Reviewerly/exHarmony huggingface dataset:
Feature Reduction and Hybrid Machine Learning Model for Email Spam Prediction

Email is a well-known and effectual technique of internet communication and data to share messages. It is employed as an electronic messaging platform to send the messages. However, the increasing significance and higher deployment of emails lead to maximize the number of spam emails. The predictive methods employ distinct phases to predict the email spam in which the data is pre-processed, the attributes are extracted and the data is classified. This work suggests a new mechanism for predicting the email spam. This mechanism employs Principal Component Analysis to mitigate the features. Thereafter, similar type of information is clustered using KMC algorithm. In the end, a voting technique is presented in which K- Nearest Neighbor, LR and Support Vector Machine are implemented. Python is executed to simulate the suggested mechanism. The outcomes are analyzed concerning accuracy, precision and recall.
IoT-enabled Secure Service-Oriented Architecture (IOT-SOA) through Blockchain

Web providers publish service descriptions in a registry for clients to find and use in conventional SOA. This design allows loosely connected service invocation but lacks a dispute resolution mechanism, compromising service calls between untrusted consumers and providers. However, integrating blockchain technology into classic SOA enables a revolutionary trust solution. IoT-enabled Service-Oriented Architecture (IOT-SOA) proposes a blockchain-based verified SOA architecture for IoT device service invocation. During a trusted service call, the consumer securely provides encrypted parameters to the provider in this innovative framework. The service provider expertly decrypts the parameters, conducts the service, and encrypts the output. The service provider methodically develops and stores a trusted credential on the blockchain while sending the encrypted result to the customer. A smart contract for arbitration resolves service-related issues. Experimental results show that, compared to traditional invocations, the proposed method effectively resolves service disputes between service providers and requesters while limiting service invocation growth to 30%. The exceptional IOT-SOA design builds trust by combining blockchain technologies and the SOA framework and leaves trust issues behind as we transform the IoT-enabled SOA model.
MANAGEMENT OF SCHOOL LITERACY WITH STUDENTS' INTEREST IN READING

The purpose of this study was to determine the effect of literacy program management on students’ reading interests. The method used is a quantitative method that is processed using statistical methods which analyze the results of the distributed questionnaire data. The population of this study was 344 students of SMP Muhammadiyah Puraseda, Bogor Regency, while the sample taken was 15%, namely 52 respondents. The calculation results have a positive effect as evidenced by the product-moment correlation coefficient (rcount) of 0.402 and rtable at N = 52 with a significant level of 5% of 0.27. The results of the study show that 1) Implementation of school literacy management in a good category is implemented in four main areas, namely planning, organizing, implementing, and evaluating programs. 2) Students’ interest in reading is still in the moderate category, 3) There is a significant relationship between literacy program management and students’ reading interest.
Digital psychological tools in complex emergencies. Putting the concept into action in Ukraine

This text contains information about these digital psychological tools in complex emergency situations. Unprecedented number of civilians joined armed forces of Ukraine rapidly, increasing the total number of mobilised personnel 4-5 times. Civil mental health professionals were also mobilised to support service members and territory defence units and their family members. Since the beginning of war, international military psychiatry and psychology experts were supporting Ukrainian community in various ways, including participation in educational webinars and panel discussions in such topics as psychological first aid for military, psychiatric care during ongoing operation, survivability in combat situations, leadership in the army, transition back from war. However, due to shortage of military psychologists and psychiatrists, prohibition for civil psychologist to provide support at military hospitals and limited capacity to provide mental health support at the front line highlights the value of digital formats. On the second day of the war, a Telegram-based self-help chatbot called was activated for Ukraine. A chatbot simulates a text conversation with a virtual psychologist on a mobile phone. The chatbot is based on psychological first aid, with the recommendations adjusted for the ongoing stress of the war (Frankova et al, 2022). It contains various exercises, tasks, and useful recommendations on relaxation exercises and stress management. Eight weeks after being made available it has already reached more than 50 000 users. The chatbot was developed to promote mental health and based on existing resources, capable to deliver neuroscience and evidence-based recommendations during the ‘Golden Hours’ right after the escalation of war in Ukraine.
Efficient segmentation model using MRI images and deep learning Techniques for Multiple Sclerosis Classification

The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the Magnetic Resonance Imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the pictures in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. Here, the MRI pictures are initially gathered from traditional online databases. The gathered images are directly given to the image segmentation process, where the Multi-scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed Random Opposition of Cicada Swarm Optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process, where the Hybrid and Dilated Convolution-based Adaptive Residual Attention Network (HDCARAN) is utilized to categorize the lesions from the MRI images very effectively to detect the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously dev
PERKEMBANGAN KEMAMPUAN BAHASA ANAK USIA 4-5 TAHUN MELALUI BERCERITA SEDERHANA DITINJAU DARI SEGI ASPEK SINTAKSIS DAN SEMANTIK

The development of language skills in children aged 4-5 years often experience obstacles in conversation, delivery or expression and talking to other people. The problem that occurs in children aged 4-5 years in the A6 group RA Muslimat NU Masyithoh 05 Banyurip Ageng Pekalongan City is that the development of children's language skills in terms of syntactic and semantic aspects of children is still not well developed. The purpose of simple storytelling activities is to determine the development of children's language skills, especially in the syntactic and semantic aspects. The research method uses a qualitative descriptive method. The research site is at RA Muslimat NU Masyithoh 05 Banyurip Ageng, Pekalongan City. The research period is from May 29 to June 11, 2021. The research subjects were 2 students in group A6 with an age range of 4-5 years with data sources from 15 students. Data collection techniques using observation, interviews, and documentation. The results of a study conducted for approximately 2 weeks with a sample of 2 children showed that through simple storytelling, children aged 4-5 years old could develop language skills.
HUBUNGAN ANTARA KEGIATAN BERMAIN MAKE A MATCH DENGAN KEMAMPUAN BERHITUNG ANAK USIA DINI

The purpose of this study was to determine the reality of making match play activities, the reality of early childhood numeracy skills, and the relationship between make a match play activities and early childhood numeracy skills in Group B2 RA Mukhlishiina Lahuddiin Cinunuk Village, Cileunyi District, Bandung Regency. The research method used in this study uses a quantitative correlational method. The results of the analysis on the activity of playing make a match obtained an average value of 80.95. This value is in the 80-100 interval with a very good category. Meanwhile, early childhood numeracy skills obtained an average value of 78.6. This value is in the 70-79 interval with a good category. The relationship between the activities of playing make a match with early childhood numeracy skills obtained a correlation coefficient of 0.937. The value of this correlation coefficient is included in the very strong category because it is in the 0.800-1,000 interval. The results of the significance test obtained tcount = 10.04 &gt; ttable value = 2.14. This means that the null hypothesis is rejected and the alternative hypothesis is accepted. Thus, the activity of playing make a match has a significant relationship with early childhood numeracy skills in Group B2 RA Mukhlishiina Lahuddiin Cinunuk Village, Cileunyi District, Bandung Regency. Meanwhile, the contribution or influence on the activity of playing make a match on early childhood numeracy skills is 87.8%. Thus 12.2% is influenced by other factors.
PERAN GURU DALAM PENGENALAN LITERASI DIGITAL PADA ANAK USIA DINI PADA MASA COVID-19 DI TK SE-KECAMATAN PAUH DUO

This study was conducted to see the extent to which the role of teachers in the introduction of early childhood digital literacy during the COVID-19 pandemic in kindergartens throughout Pauh Duo District, South Solok Regency. This study resulted that the overall average role of teachers in the introduction of digital literacy in early childhood during the Covid-19 pandemic in kindergartens in the Pauh Duo District, Solok - South Regency showed a figure of 57.42% in the less category with a score of &lt;62 .5%, that is, it can be said that the role of teachers in the introduction of digital literacy in early childhood is in the less category, and it is necessary to increase the digital literacy competence of teachers in kindergartens in Pauh Duo Sub-district, Solok Regency - South.
BALINESE INTEREFERENCE OF ENGLISH NEWS ANCHOR

The presence of English in Indonesian leads bilingualism and multilingualism. Bilingualism and multilingualism community cannot avoid using interference because it is the effect of a first language on their production second language. Interference occurs in any linguistics aspects likes phonology, morphology, syntax and semantic. Based on the analysis on the news entitled “The World Deepest Under Water Christmas Tree- New World Record Set at 37.5 meters” which has aired on Bali Channel Program, found that any interference occurred in the aspect of phonology, morphology, syntax and semantic. The interference was caused by lack of bilingual ability where first language acquisition interferes in producing second language and ignorance of grammatical structure of the second language.
أثر تطبيق استراتيجيات إدارة المواهب البشرية على الإبداع التنظيمي بكلية الاقتصاد -الخمس جامعة المرقب

The aim of this research is to identify the level of application of human talent management strategies in the College of Economics - Al Khums / University of Al-Marqab, and to know the level of organizational creativity, the level of organizational creativity in the college under discussion, and to identify the impact of applying human talent management strategies represented in (attracting and attracting talent, developing talent, Retention of the gifted) on the organizational creativity of the faculty members in the college under study, and the research relied on the descriptive approach, targeting all the permanent faculty members of the Faculty of Economics - Al-Khums / Al-Marqab University, who taught during the semester (Spring 2022 AD), and their number is ( 83) a faculty member, and to achieve the objectives of the research, a questionnaire was designed for the purpose of collecting primary data and distributed to a sample consisting of (68) faculty members, and the spss statistical program was used to analyze the research data, and the research results showed that there is a weak level of application of management strategies Human talents in their dimensions (strategy of attracting and attracting talents, talent development strategy, talent retention strategy) in the college under study. The results also showed that there is a medium level of organizational creativity in the college. The level of organizational creativity in the college under study.
An ensemble WideResNet learning-based approach for classification of multi-class colorectal cancer tissue types in histology images

Histopathology imaging (HI) plays a significant role in enhancing the prognosis of colorectal cancer (CRC), which ranks as the second leading cause of cancer-related deaths globally. Classifying colon cancer tissues with HI can be challenging due to differences in morphology, the presence of artifacts when recording microscopic images, and the lack of histological expertise. The use of WideResNet network structure as a novel method for identifying textures in HIs is proposed using deep feature maps extracted from direct HIs and correlation matrices inputs. By using HSV (Hue, Saturation and Value) space, the first step is to decrease various artifacts during HI recording. After designing the ensemble model, the suggested structure has been evaluated and validated using the datasets CRC-5000 and NCT-CRC-HE-100K. On the CRC data set, the ensemble model had accuracy of 98.71% and 99.13%. Deep ensemble learning performed better than current methods in terms of computational and temporal performance, according to the results. Our proposed method is generalizable and has been tested on many of unseen HIs. Based on their data, pathologists can classify unseen Whole Slide Images (WSIs) images using only their lesion classes. The proposed tissue analysis mechanism reliably predicts CRC when pathological images are accurate.
An Abstract Category of Dynamical Systems

A notion of time is fundamental in the study of dynamical systems. Time arises as a standalone dynamical system and also in solutions or trajectories as a special kind of map between systems. We characterize time by a universal property and use universality to motivate an abstract definition for categories of dynamical systems. We propose this definition as guidance in concrete instantiations for other kinds of systems.
Multilingual Fine-Grained Named Entity Recognition

The “MultiCoNER II Multilingual Complex Named Entity Recognition” task1 within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs, books, movies), people with different titles (e.g., politicians, scientists, artists, athletes), different categories of products (e.g., food, drinks, clothing), and so on, in several languages. In the context of SemEval, our team, FII_Better, presented an exploration of a base transformer model’s capabilities regarding the task, focused more specifically on five languages (English, Spanish, Swedish, German, and Italian). We took DistilBERT (a distilled version of BERT) and BERT (Bidirectional Encoder Representations from Transformers) as two examples of basic transformer models, using DistilBERT as a baseline and BERT as the platform to create an improved model. In this process, we managed to get fair results in the chosen languages. We have managed to get moderate results in the English track (we ranked 17th out of 34), while our results in the other tracks could be further improved in the future (overall third to last).
On some classes of formulas in S5 which are pre-complete relative to existential expressibility

Existential expressibility for all k-valued functions was proposed by A. V. Kuznetsov and later was investigated in more details by S. S. Marchenkov. In the present paper, we consider existential expressibility in the case of formulas defined by a logical calculus and find out some conditions for a system of formulas to be closed relative to existential expressibility. As a consequence, it has been established some pre-complete as to existential expressibility classes of formulas in some finite extensions of the paraconsistent modal logic S5.
Increasing the Efficacy of Umbilical Cord Blood Banking Using Machine Learning Algorithms: A Case Study from Royan Cord Blood Bank

Cord blood is the blood that obtains after the birth of a baby. Cord blood is rich in stem cells, which are used to treat a variety of diseases, including cancers and immune disorders. These treatments' effectiveness depends on the quantity of total nucleated cells (TNCs) in cord blood units (CBUs). Both public and private cord blood banks store these CBUs. Public banks rely on government funding for the cost of testing, storing, and maintaining CBUs. In addition, the quantity of TNCs in each CBU remains uncertain until the TNC test is conducted. This study aims to utilize ensemble learning algorithms to aid public banks in identifying and collecting potentially valuable CBUs prior to TNC testing in order to save the cost of TNC testing on CBUs that are not valuable. This study has three main contributions: Firstly, it demonstrates that the XGBoost and LightGBM algorithms can identify CBUs with TNC of more than 0.7×10^9,1×10^9, and 1.5×10^9; Secondly, the study combines the smote_NC method with Xgboost and LightGBM algorithms and evaluates each algorithm in identifying high TNC samples. Lastly, this article considers the effect of the phlebotomist experience on identifying high TNC samples, a variable overlooked in other studies.
Chunker Based Sentiment Analysis and Tense Classification for Nepali Text

The article represents the Sentiment Analysis (SA) and Tense Classification using Skip gram model for the word to vector encoding on Nepali language. The experiment on SA for positive-negative classification is carried out in two ways. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 scores of 0.6779 is observed for positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences. This paper also proposes using a skip-gram model to identify the tenses of Nepali sentences and verbs. In the third experiment, the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP)classification and it is observed that verb chunks had very low overall accuracy of 53%. In the fourth experiment, conducted for Tense Classification using Sentences resulted in improved efficiency with overall accuracy of 89%. Past tenses were identified and classified more accurately than other tenses. Hence, sentence based tense classification is proven to be better than verb Chunker based sentiment analysis.
Film Pedagogy as a Means of Modern Mastering of Mathematics at School

Relevance. The article provides a theoretical analysis of the requirements for an educational film from the point of view of psychology and pedagogy. The analysis of Russian and foreign studies in the field of perception of sound and video, as well as the semantic content of the educational film showed that they research general characteristics and mechanisms of processing for auditory and visual information in creating an image of reality. The main studies consider the focus of attention and semantic and cognitive attitudes. Objective. The research aims to create a video course that allows a student with problems mastering mathematics to overcome the "learned helplessness", to find meaning in studying algebra and geometry, to believe that learning mathematics is easy and interesting. Methods. Content analysis, subject-subject dialogue, assessment and forecast of changes, conclusions from general to particular and from particular to general, comparative analysis. Results. The project "Matstream" considering the main topics of algebra that students of primary school study in a series of video tutorials is presented. Taking into account the cognitive characteristics of modern fifth-ninth grade students is implemented in the presentation of educational material. Three blocks of videos are aimed at forming an attitude to the perception of abstract-logical information expressed in mathematical symbols, specific knowledge, skills and abilities. Conclusion. In creating educational audiovisual products, it is necessary to take into account motivational, emotional, and cognitive components. Within the framework of the Matstream project, a program of assistance in teaching mathematics to schoolchildren is being implemented. It also involved developing attitudes to the perception of abstract-logical language of mathematical algorithms and overcoming difficulties in mastering mathematics.
The Role of Data Security in SIEM Compliance: Meeting Regulatory Requirements and Data Protection Standards

Owing to the soaring trends and changes in policies, businesses are adopting security solutions that guarantee a proactive degree of cybersecurity. Rising concerns over information security have resulted in a corresponding rise in the number of compliance mandates across different industries. So, businesses are required to meet numerous regulatory requirements and conform to several compliance mandates. Thus considering different regulatory requirements, businesses are embracing security solutions like Security Information and Event Management (SIEM) to meet and adhere to state and industry-specific standards. This review explains in detail how SIEM solutions help organizations meet compliance and regulatory requirements.