native gates than original quantum gates by 5.3 times, 4 times, 5.3 times, and 4 times, respectively. Also, we contribute to further optimization studies for native gate decomposition by revealing the number of native gates with different error rates and latency.
Media Security of Childrenʼs Reading: Project Activity in the Library Media Environment

In the conditions of rapidly developing information society in Russia, the problem of ensuring children’s media security is becoming more urgent. Low-quality children’s literature (media products) is sold, distributed on the Internet, is in public and family libraries. Russian children currently are not protected from the influence of content that can have a re-socializing effect and promote destructive behaviour models. When choosing an interesting and useful book, children may be influenced by suggestions that are destructive to the personality of young reader. The authors substantiate the expediency of introducing the concept of “media security of childrenʼs reading” into the theory and practice of reading activity, both in relation to books and media products in general. The authors propose to solve the problem of the potentially possible re-socializing impact of some works of modern childrenʼs literature (media products) through the introduction of socio-cultural projects within the framework of information and recommendation support. At the same time, such support should include the creation of recommendation information and library products posted on the official websites of childrenʼs libraries and their online communities in the media environment, as well as the organization of their provision to users. The paper presents the results of library and pedagogical experiment, during which the socio-cultural project was developed, realized and implemented in the Pushkin Central Municipal Library of Kirov and tested in the parent community of younger schoolchildren, aimed at including the media security model proposed by the authors in the information and recommendation support for childrenʼs reading. The results obtained of project implementation indicate the effectiveness of the developed socio-cultural project, which is able to prevent the negative impact of low-quality literature.
Mengurangi Kesalahan Menulis Berbantuan Media Pembelajaran Interaktif Pada Anak Disgrafia Kelas II SD Negeri 060952 Medan

This research is motivated by the problem of dysgraphia children who often make mistakes in writing. This study aims to reduce writing errors assisted by interactive learning media in class II dysgraphia children at SD Negeri 060952 Medan. The type of research used is experimental research in the form of Single Subject Research (SSR) with an A-B-A design. The technical analysis of the data used is visual analysis of graphs, visual analysis of graphs. Research subjects are children who have difficulty learning to write (dysgraphia). The results of this study indicate that there is a reduction in writing errors in dysgraphia children. Children's writing errors decrease can be proven from the results of the analysis in conditions using a directional trend graph, where it can be seen that the trend towards the frequency of writing errors (omitting) of sentences with SPOK pattern decreases in the intervention phase (B), and decreases in the baseline phase (A2). The range of data obtained for baseline (A1) is 12–38, intervention (B) is 1-12 with a decrease in the level of change in the frequency of writing errors (omissions) of sentences with SPOK pattern in dysgraphia X children, then the stability of the data tendency is not stable. For the range of data obtained at baseline (A2), there are 1-4 frequencies with the level of change in reducing the frequency of writing errors (omitting) sentences with SPOK patterned children with dysgraphia X decreased. So, it can be concluded that interactive learning media is effective in reducing writing errors in class II dysgraphia children at SD Negeri 060952