Reviewerly/exHarmony huggingface dataset:
Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance

Abstract The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.
Schoolchildren’s Perceptions of STEM Professions and Careers in Russia: Results of a Pilot Study

Introduction. The world of work is undergoing a global shift fuelled by technological innovation, demographic changes and environmental problems. This heightens the demand for STEM (science, technology, engineering and math) competencies which are seen as a catalyst for social and economic development. The purpose of this study was to investigate learner views on STEM professions, the learners’ overall attitudes regarding STEM, as well as their aspirations and willingness to choose a career in STEM and identify factors which are crucial in forming young people’s interests in studying STEM and pursuing STEM careers. Materials and Methods. This pilot study was conducted in May 2019 across three schools in the Moscow region. The authors used a slightly modified version of the learner questionnaire used by the European study “ECB-inGenious” to investigate perceptions of STEM careers among Russian learners in grades 8–9 and 10–11. Results. Overall, it was determined that a combination of intrinsic (cognitive and attitudinal) and extrinsic (social, cultural and economic) factors were responsible for learner engagement in STEM. Learners showed a strong awareness of STEM’s significance to society and displayed positive perceptions of STEM careers, although jobs in industry appeared less popular than those in other areas of STEM. Some factors, such as a learner’s personal experiences of STEM in and out of the classroom can even play a decisive role in shaping aspirations towards STEM careers. Younger learners expressed more enthusiasm for STEM careers than their older counterparts. Learners enrolled in classes specialising in STEM and learners whose parent(s) worked in STEM-related professions showed stronger positive attitudes to STEM careers. Discussion and Conclusion. To make an informed decision, learners require practical information and advice regarding STEM careers; schools can play an important role in this process. This guidance must begin early, ideally from primary school when learners are more enthused and interested in learning about STEM careers. It is also advisable for learners to be given more opportunities to join extracurricular STEM activities; consequently, a learner’s understanding of STEM subjects is expanded outside the academic curriculum which can spark a longlasting interest in the subject. Additionally, STEM subjects’ curriculums require enriching with real-life examples and should be contextualised in terms of relevant careers. Finally, schools should be encouraged to organise regular engagements with STEM professionals.
ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection

INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations. METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET. RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively. CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.
Antisemitic comments on Facebook pages of leading British, French, and German media outlets

We conducted a cross-national study on antisemitic hate speech on the Facebook profiles of leading media outlets in the United Kingdom, France, and Germany. In a combination of qualitative pragmalinguistic analysis and quantitative analysis, we examined their comment sections concerning the conceptual and linguistic repertoire of verbal antisemitism in these three languages as well as to the frequency of antisemitic utterances. The corpus comprises 4500 comments (1500 for each language) made in reaction to the media's Facebook posts reporting on an escalation phase of the Arab-Israeli conflict in May 2021. Since in antisemitism studies, Israel-and issues related to it-are widely perceived as today's main pretext for communicating antisemitic resentment, unsurprisingly, those events led to the emergence of antisemitic content online. This article contrasts the findings of antisemitism in the three countries' comment sections and illustrates them by presenting a variety of linguistic realisations of various antisemitic concepts and illustrates the corresponding steps of interpretation.
EMPLOYEE MOTIVATION, JOB SATISFACTION, CUSTOMER SATISFACTION, AND FIRM PERFORMANCE: THE MODERATING ROLE OF EMPLOYEE EMPOWERMENT

This study mainly focuses on examining the effects of employee motivation on Job Satisfaction, job satisfaction on customer satisfaction, customer satisfaction on firm performance, and the moderating role of employee empowerment on the relationship between employee motivation and job satisfaction. The current study follows convenience sampling to collect the data. The questionnaire uses the survey method of self-administered. To analyse the relationship between the variable and test the hypothesis PLS3 (Partial least-squares) equation is used. The main contributors to the current study were staff of the hotels. The sample size of the study is 380 worker staff of a hotel. The findings of the study indicated that employee motivation effect significantly and positively on job satisfaction, job satisfaction effect significantly and positively on customer satisfaction, customer satisfaction effect significantly and positively on firm performance, and for the moderation the finding shows that Employee empowerment effect positively and significantly on the relationship between Employee motivation and Job Satisfaction.
Efektivitas E-LKPD Berbasis Liveworksheet dalam Meningkatkan Hasil Belajar Sosiologi Peserta Didik di Kelas XI IPS SMA N 5 Padang

This research is motivated by the low learning outcomes of students of class XI Social Sciences at SMA N 5 Padang. This study aimed to measure the effectiveness of the Liveworksheet-based LKPD in improving student learning outcomes in class XI IPS SMAN 5 Padang. This experimental study uses a quantitative approach with a one-group pretest-posttest research design. The population in this study amounted to 36 students of class XI IPS SMA N 5 Padang. The sampling technique used was total sampling with a total sample of 36 class XI IPS 1 SMA N 5 Padang students. The data collection technique uses pretest and posttest questions with a question indicator that presents a phenomenon so that students can identify the meaning of social integration and actual examples of social integration in the surrounding environment and the driving factors of social integration. The average pretest score was 52, and the average posttest score was 86. This study was analyzed using David Ausubel's meaningful learning theory. The data analysis techniques used were normality, homogeneity, and paired t-tests. Normality test and homogeneity test function as test requirements to find out whether the t-test data analysis can be continued or not. In contrast, the paired t-test is used to determine the difference between samples after and before using the live worksheet-based e-LKPD. To measure the effectiveness in this study using SPSS 22 with normality test results of 0.43, homogeneity test results of 0.68 proven sig value. &gt; 0.05 means that the data is usually distributed and homogeneous so that the T-test can be carried out, and the T-test results are 0.00, which proves the sig value. &lt;0.05 so that H0 is rejected, which means that there are differences in students' sociological learning outcomes. So it can be concluded that using Liveworksheet-based worksheets effectively improves student learning outcomes.
Class-specific diffractive cameras based on deep learning-designed surfaces

Recently, a new diffractive camera design based on transmissive surfaces structured using deep learning is proposed. It performs class-specific imaging of target objects and all-optical deletion of other classes of objects, which will promote the development of privacy-preserving digital cameras and mission-specific data.
An End to Shadow Banning? Transparency rights in the Digital Services Act between content moderation and curation

Now available in open access at Computer Law &amp;amp; Security Review: https://doi.org/10.1016/j.clsr.2023.105790. This paper offers a legal perspective on the phenomenon of shadow banning: content moderation sanctions which are undetectable to those affected. Drawing on recent social science research, it connects current concerns about shadow banning to novel visibility management techniques in content moderation, such as delisting and demotion. Conventional moderation techniques such as outright content removal or account suspension can be observed by those affected, but these new visibility often cannot. This lends newfound significance to the legal question of moderation transparency rights. The EU Digital Services Act (DSA) is analysed in this light, as the first major legislation to regulate transparency of visibility remedies. In effect, its due process framework prohibits shadow banning with only limited exceptions. In doing so, the DSA surfaces tensions between two competing models for content moderation: as rule-bound administration or as adversarial security conflict. I discuss possible interpretations and trade-offs for this regime, and then turn to a more fundamental problem: how to define visibility reduction as a category of content moderation actions. The concept of visibility reduction or ‘demotions’ is central to both the shadow banning imaginary and to the DSA's safeguards, but its meaning is far from straightforward. Responding to claims that demotion is entirely relative, and therefore not actionable as a category of content moderation sanctions, I show how visibility reduction can still be regulated when defined as ex post adjustments to engagement-based relevance scores. Still, regulating demotion in this way will not cover all exercises of ranking power, since it manifests not only in individual cases of moderation but also through structural acts of content curation; not just by reducing visibility, but by producing visibility.
Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches.
Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Nowadays, a digital image is often easily corrupted due to different forms of noise and complex processes resulting from the acquisition, compression, encoding, transportation, storage, retrieval, etc.All of these factors cause image quality to be distorted and visual information to be lost; in order to overcome this problem, Image denoising techniques are used widely to eliminate the various forms of noise that exist in the deteriorating image while keeping as many fine details and vital signal features as possible in the digital image.The wavelet denoising method aims to remove unwanted noise from a noisy image while preserving its vital features as a result of its ability to divide the degraded image into four sub-bands (sub-images) and operate at the frequencies of each one separately, where acquiring the original image content is vital to achieving reliable performance.This work introduces and implements a new hybrid system to the image denoising caused by Additive White Gaussian Noise (AWGN).The hybrid system is achieved using a combination of Median and Wiener filters as spatial domain filters with two-dimensional stationary and discrete wavelet transform (2D-SWT, 2D-DWT) as a multi-resolution analysis technique by applying 131 wfilters from the wavelet families (haar, db, sym, coif, bior, rbio, dmey, fk) in image processing at three levels of decomposition based on Hard, SureShrink, Bayesian, and Penalized threshold techniques on both high and low frequencies to distinguish and remove noise from affected pixel units and obtain improved results of the noise reduction process to the noisy image.Then, the multi-level 2D inverse wavelet transform (2D-IWT) eliminates noise and completes the image reconstruction by the hybrid denoising technique.Finally, the performance of the hybrid system has been estimated and measured by the peak signal-to-noise ratio (PSNR) value as an image quality metric.Experimental evaluation findings that the results of the proposed approach improved by about 17.5% by comparing them to the results of the related work, as well as enhancement the essence of image quality in terms of better noise reduction and edge preservation instead of using a multi-resolution WT domain or spatial domain filters separately.
Comparison Of Waterfall And Prototyping Models In Research And Development (R&amp;D) Methods For Android-Based Learning Application Design

&lt;p&gt;&lt;strong&gt;Design of learning applications that use Development Methods (Research and Development/R&amp;amp;D) using a commonly used system development methodology, namely SDLC (System Development Life Cycle). Several stages of SDLC consist of planning, analysis, design, implementation to system maintenance. The SDLC concept is the basis of various types of software development models to form a framework in planning and controlling information systems. The SDLC model that is often used in research and development (R&amp;amp;D) research for the development of Android -based learning applications is a waterfall and prototyping model. The comparison of the two models states that the waterfall model is more suitable for a system or software that is generic or in the form of software that can provide services to the buyer. While prototyping is more suitable for customize systems or software, meaning software made based on demand and needs (even certain situations and conditions). In accordance with the analysis obtained, the two methods have the advantages and disadvantages of each, so that the developer can choose which method is more suitable for the development of the software.&lt;/strong&gt;&lt;/p&gt;
Development Of Employee Attendance Information System Using Quick Response Code In Bekasi City Education Office

&lt;p&gt;&lt;em&gt;Attendance can be said as a sign of a presence which is an activity of Reporting in a company or government agency, except for the attendance system in the Bekasi city education office which generally still uses the finger print attendance system. Where this is still inefficient because the current situation still has to be completely limited by the effects of the covid-19 pandemic, therefore it is very necessary to implement an online attendance system development that can be used by employees in the Bekasi city education office. The purpose of this study is to develop an online attendance information system using the "Quick Response Code" where with the implementation of this online attendance system, employees can directly scan the Qr Code using an Android smartphone. The design of this online attendance system uses the Unifield Modeling Language (UML) method. The script editor used is Android studio and visual code. The results of making the development of this online attendance system in the form of an attendance system that can be done online so that the attendance process carried out by employees becomes easier and more effective to use.&lt;/em&gt;&lt;/p&gt;
Analysis of instrumentation system for photovoltaic pyranometer used to measure solar irradiation level

A pyranometer is a device used to measure the level of solar irradiation. This device has a sensor that measures the density of the electromagnetic flux of solar radiation on a flat plane. The electromagnetic flux density parameter is converted into an electrical parameter in watts per square meter. Pyranometers are used in weather station devices to analyze and predict weather conditions. Solar power generation systems are usually also installed with this device. It is intended to monitor solar irradiation's condition to examine the generating system's performance. This article discusses the photovoltaic-based pyranometer characterization method. The characterization method is carried out to determine the measurement parameters such as accuracy, precision, and hysteresis. Knowing these parameters will make it possible to see the performance of measuring solar irradiation levels by a measuring instrument for solar irradiation levels, like a pyranometer. The characterization method is to compare the measurement results with standard instruments. The solar irradiance level monitoring is also optimal, accurate, and precise with a reliable measurement method.
A Study of the Utilization of Apple Pencil in the Classroom for Students with Low Vision

Students with low vision make effective use of ICT (Information and Communication Technology) through accessibility settings, and in many cases can use ICT at the same level as able sighted students.In particular, tablets such as the iPad have become an indispensable part of their daily lives and are considered to be one of the tools that can dramatically enhance their learning environment.In this study, we tried to digitize their notes by using iPad and Apple Pencil.Our results showed, the iPad and Apple Pencil were not only effective in the promotion of content understanding and promotion of home study, but also proved usefulness in answering tests.The only ICT used in this experiment was the iPad and Apple Pencil.No other special equipment or environment is required.The only difference is that instead of the conventional form of distributing printed class handouts created by teachers on their PCs the handouts are converted to PDF files and distributed electronically.As for the applications, the standard installed ones are sufficient.Therefore, as long as students and teachers have iPads and Apple Pencils, it is relatively easy to introduce the system, and it is possible to spread the system as a measure for students with low vision.In addition, it is expected that ICT devices such as the iPad and Apple Pencil can be used as an effective support tool for their social participation.
MFEAFN: Multi-scale feature enhanced adaptive fusion network for image semantic segmentation

Low-level features contain spatial detail information, and high-level features contain rich semantic information. Semantic segmentation research focuses on fully acquiring and effectively fusing spatial detail with semantic information. This paper proposes a multiscale feature-enhanced adaptive fusion network named MFEAFN to improve semantic segmentation performance. First, we designed a Double Spatial Pyramid Module named DSPM to extract more high-level semantic information. Second, we designed a Focusing Selective Fusion Module named FSFM to fuse different scales and levels of feature maps. Specifically, the feature maps are enhanced to adaptively fuse these features by generating attention weights through a spatial attention mechanism and a two-dimensional discrete cosine transform, respectively. To validate the effectiveness of FSFM, we designed different fusion modules for comparison and ablation experiments. MFEAFN achieved 82.64% and 78.46% mIoU on the PASCAL VOC2012 and Cityscapes datasets. In addition, our method has better segmentation results than state-of-the-art methods.
Evaluation of Teachers’ Active Listening Training at Madrasah Ibtidaiyah Hidayatul Athfal

The aims of this research are to describe: 1) participants’ satisfaction with active listening training in reaction level, and 2) the effectiveness of active listening training in learning level. This evaluation research used the Kirkpatrick model (4 levels). The research objects who were involved in active listening training were teachers from first to sixth grades in Madrasah Ibtidaiyah (MI) Hidayatul Athfal. Data collection was through questionnaires which were distributed before and after training. Descriptive analysis was used to analyze the data. The evaluation results show that participants were satisfied with active listening training.