Reviewerly/exHarmony huggingface dataset:
ENHANCING CUSTOMER SATISFACTION THROUGH SOCIAL MEDIA

The purpose of this research is to determine the impact of utilizing social media to increase customer satisfaction. To address the study's questions and evaluate hypotheses, a quantitative technique was adopted to answer questions and to test hypotheses. About 500 questionnaires were distributed using the Simple Random Sample technique to achieve the research purposes in Jordan using Vitas Company as case study. 392 questionnaires were valid for analysis, based on the Statistical Package for Social Sciences (SPSS. V.25) and (AMOS) software. The findings of the research revealed that there is a significantly positive impact of social media usage on customer satisfaction. The research results confirmed the importance of using multichannel of social media to enhance customer satisfaction and increase their loyalty and recommended managers concentrate and maintain the use of social media for providing unique services and responding to customers’ inquiries in a timely manner. Additionally, the research showed the need to expand the usage of Twitter and Instagram for marketing purposes, and seeking services that exceed customer expectations and recruiting and properly training qualified staff in the social media department. Finally, companies should pay attention to customers' opinions through comments on posts and social media chat messages, because it has a positive role in improving the customer feedback process.
EFEKTIFITAS PROBLEM BASED LEARNING MENGGUNAKAN MEDIA SMARTPHONE DITINJAU DARI KEMAMPUAN KOGNITIF SISWA

Currently, a technology media is needed in a mathematics learning process. However, some technological media such as smartphones are still not used optimally in creating a certain learning method in learning mathematics. This study aims to test the effectiveness of the Problem Based Learning (PBL) learning method facilitated by smartphone media by utilizing students' cognitive abilities. This study uses a quantitative approach (pretest-posttest with nonequivalent group design) with two treatments, namely the experimental class using the PBL method using Smartphone media and the control class using the Conventional PBL method. The subjects of this study were class VIII students who collected 63 students in the control class and 60 students in the experimental class. The effectiveness of the treatment in both classes was analyzed using the one sample t-test with a significance level of 5%. The data from this study were analyzed using independent sample t-test with a significance level of 5%. The results of this study indicate that PBL using smartphone media is more effective than conventional PBL. Based on the results of this study, a new variation of the Problem Based Learning (PBL) method was found, namely this method can be done through face-to-face learning or online learning.Keywords: Problem Based Learning, smartphone, insturctional media
Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.
Education and Training Against Threat of Phishing Emails

The research results published in this article are oriented toward two areas: phishing email analysis and education for defense against the threats of phishing emails. The first topic builds on previous research primarily by analyzing changes in captured phishing emails over an interval of 4 weeks, half a year after the previous experiment. In this section, a statistical survey of phishing emails from both experiments is carried out and emails are segmented into categories focused on business, charity, asset transfer, and fund offers. The results of both experiments are then compared and validated. Based on this comparison and validation, a conclusion is made on trends and development in the phishing email domain in the last half a year. The second focus of our research is analysis of the existing education and testing systems for phishing emails. Based on the results of the analysis, a suitable system for university education and training against phishing and other malicious email threats will be designed. There is also an analysis of existing systems for improving and testing users' ability to recognize and react to phishing emails. Based on our findings about these systems, our own system is proposed. An experiment is prepared on "self-service" testing of phishing email detection skills performed by students with their colleagues. Some activists were employed to assist with this experiment; they will operate and prepare the environment according to the processed scenario. All experiments must be completely safe and effective at the same time. The experiments will be evaluated and the experience used to develop the education and training system at the university.
Ensuring the Security of Space Systems from Eavesdropping Attacks

In a day and age where satellite communications are more important than ever to ensure global communication, establish international military power, and support our everyday way of life, satellite security must be at the forefront of innovation. However, eavesdropping attacks pose a serious threat to satellite communication systems that have not been adequately addressed. An eavesdropping attack threatens to put sensitive data in the wrong hands or even jeopardize critical missions. Research is needed to explore why defense against eavesdropping attacks is crucial, particularly for satellite systems. Three potential solutions to the problem are presented, addressing different challenges. Realistic solutions to the eavesdropping threat are needed urgently to defend the space domain from malicious threats.
Context-Aware Cyber Threat Intelligence Exchange Platform

The ubiquity of network and internet-connected devices has increased exponentially in the past decade. The proliferation of end-user devices has created a lucrative environment for cybercriminals to exploit unsuspecting users at a personal and organizational level. Moreover, businesses and governments are heavily reliant on cyberspace to conduct their business. According to Accenture, in 2019 South Africa saw a spike in cyberattacks on all fronts—banks, Internet Service Providers (ISPs), utilities and eCommerce platforms. This shows that threat actors are continuously looking to exploit new and old vulnerabilities at ever-increasing rates. Furthermore, threat actors are sharing tactics, tools, and procedures to expand their attack surface and to improve the effectiveness of their attacks. Security research tends to be an insular process and rarely do individuals or groups share threat data. This is due to lack of trust, organizational policies, or simply the inability to get the information out to the masses. The idea behind this paper is to design a context-aware threat intelligence exchange platform that encourages collaboration and creates a federated environment amongst different industry stakeholders to share Indicators of Compromise. This paper further aims to define the process of transforming raw Indicators of Compromise into cyber threat intelligence. The platform described in this paper, when implemented, would provide the basic building blocks for developing a highly effective cybersecurity intelligence-sharing system that can improve vulnerability detectionand remediation by speeding up the time required to identify/resolve incidents.
NoSQL: Will it be an alternative to a relational database? MySQL vs MongoDB comparison

NoSQL databases and related technologies are rapidly evolving and are commonly used in a variety of scenarios (consistency, availability, partition tolerance), and this has sparked controversy in the database world in several, so this paper aims to shed light on the characteristics that determine the use of databases.By discussing the challenges and studying the differences in depth and trying to answer the question related to the future use of the database, we started by discussing both relational and non-relational databases, shortcomings, and challenges, then we chose a model of both types, and we studied the differences in practice in the query process by installing database servers, it is an important part of the data handling process.We also discussed the popularity of the two models adopted for several considerations and concluded that non-relational databases, despite the challenges, tend to be the first choice soon.
The Comparison of E-Modules and Print Modules to See Character Like to Read on Elementary School Students

Technological advances are not new for students in Indonesia, whether they use electronic learning media in their daily lives or not. One example is the application of electronic modules. This study aims to determine the comparison of the print modules and the e-modules. This type of research is comparative research. Comparative research is research that compares the object under study. The method used in this study is a quantitative method using a survey research design. The sample of this research is class IV students, totaling 22 people. Data analysis used descriptive and inferential statistics, namely analyzing data by describing the data that has been collected. The results of this study were from the two teaching materials applied, namely the printed module and the electronic module, it was possible to see indicators of reading fondness characters. In the study, the dominant results were good for looking at indicators of reading fondness characters with the application of electronic modules, while for the application of print modules in the sufficient category, it can be seen that there are differences between the two teaching materials that are applied to see indicators of reading fondness characters, this is reinforced by the test results t that has been done, where the obtained sig is smaller than 0.05 then there is a difference between the two. So the electronic module based on local wisdom needs to be applied by teachers to instill the character of reading fondness among students.
Navigating the Network: An Exploratory Study of LGBTQIA+ Information Practices at Two Single-Sex HBCUs

Using focus groups and interviews, this study investigates the information practices of 23 LGBTQIA+ students attending Morehouse College and Spelman College in Atlanta, GA. Responses suggest that students rely heavily on peer support and word of mouth for information seeking and sharing. Social media seems to play a large role in sharing information on campus, even for students who are not active on social media platforms. And students’ personalities (such as level of extroversion) may indirectly impact the amount of information they receive. Overall, these students seemed to face the challenge of being accepted for who they were on campus, which may have negatively impacted their information exchanges.
Beyond counting the correct responses: Metacognitive monitoring and score estimations in mathematics

Abstract This study investigated how well students differentiate their responses' accuracies (metacognitive monitoring) and estimate their test scores beyond counting—and counting on—the number of correct responses alone. Monitoring abilities of 2832 sixth‐graders (1410 male and 1422 female native in Turkish) at an 11‐item Program for International Student Assessment (PISA)‐equivalent mathematics test were measured via response‐contingent Type‐2 signal detection theory. The students also made score estimations right before and immediately after completing the test (pre‐ and posttest estimations, respectively). Although high‐scoring students underestimated and low‐scoring ones overestimated how they would perform in the test, high‐scorers were accurate in their posttest estimations unlike the low‐scoring group, where the lattaer retained their overestimation tendencies. Having better monitoring performance, the high‐scoring group could subsequently calibrate their posttest estimations. Additional assessment methods such as measuring monitoring and score estimations seem to have the potential to reveal how mathematics students behave before, during, and after responding.
A low-cost pathological image digitalization method based on 5 times magnification scanning

Digital pathology has aroused widespread interest in modern pathology. The key to digitalization is to scan the whole slide image (WSI) at high magnification. The file size of each WSI at 40 times magnification (40×) may range from 1 gigabyte (GB) to 5 GB depending on the size of the specimen, which leads to huge storage capacity, very slow scanning and network exchange, seriously increasing time and storage costs for digital pathology.We design a strategy to scan slides with low resolution (LR) (5×), and a superresolution (SR) method is proposed to restore the image details during diagnosis. The method is based on a multiscale generative adversarial network, which can sequentially generate three high-resolution (HR) images: 10×, 20×, and 40×. A dataset consisting of 100,000 pathological images from 10 types of human body systems is used for training and testing. The differences between the generated images and the real images have been extensively evaluated using quantitative evaluation, visual inspection, medical scoring, and diagnosis.The file size of each 5× WSI is approximately 15 Megabytes. The peak-signal-to-noise ratios (PSNRs) of 10× to 40× generated images are 24.167±3.734 dB, 22.272±4.272 dB, and 20.436±3.845 dB, and the structural similarity (SSIM) index values are 0.845±0.089, 0.680±0.150, and 0.559±0.179, which are better than those of other SR networks and conventional digital zoom methods. Visual inspections show that the generated images have details similar to the real images. Visual scoring average with 0.95 confidence interval from three pathologists are 3.630±1.024, 3.700±1.126, and 3.740±1.095, respectively, and the P value of analysis of variance is 0.367, indicating the pathologists confirm that generated images include sufficient information for diagnosis. The average value of the Kappa test of the diagnoses of paired generated and real images is 0.990, meaning the diagnosis of generated images is highly consistent with that of the real images.The proposed method can generate high-quality 10×, 20×, 40× images from 5× images, which can effectively reduce the time and storage costs of digitalization up to 1/64 of the previous costs, which shows the potential for clinical applications and is expected to be an alternative digitalization method after large-scale evaluation.
Using ordinal logistic regression to analyse self-reported usage of, and attitudes towards swearwords

Likert-type data is commonly used in many research fields in humanities: from gaging the usability of different user-interface designs, to determining users’ likeliness to vote for a particular political party, to evaluation of course materials–to name but a few examples. Despite its prevalence, there is still some disagreement within the statistics community on whether Likert-type scales are true ordinal variables, and by implication whether parametric tests are legitimate to be used in such cases (Endresen &amp;Janda 2017).In this paper, we explore one parametric statistical test, viz. cumulative odds ordinal logistic regression (OLR),as an analysis method for self-reported data in the humanities. For illustration purposes, our focus is specifically on data of users’ self-reported usage of, and attitudes towards swearwords, with the aim of identifying demographic attributes that are predictive of their usage and/or attitudes. After a brief description of the data we’re using, including how the data is being collected, we give a layman’s overview of OLR. Since one of our aims is to demonstrate the usability of OLR, we apply our discussion practically to a step-by-step procedure (based on Laerd Statistics 2015) that could be followed easily. We demonstrate the usefulness of the results in reporting on the usage of, and attitude towards two near synonymous Afrikaans swearwords. We show, amongst others, that the odds ratios that are generated as part of the modelling procedure can be used to draw direct conclusions about specific demographic groups.
Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration

For automatic disease-severity-level estimation, a large-scale medical image dataset with level annotations is generally necessary. However, attaching absolute-level annotations (such as levels 0, 1, and 3) is very costly and even inaccurate due to the level ambiguity. In this study, we proved experimentally that using a ranking function for level estimation can relax this difficulty. We propose a multi-task learning method for automatically estimating disease-severity levels that combine learning to rank with regression. The ranking function of the proposed method is trainable by relative-level and a small number of absolute-level annotations. For relative-level annotation, an annotator only needs to specify that one image has a higher disease level than another&#x2014;this is much easier than absolute-level annotation. The proposed method enables disease-severity classification by calibrating the ranking function based on relative-level annotation through regression. The effectiveness of the method was proved through a large-scale experiment of ulcerative colitis-severity estimation with colonoscopy images.
An Improved Cervical Cell Segmentation Method Based on Deep Convolutional Network

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.
KESANTUNAN BERBAHASA DOSEN TERHADAP MAHASISWA DALAM PEMBELAJARAN BAHASA INDONESIA

This study describes the politeness of language carried out by lecturers to students in learning Bahasa at a private university in the Karawang district. The method in this research is the descriptive qualitative. The data in this study are in the form of lecturers' speeches containing language politeness to students in learning Indonesian. The results and discussion in this study referred to Leech's theory, the researcher found that the maxim of wisdom is 13 utterances, the maxim of generosity is 11 utterances, the maxim of praise is 15 utterances, the maxim of humility is 11 utterances, the maxim of agreement is 10 utterances, and the maxim of sympathy is 13 utterances. The total number of politeness utterances from the lecturer is 73 utterances. Furthermore, based on the interview result, the researcher found out that the lecturer remained polite, especially using language towards the speech partner. The researchers conclude that the lecturer has realized polite speech using Leech's theory, and the lecturer will continue to speak consistently to his partner, especially in the academic setting.