Reviewerly/exHarmony huggingface dataset:
Application of social media in advertising the fresh fruits and vegetables products: case study in Gombak, Selangor, Malaysia

Advertising is not a new platform in marketing, especially for people involved in business. This research focused on the application of social media in advertising fresh fruits and vegetable products in Gombak, Selangor. In this research, two objectives were involved: to determine factors of application of social media in advertising fresh fruits and vegetables among farmers and to determine the relationship between the independent variables and dependent variables. A simple random sampling technique was used to determine the sample size. The researcher carried out three analyses to analyze the result. Firstly, descriptive analysis was used to analyze the demographic of respondents. Next, correlation analysis was used to determine the relationship between the independent variables and dependent variables. The result showed three hypotheses that have positive and strong significance between the independent variables and dependent variables. Lastly, in order to answer the second objective, factor analysis was used to analyze the factors that influence the application of social media in advertising fresh fruits and vegetables among farmers. The factors consisted of cost-effective, social media marketing tools, and promotion. The result indicated in factor loading showed there was a good relationship between cost-effective, social media marketing tools and promotion with advertising of fresh fruits and vegetables.
Perl CGI and MySQL Essentials

The Perl Common Gateway Interface (CGI) module is a module capable of creating HTML markup code on the fly within your Perl script. It is mostly used as a means to catch the parameters of a page. While you could create entire pages with the methods included in the CGI module, it is a lengthy process and isn’t very efficient. A simple print statement with your HTML markup as the printed material is a much more practical use of the CGI module.
The significance of artpedagogical tools in military patriotic education of young students

In this article, the importance of art-pedagogical tools in the military-patriotic spirit and patriotic education of students, students, form of organizing and conducting training sessions, practical sessions and spiritual-educational events with their help, about the styles, about patriotism and its place in society and the state system, about the policies and reforms of the head of state in this regard, and about the author's personal thoughts and opinions based on scientific and modern knowledge, state political processes are revealed. &nbsp
MECHANISMS FOR IMPROVING THE QUALITY AND DENOISING OF IMAGES BASED ON THE CONVOLUTION AND RECURRENT NEURAL NETWORKS

This article provides an overview of methods for image enhancement and denoising based on convolutional and recurrent neural networks with the addition of a non-local operations block. These methods are widely used in various domains. In medicine, these methods improve MRI images, assisting doctors in making accurate diagnoses. In security applications, these approaches enhance images and enable better visualization of details. The article covers the main existing approaches to image enhancement. The article presents an analysis of the key characteristics of the investigated neural networks, as well as the scenarios in which they are most effective. It also includes a table of results from several image enhancement methods and introduces a research method for comparing its effectiveness in image enhancement. The strengths of each approach are highlighted, and their efficiency in different scenarios is discussed. Considering specific characteristics of denoising tasks such as noise patterns, image types, and processing constraints can help in selecting the most suitable architecture to achieve the desired outcome. The article also highlights the use of the non-local operations block to improve image quality. This block is used to capture global dependencies among pixels, allowing better modeling of relationships between different parts of the image. The non-local operations block enables efficient detection of long-range dependencies and contextual information, leading to improved denoising and image restoration. Overall, this article is useful for researchers in the field of image processing and machine learning who are interested in understanding the key differences between convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and exploring existing approaches to image enhancement and denoising. It provides a comprehensive overview of methods for image enhancement and denoising using convolutional and recurrent neural networks with the addition of a non-local operations block, along with information about existing approaches. The information and recommendations presented in this article can assist in selecting appropriate methods for addressing image processing tasks.
Newspapers, Images and Income Support Policy

To what extent do different newspapers have different kinds of images associated with articles on the same topic? We investigate this research question by considering one of the most important Income Support Policies implemented in Italy in recent times (‘Reddito di cittadinanza’ - RdC) which generated a strong debate in public opinion. Focussing on the national wide media, we downloaded images associated with articles about RdC and by means of Image Captioning algorithms, we generate the description of them. Results show that different newspapers have images containing different objects. Some topics emerging from images published by newspapers are very exclusive and the sentiment associated with the text extracted from the images has a wide heterogeneity. Furthermore, right-hand newspapers show a lower sentiment compared with left-hand newspapers. Overall, the results confirm that the ideological stance associated with different media outlets is reflected also in the images associated with articles and that the integration of Image Captioning algorithms and Natural Language Processes is very promising in this research area.
Density modelling with functional data analysis

Recent technological advances have eased the collection of big amounts of data in many research fields. In this scenario density estimation may represent an important source of information. One dimensional density functions represent a special case of functional data subject to the constraints to be non-negative and with a constant integral equal to one. Because of these constraints, a naive application of functional data analysis (FDA) methods may lead to non-valid results. To solve this problem, by means of an appropriate transformation, densities are embedded in the Hilbert space of square integrable functions where standard FDA methodologies can be applied.
Using categories as abstract structures to formalize computational models

The paper provides an analysis of various formal models of computation, including recursive functions, fuzzy models, and categorical models. These models are subject to research in order to identify ways of performing calculations based on them. In the context of each of these models, the concept of computability and basic arithmetic operations is introduced, which allows to deepen the understanding of their structures and mechanisms. The main conclusion of the study is that the considered formal models of calculations can be simulated in the context of different categories. This approach paves the way for the construction of an abstract theory of computability and corresponding programming languages on a categorical basis. This approach has the potential to create a universal programming language that would support the description of tasks from different subject areas. It is possible to implement the approach by interpreting these problems in the appropriate categories and using a universal category apparatus for their solution. It deserves special attention that such a language should be aimed at solving scientific problems, which reflects its potential value in the field of scientific research. In addition, this study can lay the foundation for further developments in expanding the capabilities of formal computational models. Given the presence of categories including fuzzy sets, fuzzy functions and partially recursive functions, the considered calculation models can be the object of analysis in the context of these categories. This provides an opportunity to construct an abstract theory of computability and corresponding programming languages based on a categorical basis. The development of the Haskell programming language also represents a first step in this direction, highlighting the importance and potential benefits of this approach. It is possible to consider Haskell as an experimental plane for the development of categorical concepts in programming languages, which opens new horizons for further research in this area.
Recommendation system for managing a Smart Home

Based on the results of the analysis of the history of development and the final stage of the smart home concept and data transmission methods, it can be concluded that the history of development has gone through a long and full of updates, finding the most optimal concept of operation. The chosen topic is currently particularly relevant, as the IoT concept can significantly improve many areas of our lives and help us create more convenient, smart and safe homes. The term “smart home” was first coined by the American Association of Homebuilders in 1984. With the invention of microcontrollers, the cost of electrical appliances was falling rapidly. The same organization noted that such a home is different from the usual one in its ability to ensure productive and efficient use of the working and living environment. Today, technology allows you to assemble home automation in a componentized manner: selecting only those smart home functions that the user really needs. Nowadays, the latest room management technologies appear every day. Even things that were previously seen only as beautiful interior items can now perform a number of multimedia or household functions. In the practical part, we evaluated the main characteristics of a software product designed to manage a smart home using cloud computing. Below is an analysis of various software implementation options in order to select the optimal one, taking into account both economic factors and product characteristics that affect performance and compatibility with hardware. For this purpose, we used a functional and cost analysis tool. A functional and cost analysis of the software was conducted. The use of remote control technology was investigated and modeled. The work included options for implementing significant economic alternatives. The comparison was based on labor, electricity, and capital costs. After the functional and cost analysis, the software is in development. After completing all the surface analyzes, it was found that the options for the server and client side programming languages are Javascript, and the use of the Amazon cloud platform and its virtual server is the most efficient goal.
Zero trust architecture: the basics organization principles

Zero trust (ZT) is the term for an evolving set of cybersecurity paradigms that move defenses from static, network-based perimeters to focus on users, assets, and resources. A zero trust architecture (ZTA) uses zero trust principles to plan industrial and enterprise infrastructure and workflows. Zero trust assumes there is no implicit trust granted to assets or user accounts based solely on their physical or network location (i.e., local area networks versus the internet) or based on asset ownership (enterprise or personally owned). Authentication and authorization (both subject and device) are discrete functions performed before a session to an enterprise resource is established. Zero trust is a response to enterprise network trends that include remote users, bring your own device (BYOD), and cloudbased assets that are not located within an enterpriseowned network boundary. Zero trust focuses on protecting resources (assets, services, workflows, network accounts, etc.), not network segments, as the network location is no longer seen as the prime component to the security posture of the resource. This document contains an abstract definition of zero trust architecture (ZTA) and gives general deployment models and use cases where zero trust could improve an enterprise’s overall information technology security posture. This article describes zero trust for enterprise security architects. It is designed to help understand zero trust for civilian unclassified systems and provide a roadmap for the migration and deployment of zero trust security concepts in an enterprise environment. Starting with a clear understanding of the organization’s business and data can lead to a strong zero-trust approach for enterprise security architects.
Human Posture Recognition Based on Isolation Forest Algorithm

With the rapid development of semiconductor technology and mobile communication networks, inertial sensors have higher performance, and correspondingly, the demand for human behavior recognition is increasing. At this stage, the mainstream research methods mainly analyze images in video streams. Although they can achieve good recognition results, due to the high requirements for camera location, environmental conditions, and privacy considerations, it is difficult to popularize behavior recognition methods for videos. Therefore, this article conducts research on human behavior recognition based on wearable sensor data, The innovative introduction of the isolation forest algorithm for prior processing of data can quickly detect anomalies in the behavior process, and also reduce data anomalies caused by the impact of sensor performance. Finally, the Convolutional Neural Networks is used for classification to obtain the final recognition results. The system has been tested on the MHEALTH datasets, and the recognition rate has reached 99.01%, achieving good classification results.
Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules

Abstract This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson 's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.
To get the best value out of data, you have to protect it

It has become a truism that data is the lifeblood of any modern organisation. But to reap the maximum value from data, you have to use it – and that makes it vulnerable. But this means you should view implementing an effective data security as a business investment – unlocking the potential of your data while keeping the organisation safe.
ALUMNI ENGAGEMENT PROGRAM SEBAGAI UPAYA UNTUK MENINGKATKAN KUALITAS AKADEMIK PROGRAM STUDI ILMU PERPUSTAKAAN

The purpose of this research is to examine the alumni engagement program in order to improve the academic quality of the study program. This study uses an exploratory method with a qualitative approach.. Focus Group Discussions (FGD) with alumni and interviews with the head of the Library Science Study Program, Faculty of Adab and Humanities UIN Syarif Hidayatullah Jakarta, were used to collect data. Data analysis was carried out in stages, including data reduction, presentation, and conclusion. The findings revealed that alumni of the Library Science Study Program, Faculty of Adab and Humanities UIN Syarif Hidayatullah, were involved in a variety of academic and non-academic programs, which were carried out by fellow alumni, active students, and those working with study programs. These initiatives allow alumni to stay connected to their alma mater. However, numerous programs did not run as anticipated, and other activities were unsustainable. This is because program implementation requires collaboration. As a result, the alumni involvement program in increasing the quality of the Library Science Study Program at UIN Jakarta has not been fully utilized.
A Sentiment Analysis Based Model for Recruitment by Higher Institutions

The traditional roles of a university are teaching and research with the aim of developing society and contributing positively to the national economic development by producing skilled and well-tutored graduates. However, recruitments by these higher institutions are too reliant on the eligibility provided by Resumes of candidates, while neglecting their suitability drawn from their research activity and publications online. This study identifies insights in recruitment trends in higher institutions of learning and uses Artificial Intelligence to produce a more rounded and balanced decision-making process that caters for both eligibility and suitability. The methodology employs the machine learning process using the Multinomial Naïve Bayes for training the model as well as the Vader sentiment analyzer for accuracy and testing. The datasets used contained Resume instances as well as author publication information. The results show a score of 83.9% for the model as well as a sentiment analysis score of 1, indicating an overall positive score. The results show that sentiment analysis can help educational institutions in improving their recruitment models and attracting more suitable candidates for such roles.
Regulation of Regional Economic Law Laws Incorporating Adaptive Differential Evolutionary Algorithms from a Blockchain Perspective

Abstract This paper firstly studies the regional economic law under the perspective of blockchain, using the characteristics of blockchain as a regulatory system outside the bottom layer, but also needs to combine the application scenario of supply chain finance and build a financial and economic regulatory system and a heterogeneous multi-chain cooperative regulatory system. Secondly, in response to the problems such as the irrationality of regional economic law, the price discrimination antitrust litigation system is proposed to play the role of economic recovery. Then the digital currency economic law is studied by using an adaptive differential evolutionary algorithm, using the process of crossover, variation, and selection to obtain digital currency behavioral characteristics so as to ensure the digital currency economic visualization analysis and to plan the monetary policy of economic law and analyze the digital currency economic risk. The results show that Bitcoin’s daily average right-tail VaR (94.8%) is the lowest among the listed crypto-digital currencies at 2.58%, while the left-tail mean is the highest among the three crypto-digital currencies, which is consistent with the fact that BTC has been issued for the longest time and is more mature among the crypto-digital currencies with the most dramatic tail risk variation as well as the largest magnitude.
The construction of a professional English online and offline hybrid teaching model based on data mining technology

Abstract In order to provide fresh concepts for the construction of a professional English teaching model, digital mining is used as a novel analysis tool. In order to teach professional English, this study first develops a combination of both online and offline education models based on the traits of English teaching. Second, using data mining the internet, we choose rough set theory as the primary algorithm for this study. We then analyze and decompose the information data table and build a rough set model for the mixed English teaching data. Finally, a process assessment and summative evaluation are used to analyze the hybrid online and offline English teaching software for professional English. The experimental class saw a 6.5% gain in scores, which was a much larger value and amount of improvement than those of the other seven classes. It shows that the learning effect of the online and offline blended learning style is better than the traditional learning mode.
Toward more personalized breast cancer risk assessment

ABSTRACT Healthcare providers often are uncertain about how best to assess and manage breast cancer risk. Women at average risk wonder when to start mammography and how often to go. Women at increased risk might inquire about genetic testing, MRI screening, and preventive measures. Patients who carry gene mutations face higher stakes and more complex risk management choices, but only some are aware of their status. This article helps clinicians stratify breast cancer risk and discusses a newer genomic test, the polygenic risk score, that may enable more personalized risk management and decision-making.
The Implementation of User Centered Design Method in Developing UI/UX

The university has an academic information system called AIS, of which students are one of the main users. Based on the results of the questionnaire from previous research, it has been proven that the AIS display still needs development. This research develops the UI/UX of AIS to better suit the needs of users with the UCD method. Starting from planning agreements with parties related to research, determining the context by interviewing parties who agreed in the first stage, determining needs with questionnaires to users, using the System Usability Scale (SUS) method to determine the results, designing a prototype based on the results of the third step, and UI/UX evaluation, which is carried out using the same method when evaluating the UI/UX AIS running in the third stage to prove whether there has been a significant improvement or not. The evaluation results show that there is an increase in UI/UX, and based on statistical tests, there is a significant difference between the current UI/UX AIS and the UI/UX AIS being developed. In the process of evaluation and statistical tests, even though the SUS calculation results were still marginal, the two AIS questionnaire results proved to have significant differences. So that the results of this study can be accepted, improvements can be made in the future.