Reviewerly/exHarmony huggingface dataset:
Automated models of visual information processing

The article presents a study devoted to the development and research of an automated model of visual information processing. The goal of the research was to create a comprehen-sive model capable of automatically processing and analyzing various forms of visual data, such as images and videos. The model is developed on the basis of a combined approach that combines various algorithms and methods of visual information processing. The literature review conducted within the scope of this study allowed us to study the existing methods and algorithms for visual information processing. Various image processing approaches were analyzed, including segmentation, pattern recognition, object classification and detection, video analysis, and other aspects. As a result of the review, the advantages and limitations of each approach were identified, as well as the areas of their application were determined. The developed model showed high accuracy and efficiency in visual data processing. It can suc-cessfully cope with the tasks of segmentation, recognition and classification of objects, as well as video analysis. The results of the study confirmed the superiority of the proposed model. Potential applications of the automated model are considered, such as medicine, robotics, security, and many others. However, limitations of the model such as computational resource requirements and quality of input data are also noted. Further development of this research can be aimed at optimizing the model, adapting it to specific tasks and expanding its func-tionality. In general, the study confirms the importance of automated models of visual infor-mation processing and its important place in modern technologies. The results of the research can be useful for the development of new systems based on visual data processing and con-tribute to progress in the field of computer vision and artificial intelligence.
Phishing like the first step to gaining access

Phishing as a term that means the technique of sending phishing messages will be re-searched based on findings in public access and using the listed links. The process of a phish-ing attack will be analyzed, and then we will pay attention to the technical vectors of how us-ers become victims of the attack. Finally, existing research on phishing attacks and related prevention approaches will be reviewed. Mitigating phishing attacks is an important research topic worth exploring. Although a lot of research has been done, this threat still exists in the real world, and its prevalence is constantly increasing. According to research results, detecting phishing attacks is a difficult problem. There are two main strategies used to mitigate phishing attacks; or improving the performance of phishing detection technology or improving people's awareness of these at-tacks. Developing human expertise is a key way to defeat phishing attacks, as phishing attacks exploit human weaknesses rather than network weaknesses. Also, humans are always the weakest link in social engineering attacks. Compared to phishing website detection, phishing email detection may require user in-volvement to achieve better detection results. Because the success of a phishing email de-pends on its context. Specifically, when the premise of the phishing email is consistent with the user's work context (or current situation). Most anti-phishing solutions are implemented to mitigate general phishing attacks, but they ignore some specific situations, such as advanced phishing attacks. To prevent advanced phishing attacks, phishing websites are difficult to detect if a victim is attacked using stolen DNS data because the URL content and website content are the same as legitimate websites. Most content-based approaches may not work because the content of the accessed URL is an important factor in the decision. To prevent subdomain hijacking attacks, it is difficult to detect a phishing website if the phishers have hosted the website on a subdomain taken from a legitimate website. Regardless of the web content, URL, and SSL certificate information, they will all be the same as the le-gitimate website. Moreover, the approach to enumeration of subdomains needs improvement, as most current tools are based on rough enumeration, existing dictionaries may not cover all instances of subdomains, as some subdomains may be meaningless.
Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.
Unravelling the Web of Issues and Challenges in Healthcare Cybersecurity for a Secure Tomorrow

Cybersecurity is becoming increasingly crucial specifically for healthcare organizations. The advent of digital technologies such as telemedicine, the Internet of Medical Things (IoMT), and cloud-based solutions has brought about transformative advancements in patient care and administrative processes. However, with this progress comes an array of cyber threats that can have adversarial consequences if managed ineffectively. Driven by the growing concerns within the realm of cybersecurity, this paper delves into the issues and challenges faced by healthcare organizations in the deployment of cyber technologies. The research aims to dissect the multifaceted issues and challenges that contribute to compromised cybersecurity measures, resulting in potential data and system breaches. The study seeks to offer valuable insights, thereby paving the way for informed strategies and solutions to enhance cyber defenses and mitigate risks of unauthorized access and data breaches.
Two-Phase Semantic Retrieval for Explainable Multi-Hop Question Answering

Explainable Multi-Hop Question Answering (MHQA) requires an ability to reason explicitly across facts to arrive at the answer. The majority of multi-hop reasoning methods concentrate on semantic similarity to obtain the next hops or act as entity-centric inference. However, approaches that ignore the rationales required for problems can easily lead to blindness in reasoning. In this paper, we propose a two-Phase text Retrieval method with an entity Mask mechanism (PRM), which focuses on the rationale from global semantics along with entity consideration. Specifically, it consists of two components: 1) The rationale-aware retriever is pre-trained via a dual encoder framework with an entity mask mechanism. The learned representations of hypotheses and facts are utilized to obtain top K candidate core facts by a sentence-level dense retrieval. 2) The entity-aware validator determines the reachability of hypotheses and core facts with an entity granularity sparse matrix. Our experiments on three public datasets in the scientific domain (i.e., OpenbookQA, Worldtree, and ARC-Challenge) demonstrate that the proposed model has achieved remarkable performance over the existing methods.
Programmable heralded linear optical generation of two-qubit states

We investigated the heralded generation of two-qubit dual-rail-encoded states by programmable linear optics. Two types of scheme generating the states from four single photons, which is the minimum possible to accomplish the task, were considered. The schemes have different detection patterns heralding successful generation events; namely, one-mode heralding, in which the two auxiliary photons are detected in one mode, and two-mode heralding, in which single photons are detected in each of the two modes simultaneously. We show that the dependencies of the schemes' success probabilities on the target state's degree of entanglement are essentially different. In particular, one-mode heralding yields greater efficiency for highly entangled states if the programmable interferometers can explore the full space of the unitary transfer matrices. The reverse is the case for weakly entangled states, where two-mode heralding is better. We found a minimal decomposition of the scheme with two-mode heralding that is programmed by one variable phase shift. We infer that the linear optical schemes designed specifically for the generation of two-qubit states are more efficient than schemes implementing known linear optical gate--based circuits. Our results reveal a substantial reduction of physical resources needed to generate some types of nonmaximally entangled multiqubit states used in quantum computing.
EFFNet: semantic segmentation network for enhanced feature fusion in traffic scenes

Semantic segmentation of high-resolution traffic scene images is a challenging task due to complex backgrounds, diverse object shapes, similar appearances of multiple objects, and multi-scale characteristics of the same object. Many existing semantic segmentation networks only perform simple feature fusion when dealing with different object shapes and multiscale properties of the same object, often failing to provide satisfactory results. To address these issues, we propose an end-to-end multi-scale feature fusion network called EFFNet for traffic scenes semantic segmentation. EFFNet adopts an encoder-decoder structure, where ResNet-34 is used as the backbone network for feature extraction. At each stage of feature extraction, we introduce the Feature Fusion Module for multi-scale information. The Feature Fusion Module fuses the feature maps extracted by the backbone network at different stages, enriching the spatial and semantic information of the feature maps. Through end-to-end training, the accuracy of the segmentation results is significantly improved. We evaluated EFFNet on the traffic datasets Camvid and Cityscapes using a device equipped with a GTX 1650Ti graphics card. The results show that EFFNet achieves Miou scores of 55.3% and 50% on these two datasets, respectively. This indicates that EFFNet outperforms existing methods and demonstrates excellent segmentation accuracy in traffic scenes semantic segmentation tasks.
Active Contour Models for Boundary Extraction with Application to Medical Images with Noise

In medical image processing, active contour model is a method used to segment or extract the boundaries of an image for further processing. Recently, a selective active contour model called Selective Segmentation with Chessboard Distance (SSCD) model has been proposed to effectively segment a particular object in an image. However, the SSCD model has problems in extracting noisy images, which may result in poor segmentation. It is known that the presence of noise in some medical images cannot be avoided and can lead to poor segmentation. The aim of this research is therefore to reformulate the SSCD model to segment some medical images with noise. The modification is done by considering two different image denoising algorithms, the Gaussian filter and the bilateral filter, as new fitting terms in the SSCD model, resulting in two variants of modified SSCD models, referred to as SSCDG and SSCDB, respectively. The accuracy of the segmented image was evaluated using the Jaccard (JSC) and Dice similarity coefficient (DSC). Numerical experiments showed that the proposed SSCDG model based on the Gaussian filter denoising algorithm has the highest JSC and DSC values, which means the highest segmentation accuracy compared to the SSCD and SSCDB models. In the future, the proposed model can be extended to three-dimensional and color formulations.
Embracing Stretchable “Form Factor‐Free” Displays

Abstract Stretchable displays will offer a new level of convenience, versatility, and user experience.
CREATING ENGAGING E-MODULES FOR TEACHING PROCEDURE TEXT WRITING TO SEVENTH GRADERS AT ISLAMIC INTEGRATED JHS HARAPAN MULIA PALEMBANG USING GOOGLE SITES

Abstract:The development of current e-module for writing procedural text with Google stie was done and applied for seventh graders of Integrated Islamic JHS Harapan Media Palembang This RD applied the development model of Dick, Carey, and Carey to produce a valid, practical, and effective e-module of writing procedure text with Google Site. From the text, the learners and the teachers required creative and innovative e-module for writing procedure text with Google Site to develop learners’ learning independence, interest, and motivation. The research subjects were the 93-seventh graders and 3 Indonesian language teachers of Integrated Islamic JHS Harapan Mulia Palembang taken by saturated sampling. On the other hand, the effectiveness test involved 24 learners from different class within the same grade. The applied sampling technique was saturated sampling technique. The validity test of the e-module with Google site involved three experts. They were material, language, and media experts. The validity with material expert obtained a mean of 4.75, categorized as valid. The validity with language experts obtained a mean of 4.33, categorized as extremely valid. Then, the validity test with the media expert obtained a mean score of 4.92, categorized as very valid. The practicality test of the e-module with Google site was based on the one-to-one, small group, and field tests.The one-to-one test obtained a mean of 4.15, categorized as practical. The small group test obtained a mean of 4.21, categorized as very practical. Then, the field test obtained a mean of 4.14, categorized as practical. The effectiveness test of the e-module for writing procedure text with google sites was based on the pretest-posttest results. The pretest result obtained a mean score of 54.95, categorized unaccomplished while the posttest obtained the results of 81.21, categorized accomplished. From the development of an e-module for writing procedure text with Google site, the result showed that the seventh graders of Integrated Islamic JHS Harapan Mulia Palembang found the developed module valid and practical to learn procedure text writing. The product was also effective to improve the learners' procedure text writing.
ENCHANCING DESCRIPTIVE MATERIAL LEARNING AT PUBLIC JUNIOR HIGH SCHOOL THROUGH DIFFEREFTIATED LEARNING AND WIZER.ME E-WORKSHEET

: The aim of this research was to address the challenges posed by changes in regulations for admitting new learners at Public JHS 54 Palembang, where test scores or reports were no longer the primary criteria for enrollment. This research employed a modified model based on the work of Gall, Gall Borg, and Tessmer. The initial phase involved analyzing the diverse needs of seventh-grade students, encompassing their intentions, profiles, and readiness for learning. The curriculum changes had led to an increased reliance on textbooks, resulting in a need for supplementary worksheets. The researcher used the Canva application to design content within the Wizer.me platform. The validation process included expert validation of the prototype, focusing on material substance. Validation results indicated a high level of approval, with a percentage of 97.3% and a mean score of 4.86. The language aspect received an 86% approval rate, equivalent to an average score of 4.3. Material presentation in the learning materials received a 90% approval rate, with an average score of 4.5, affirming the validity of the developed worksheet.Following expert validation, a small group evaluation was conducted to assess the practicality and reliability of the developed worksheet for descriptive text materials employing differentiated learning. The expert validation results demonstrated that the developed worksheet, created using the Wizer.me application, was highly valid. Additionally, the small group evaluation confirmed that the worksheet was practical and reliable for use in an educational setting. In conclusion, this research successfully addressed the challenges posed by changes in admission regulations at Public JHS 54 Palembang by introducing differentiated learning materials. The use of electronic worksheets, implemented through the Wizer.me platform, proved to be an effective solution for catering to the diverse student profiles. The expert validation and small group evaluation affirmed the validity, practicality, and reliability of the developed worksheet, making it a valuable addition to the educational resources available for seventh-grade students.
Employee Information System Development at PT. Friends of Sakinah Flashlight Surabaya

PT Sahabat Sakinah senter is a service provider providing smart solutions, especially focusing on clearing in non-performing assets. We are committed to using information technology and algorithms to provide customers with quality non-performing asset collection strategies and resource pooling, as well as providing one-stop post-loan solutions. Our community service was carried out at PT Sahabat Sakina Center, a company engaged in billing customers who make online loans, where this company only accepts billing services. With the information system development training at PT Sahabat Sakinah Senter, it could modernizes its database and office systems.
Discriminant Analysis, Nearest Neighbor, and Support Vector Machine

This chapter covers three related machine learning techniques: discriminant analysis (DA), support vector machineSupport vector machine (SVM), and k-nearest neighbor (KNN) algorithms. It focuses mainly on classificationClassification but shows also how to extend SVM and KNN to a regressionRegression setup. The chapter starts by introducing the Discriminant analysis, which is a Bayesian approach to classificationClassification allocating unknown class membership using the Bayes rule. Here, we discuss both linear and quadratic DADiscriminant analysisquadratic DA, lending attention to the concept of decision boundary. The chapter goes on by introducing the k-nearest neighbors (KNN) algorithm, an extension of the discriminate analysis to nonparametric estimation of the decision boundary, which uses a local proximity-based imputation technique to classify. We show how KNN can be extended also to a regressionRegression setting. The chapter continues by introducing the Support vector machine (SVM), a method that can be used both in the classificationClassification and regression setting. It then devotes wide attention to the two-class SVM classificationClassification, to then extend SVMSupport vector machine classificationClassification to a multi-class setup. Finally, it discusses SVM regression. The applied part of the chapter is fully dedicated to the Stata, R, and Python implementations of the methods presented in the theoretical part.
Innovative educational environment and psycho-pedagogical interaction as factors of professional competencies development in a university teacher

Introduction. Uzbekistan currently pays special attention to introducing innovative educational technologies into the educational process at universities. This can be accomplished only if university teachers have the competencies necessary to use educational innovations when interacting with students. The article analyses the contribution of innovative educational environment and psycho-pedagogical interaction to the development of professional competencies of a university teacher. Materials and Methods. The study is a theoretical one. The following approaches were used: humanistic, environmental, system-structural, competence-based, innovative, synergetic, and general cultural. Results. The effectiveness of the innovative educational environment at a university is ultimately measured by the development of professional competencies of teachers. Such development is possible only where the teacher continuously interacts with students in a cooperative manner. Psychological adaptation and pedagogical cognition are considered as the main directions of implementation of psycho-pedagogical cooperation between teacher and students. In an innovative educational environment, university teachers acquire competencies required to solve socially significant tasks. Teachers’ professionalism increases in the process of exchanging ideas and experiences with students. Cooperation creates opportunities for self-management and self-development of all its participants and, as a result, is an important condition for the development of their communicative, personal-psychological and creative abilities. This means that cooperation with students can be considered as a prerequisite for the teacher’s aspiration for constant professional growth. Conclusion. Psycho-pedagogical cooperation in an innovative educational environment is an important condition for the formation of new forms of intellectual and creative activity of university teachers. Psycho-pedagogical cooperation in such an environment is also a factor of increasing motivation and support of university teachers, as well as a factor of development of professional competencies which are necessary to solve strategic tasks of the education system.
Demonstration Experiment of a System that Helps Users Select Educational Content Using Terms in Basic/advanced Relationships

With a range of learning platforms available on the Internet and the increasing use of video content in education, there have been many studies on using ontologies to support education and learning. However, in many cases, the connections among content systematized in this way are confined within a subject, and there is no learning across subjects or expansion of learners' interests beyond individual subjects. We aimed to connect all educational content in a way that goes beyond school subjects and curricula. In this study, we re-examined the learning sequence of subject matter terms over the course of the learning process, redefined it as a set of basic/advanced relationships among the terms, and proposed a method of estimating the appropriate ordering of content for learning by structuring and utilizing this terminological attribute. We also prototyped a system incorporating our proposed method to help users select educational content and tested it in a proof-of-concept experiment. By comparing the textbook-based presentation of related content with our proposed cross-subject presentation, we identified each method's characteristics and confirmed the proposed method's effectiveness at expanding learners' interests.