Reviewerly/exHarmony huggingface dataset:
Forecasting Breast Cancer: A Study of Classifying Patients’ Post-Surgical Survival Rates with Breast Cancer

Breast cancer is the most lethal form of cancer that can strike women anywhere in the world. The most complex and tough undertaking in order to lower the death rate is the process of predicting a patient's likelihood of survival following breast cancer surgery. Due to the fact that this survival prediction is linked to the life of a woman, effective algorithms are required for the purpose of making the prognosis. It is of the utmost importance to accurately predict the survival status of patients who will have breast cancer surgery since this shows whether or not doing surgery is the actual approach for the specific medical scenario. Given the gravity of the situation, it is impossible to overstate how important it is to investigate new and improved methods of prediction in order to guarantee an accurate assessment of the patient's chances of survival. In this paper, we collect data and examine some models based on the survival of patients who underwent breast cancer surgery. The goal of this research is to evaluate the forecasting performance of various classification models, including the Linear regression model, logistic regression analysis, LDA, QDA, KNN, ANN, and Decision Tree. The results of the experiment on this dataset demonstrate the better performance of the came up with ANN approach, with an accuracy of 82.98 percent.
ANALYSIS OF THE USE OF DISTANCE LEARNING FORMAT IN TEACHING THE DISCIPLINE "INTERCHANGEABILITY, STANDARDIZATION AND TECHNICAL MEASUREMENTS" USING GOOGLE FORMS

The article analyzes the experience of implementing training using remote components in teaching the discipline "Interchangeability, standardization and technical measurements" at the Odesa National Maritime University (ONMU) during the pandemic and the effect of the legal regime of martial law in Ukraine in order to find effective ways to improve the educational process. The study of the problems of education development using distance forms of education continues and has become even more relevant not only in connection with the COVID-19 pandemic, but also with the special conditions of today - the state of war in Ukraine. In just a few years, advances in technology, the COVID-19 pandemic, and martial law in Ukraine have revolutionized traditional education systems and fundamentally changed the scope of virtual and online learning in higher education institutions, primarily in ONMU. Modern trends require a rethinking of the basic processes of training future specialists. With distance learning, fundamentally new learning models are introduced into the educational process, which include holding conferences, independent work of students with information fields from various knowledge banks, project work, trainings and other types of activities with computer technologies. The source of information in these models are databases in the virtual learning environment, the coordinator of the learning process is the teacher, and the interpreter of knowledge is the student. Thus, the student's activity changes in the direction from acquiring knowledge to searching for it. Having considered the pros, cons and risks of such training formats, using his experience, a professional teacher will skillfully choose a type of activity that will allow the effective application of each of these training modes. Teachers: associate professors Pizintsali L.V. and Rossomakha O.I. improved professional competence through participation in an online seminar organized within the framework of cooperation between Google Ukraine and the Ministry of Education and Science of Ukraine. The teachers used this experience to determine the students' opinion about the mode of learning using remote components and to further improve the method of conducting classes. According to the results of the survey, the authors of the article claim that it is necessary to work on the improvement of education using the distance format and the development of online educational platforms in the long term, and its perception as temporary, for the period of martial law, is erroneous.
Evaluation of GAN-based Models for Phishing URL Classifiers

Phishing attacks by malicious URL/web links are common nowadays. The user data, such as login credentials and credit card numbers can be stolen by their careless clicking on these links. Moreover, this can lead to installation of malware on the target systems to freeze their activities, perform ransomware attack or reveal sensitive information. Recently, GAN-based models have been attractive for anti-phishing URLs. The general motivation is using Generator network (G) to generate fake URL strings and Discriminator network (D) to distinguish the real and the fake URL samples. This is operated in adversarial way between G and D so that the synthesized URL samples by G become more and more similar to the real ones. From the perspective of cybersecurity defense, GAN-based motivation can be exploited for D as a phishing URL detector or classifier. This means after training GAN on both malign and benign URL strings, a strong classifier/detector D can be achieved. From the perspective of cyberattack, the attackers would like to to create fake URLs that are as close to the real ones as possible to perform phishing attacks. This makes them easier to fool users and detectors. In the related proposals, GAN-based models are mainly exploited for anti-phishing URLs. There have been no evaluations specific for GAN-generated fake URLs. The attacker can make use of these URL strings for phishing attacks. In this work, we propose to use TLD (Top-level Domain) and SSIM (Structural Similarity Index Score) scores for evaluation the GAN-synthesized URL strings in terms of the structural similariy with the real ones. The more similar in the structure of the GAN-generated URLs are to the real ones, the more likely they are to fool the classifiers. Different GAN models from basic GAN to others GAN extensions of DCGAN, WGAN, SEQGAN are explored in this work. We show from the intensive experiments that D classifier of basic GAN and DCGAN surpasses other GAN models of WGAN and SegGAN. The effectiveness of the fake URL patterns generated from SeqGAN is the best compared to other GAN models in both structural similarity and the ability in deceiving the phishing URL classifiers of LSTM (Long Short Term Memory) and RF (Random Forest).
Analysis of The Use of Chinese Degree Adverbs by Indonesian Students

Abstract. Degree adverbs are one of the important and difficult learning points in Teaching Chinese as a second language (TCSL). Commonly, Chinese degree adverbs are divided into two types and four levels. Each type and level have different collocations, semantic features, etc. According to the academic article collections in CNKI and google scholar, research on Chinese degree adverbs from the perspective of TCSL for Indonesian students is still very limited. This study discusses the use of Chinese degree Adverbs by Indonesian students. This study aims to describe the student selection of Chinese degree adverbs and to find the most frequent error types in using four commonly used comparative degree adverbs: low-level ‘shaowei’, medium-level ‘bijiao’, high-level ‘geng’, and extreme-level ‘zui’. The approach to this study is a quantitative-qualitative method. The samples are Chinese Department students at one of the universities in Bandung city. The data was collected from two exercises. This study applies the error analysis procedure introduced by McDowell to find the most frequent error types. The result shows that the students tend to use pure degree adverbs, and the most used pure degree adverb is high-level ‘hen’, followed by its synonymous words ‘tebie’, ‘feichang’, ‘tai’, and ‘shifen’. In using synonymous degree adverbs, students tend to use the first learned word. The form of exercises in the teaching material should encourage the student to use more varied degree adverbs. The most frequent error types in using four different levels of comparative degree adverbs are selection in bijiao word sentences and zui word sentences, misordering of degree adverbs, and the omission of complement ‘yidianr/yixie’ in shaowei word sentences. The major cause of the errors is intralingual interference. This study made some suggestions for the improvement of Chinese degree adverbs teaching material based on the findings.Keywords: Chinese Degree Adverbs, Selection Tendencies, Frequent Error Types, Indonesian Students
Playful activities in the learning process

In the field of education, the benefits of playfulness are widely known in the teaching-learning process, from where they contribute to the integral development of the student. As a problem in the Juan Montalvo Educational Unit No. 41, these activities have not been used as a strategic tool to facilitate the teaching-learning process, the objective of the research is to analyze the playful activities in the teaching-learning process, in its development the playful characterizes , its benefits in the teaching-learning process are identified, and activities are proposed to strengthen the teaching-learning process. This study is based on a qualitative type of research where the documentary analysis and interview techniques were used. The study population corresponds to two managers, to whom the interview was applied. It was obtained as a result that the playful are necessary in the teaching-learning process, having benefits that contribute to creative thinking.
Class Balancing Methods Comparison for Software Requirements Classification on Support Vector Machines

Cost, time, and development effort can increase due to errors in analyzing functional and non-functional software requirements. To minimize these errors, previous research has tried to classify software requirements, especially non-functional requirements, on the PROMISE dataset using the Bag of Words (BoW) feature extraction and the Support Vector Machine (SVM) classification algorithm. On the other hand, the unbalanced distribution of class labels tends to decrease the evaluation result. Moreover, most software requirements are usually functional requirements. Therefore, there is a tendency for classifier models to classify test data as functional requirements. Previous research has performed class balancing on a dataset to handle unbalanced data. The study can achieve better classification evaluation results. Based on the previous research, this study proposes to combine the class balancing method and the SVM algorithm. K-fold cross-validation is used to optimize the training and test data to be more consistent in developing the SVM model. Tests were carried out on the value of K in k-fold, i.e., 5, 10, and 15. Results are measured by accuracy, f1-score, precision, and recall. The Public Requirements (PURE) dataset has been used in this research. Results show that SVM with class balancing can classify software requirements more accurately than SVM without class balancing. Random Over Sampling is the class balancing method with the highest evaluation score for classifying software requirements on SVM. The results showed an improvement in the average value of accuracy, f1 score, precision, and recall in SVM by 22.07%, 19.67%, 17.73%, and 19.67%, respectively.
Correction: Demidova, L.A. A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature. Mathematics 2023, 11, 792

The author wishes to make the following corrections to this paper [...]
Query-Based Hard-Image Retrieval for Object Detection at Test Time

There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applications such as autonomous driving, it is also crucial to characterise potential failures beyond simple requirements of detection performance. For example, a missed detection of a pedestrian close to an ego vehicle will generally require closer inspection than a missed detection of a car in the distance. The problem of predicting such potential failures at test time has largely been overlooked in the literature and conventional approaches based on detection uncertainty fall short in that they are agnostic to such fine-grained characterisation of errors. In this work, we propose to reformulate the problem of finding "hard" images as a query-based hard image retrieval task, where queries are specific definitions of "hardness", and offer a simple and intuitive method that can solve this task for a large family of queries. Our method is entirely post-hoc, does not require ground-truth annotations, is independent of the choice of a detector, and relies on an efficient Monte Carlo estimation that uses a simple stochastic model in place of the ground-truth. We show experimentally that it can be applied successfully to a wide variety of queries for which it can reliably identify hard images for a given detector without any labelled data. We provide results on ranking and classification tasks using the widely used RetinaNet, Faster-RCNN, Mask-RCNN, and Cascade Mask-RCNN object detectors. The code for this project is available at https://github.com/fiveai/hardest.
In media(tization) studies we love metaphors

The long history of mass communication theories is full of metaphors, from Shannon and Weaver’s ‘transmission channel’ to Noelle-Neumann’s ‘spiral of silence’. The objective of the chapter is to give an overview of the use of metaphors and models in mediatized communication studies. Special attention is given to the metaphors that support the representations of digital and interactive communication practices; in this context, the chapter deals with the metaphors of the Internet, the World Wide Web, and new platforms and introduces the main metaphors of media change. The chapter concludes with a series of reflections on the risks and benefits of metaphorical reasoning and includes a call for ‘metaphorical experimentation’.
Information System Strategic Planning at Institut Agama Islam Negeri Ternate

Institut Agama Islam Ternate (IAIN) is one of the universities that run business processes in terms of educational services. IAIN Ternate has implemented information systems and information technology (IS/IT) to support its business processes. However, the implementation of IS/IT has not been equipped with strategic planning, so all forms of procurement, development, and maintenance of IS/IT are only available upon request and are not yet aligned with the organization's strategic plan. This research was conducted to design organizational needs into an IS/IT strategic plan at IAIN Ternate using the Ward and Peppard Framework. This research was conducted using interviews and focus group discussions (FGD) with the leaders and staff of the units at IAIN Ternate. The tools used for the analysis of the organization's internal environment are SWOT and value chain; for the analysis of the organization's external environment, PESTLE and McFarlan Grid are used to map the application portfolio. Based on the results of the study, IAIN Ternate has utilized IS and IT. To achieve the vision, mission, and objectives of IAIN Ternate, it is recommended that IS/IT become a priority for development. The IT Strategic Plan consists of an IS strategy, an IT strategy, and an IS/IT management strategy.
Flashcard Sentence As A Media For Increasing Elementary School Students' Literacy Understanding

This study aims to find solutions to the challenges of learning to read difficulties for students who are at SDI Nurul Ulum Kab. Sumenep through interesting learning media in the form of Flashcard Sentence. The media is expected to be able to optimize the process of learning to read for elementary school students, as well as provide an understanding of what they read with media images related to the sentences read to increase students' literacy levels. This research uses Classroom Action Research (CAR) technique which goes through 3 cycles with each cycle containing 4 stages, namely planning, implementation, observation, reflection. After observing the Flashcard Sentence Learning Media, it can improve literacy understanding of SDI Nurul Ulum students by being shown when the Pre-action average score is 60.66 with a passing percentage of 0%, increasing in Cycle 1 the average score is 64.11 with a fixed passing percentage numbered 0%. After going through the evaluation process followed by cycle 2 getting an average score of 74.66 with a passing percentage of 55.56%, as the author's commitment at the beginning was that for a minimum graduation of 77.78% of the number of grade 2 students at SDI Nurul Ulum, cycle 3 was refined and giving an average score of 81.56 with a passing percentage of 100%.
Monitoring of qualification improvement programmes: learning transfer to practice model

The article actualizes the issue of the effectiveness of qualification improvement programmes, presents a four-level model for monitoring the learning transfer into practice, which aims to highlight the factors of the program‘s effectiveness. An effective qualification improvement program is considered to be one that creates conditions for the learning transfer into pedagogical practice. Based on this model, the case analysis of the monitoring of the X qualification improvement program revealed that the model creates prerequisites for the monitoring of the qualification improvement programs and the evaluation of their effectiveness. Keywords: learning transfer, monitoring of qualification improvement programmes, professional development.
The Divergence of Physics

It shows that the current physics is divorced from reality in many aspects and violates some basic scientific principles. This paper makes an illustration in six aspects: impossible time travel, unreal black hole photos, unvalid principle of equivalence, unscientific principle of electromagnetism, incorrect subjective analysis, and untrue lunar motion.
IdProv: Identity-Based Provenance for Synthetic Image Generation (Student Abstract)

Recent advancements in Generative Adversarial Networks (GANs) have made it possible to obtain high-quality face images of synthetic identities. These networks see large amounts of real faces in order to learn to generate realistic looking synthetic images. However, the concept of a synthetic identity for these images is not very well-defined. In this work, we verify identity leakage from the training set containing real images into the latent space and propose a novel method, IdProv, that uses image composition to trace the source of identity signals in the generated image.
Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks

Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.
SkateboardAI: The Coolest Video Action Recognition for Skateboarding (Student Abstract)

Impressed by the coolest skateboarding sports program from 2021 Tokyo Olympic Games, we are the first to curate the original real-world video datasets "SkateboardAI" in the wild, even self-design and implement diverse uni-modal and multi-modal video action recognition approaches to recognize different tricks accurately. For uni-modal methods, we separately apply (1)CNN and LSTM; (2)CNN and BiLSTM; (3)CNN and BiLSTM with effective attention mechanisms; (4)Transformer-based action recognition pipeline. Transferred to the multi-modal conditions, we investigated the two-stream Inflated-3D architecture on "SkateboardAI" datasets to compare its performance with uni-modal cases. In sum, our objective is developing an excellent AI sport referee for the coolest skateboarding competitions.
Exploration Of The 2021 Senja Drive-In Visitor Experience Using The Event Experience Conceptual Model

Drive-in Events are a new phenomenon in the event industry in 2020, emerging as innovations to overcome social distancing policies. The researcher was interested in exploring the experience of Drive-In Senja 2021 visitors using the event experience conceptual model, which helps add to the study of event experience and provides insight for the event industry in designing event experiences, especially Drive-In event experiences. This study uses the conceptual basis of the event experience conceptual model. Qualitative approaches and phenomenological methodologies, especially IPA (interpretative phenomenology analysis), were used to examine five informants representing three categories: couple, double date, and girls' night out. The results of data analysis found 37 emergent themes that formed 11 superordinate themes, which constructed the Drive-In Senja event experience conceptual model. The Drive-In Senja event experience conceptual model differs from the event experience conceptual model, namely a personal and social realms in a more detailed event environment. As a result, there are more superordinates in the personal realm than in the event environment, which shows the limitations of Drive-In Senja organizers in designing event environments during pandemic conditions.
The Reshaping of the Criteria for Defining the Criminal Organization of the Crimes of Cyber Mafia

Under the impact of the network society, some of the characteristics of the crimes of cyber mafia have undergone different degrees of transmutation, and the incompatibility between the original legal norms on the identification criteria of criminal organizations and judicial practice has become more and more obvious. After analyzing the substantive changes and regulatory dilemmas of the criminal organization of gangland and evil force under the threshold of the network based on the demarcation of the concept of network gangland and evil force, the reshaping of the definition criteria of criminal organization should emphasize the substantive side of organizational characteristics, while enhancing the systemic status of soft violence as the basis for crime identification, and increase the corresponding quantitative standard setting.