Reviewerly/exHarmony huggingface dataset:
Penyuluhan Tatalaksana Awal Trauma Gigi pada Anak-anak Guru dan Wali Murid RA Al-Muttaqin

Anterior tooth trauma often occurs in children because children are more active than adults and their coordination and assessment of conditions are not good enough, so they often fall while learning to walk, run, play and exercise. Trauma that occurs to a child's teeth can interfere speech, chewing, aesthetics and the eruption of permanent teeth, thereby disrupting the growth and development of the teeth and jaw. Therefore, it is important to pay attention to the approach regarding the first treatment of trauma to primary teeth for parents and children. The choice of Community Service target at RA Al-Muttaqin was due to a request from the school principal as community leader. Education and training activities were carried out on July 27 2023 for a total of 20 teachers and parents. The aim of this activity is to increase knowledge and motivate changes in attitudes and behavior in maintaining oral health and regarding first care for primary tooth trauma. The initial assessment of knowledge and attitudes regarding primary care for trauma to primary teeth and the mouth of teachers was carried out through a pre-test. Counseling on how to maintain healthy teeth and mouth, including dental and oral health problems, prevention methods, as well as first care for primary tooth trauma by the presenter as well as educational videos. As an evaluation, a post test was carried out. The evaluation of the post test results showed an increase in scores. Recommendations for continuing this training activity in other elementary schools, especially for teachers in the school environment.
An integrated system for breast cancer diagnosis using convolution neural network and attention mechanism

&lt;p&gt;In most malignancies, breast cancer is fatal, accounting for approximately 500,000 annual deaths. The subtype of breast cancer known as Invasive Ductal Carcinoma (IDC) is surprisingly common. Pathologists commonly focus on IDC-containing regions when trying to determine if a patient has breast cancer. Although extremely fatal, survival rates and expected lifespans improve dramatically with prompt diagnosis and treatment. The treatment strategy also varies based on the breast cancer patient’s stage. In this research, we use a classification method for a publically available dataset of breast histopathology images obtained from the Kaggle. The IDC regions of the images in this dataset have been restricted for easy retrieval. The breast cancer IDC data set contains 277,524 records, of which 78,786 are positive. The 277,524 images were classified using an IDC breast cancer dataset, with 78,786 positive IDC and 198,738 negative IDC, respectively. The authors introduce a new architecture of deep convolutional neural networks and attention mechanism for classification. The model achieves state-of-the-art levels of accuracy for IDC identification, setting a new benchmark for future studies.&lt;/p&gt;
Commutative Cryptanalysis Made Practical

About 20 years ago, Wagner showed that most of the (then) known techniques used in the cryptanalysis of block ciphers were particular cases of what he called commutative diagram cryptanalysis. However, to the best of our knowledge, this general framework has not yet been leveraged to find concrete attacks.In this paper, we focus on a particular case of this framework and develop commutative cryptanalysis, whereby an attacker targeting a primitive E constructs affine permutations A and B such that E ○ A = B ○ E with a high probability, possibly for some weak keys. We develop the tools needed for the practical use of this technique: first, we generalize differential uniformity into “A-uniformity” and differential trails into “commutative trails”, and second we investigate the commutative behaviour of S-box layers, matrix multiplications, and key additions.Equipped with these new techniques, we find probability-one distinguishers using only two chosen plaintexts for large classes of weak keys in both a modified Midori and in Scream. For the same weak keys, we deduce high probability truncated differentials that can cover an arbitrary number of rounds, but which do not correspond to any high probability differential trails. Similarly, we show the existence of a trade-off in our variant of Midori whereby the probability of the commutative trail can be decreased in order to increase the weak key density. We also show some statistical patterns in the AES super S-box that have a much higher probability than the best differentials, and which hold for a class of weak keys of density about 2−4.5.
VU Amsterdam supports ResearchEquals

VU Amsterdam is now a supporting member of ResearchEquals, sustaining and governing the development of modular publishing. VU Amsterdam joins as the third institutional supporter, alongside Tilburg University and KU Leuven. VU Amsterdam supports new research initiatives through their VU Open Access Innovation Fund. They support open, free and fair publishing alternatives set up by and for researchers.
Forward Secrecy Attack on Privacy Preserving Machine Authenticated Key Agreement for Internet of Things

Internet of things (IoT) is to connect billions of devices and machines via Internet and to have a smart system. Sensors and devices in IoT environment are connected and communicated together. Connecting such a huge number of devices requires high level of security and privacy. A crucial characteristic of ubiquitous IoT devices is their limited resources. In recent times, a scheme for privacy-preserving machine authenticated key agreement scheme (PPMAKA) has been introduced for the IoT environment. It was argued that PPMAKA provides security and privacy at the same time including forward secrecy. Nevertheless, this paper will demonstrate that PPMAKA lacks forward secrecy, a crucial security and privacy feature in the IoT environment. We use Cannetti and Krawzyck threat model for the detailed analysis of PPMAKA. Furthermore, we provide remarks for the future research as it is recommendable to design any security and privacy schemes over IoT environments with lightweight operation and communication property, authenticated key agreement with forward secrecy, anonymity and unlinkability.
University students´ emotions in forced remote education.

In 2020, due to the COVID-19 pandemic, universities had to suddenly and forcefully implement remote education. The study had three objectives: to explore and compare university students´ emotions; to analyze the associations between emotions and socio-educational factors; and to understand, from the student's perspective, emotions and experiences lived during remote education. The study was exploratory and comparative. The analyses were quantitative and qualitative. Five hundred and eighty students from the University of Extremadura (Spain) and the University of Río Cuarto (Argentina) participated. Data collection was carried out between May and September 2020. Two data collection instruments were used: self-administered online questionnaire and focus groups. The results indicated concern about connectivity and lack of technological resources. The predominance of negative emotions and communication difficulties was observed in both quantitative and qualitative data. However, we also found flexible and adaptive practices that had allowed students to cope with communication difficulties, and unfavorable emotional states.
An Overseas Sentiment Analysis of Xi’an City Based on Deep Learning and NOW Corpus

With the development of corpus linguistic and the inter-disciplinary application of deep learning, the quantitative research on the cross-cultural communication of city image is becoming more and more popular recently. The purpose of this study is to reveal the current communicative situation of Xi’an City on English online news medias and overseas audiences. This paper uses the real-time English online news Corpus NOW and public opinion texts drawn from Twitter as the research data. Firstly, this study analyzes the text data from the perspective of Word Frequency, Keywords, Key Word In Context (KWIC) and Collocation. Then sentiment analysis based on a pre-trained Natural Language Processing (NLP) model Bidirectional Encoder Representations from Transformers (BERT) is used to analyze overseas audiences’ attitude toward Xi’an City. The results find that Xi’an City is less reported by overseas online new medias than other 14 new first-tier cities in China. Southeast and Central Asian countries are the main reporters of Xi’an City. International society mainly concentrates on Xi’an City’s national defense industry (especially military aircraft), followed by its long history and economy. Besides, 46.2% of overseas audiences keeps neutral toward Xi’an, 30.4% have a negative attitude, and another 23.4% have a positive attitude. According to the research result, this study proposes three practical suggestions to help enhance Xi’an City’s international influence effectively.
Osservazioni sulle modalità di traduzione in romeno delle coppie correlative chi... chi del romanzo I promessi sposi di Alessandro Manzoni

This article presents and discusses the translation of the correlative pairs chi… chi of Alessandro Manzoni’s I promessi sposi, in the Romanian version Logodnicii by Alexandru Balaci. We notice that the translator Alexandru Balaci (1916-2002), a refined specialist of Italian language and culture, uses a wide range of translation versions, sometimes even deciding not to translate the correlative pair chi… chi, but to rephrase the entire utterance.
An Empirical Study on the Application of Moving Averages on Charts with Special Reference to Candlesticks and Heikin-Ashi

The main aim of this paper is to apply the moving averages to know the possibility of gaining better returns in candlesticks or Heikin-Ashi charts. For the purpose of this, top five sectoral indices of Nifty are selected on the basis of market capitalization. Daily closing prices of select sectoral indices is collected for the period of five years starting from 01/12/2017 to 30/11/2022. 10 Days Simple Moving Average (DSMA) and 50 Days Simple Moving Average (DSMA) are selected for tracking the short-term trend and 100 Days Simple Moving Average (DSMA) and 200 Days Simple Moving Average (DSMA) are selected for tracking the long-term trend. The study has found that, it is possible to gain better returns in Heikinashi than compared to Candlesticks.
СОЦИАЛЕН СТАТУТ НА УЧИТЕЛИТЕ ОТ РАЗГРАДСКАТА КАЗА ПРЕЗ ХІХ ВЕК

The teachers in Razgrad area had a special place in the Renaissance society. The agreements of the teachers residing in Razgrad from the 1860s and 1870s were actually their employment contracts. Through them, the material and household problems and the professional duties of the teachers were settled. The documents show the desire of the residents of Razgrad to create the best possible conditions for teachers to work with adolescents. Many teachers, outside of their official duties, were engaged in active social activities. In addition, there were informal agreements with teachers regarding their professional rights and obligations. The social status of the teachers is indicative of their authority in the Bulgarian Renaissance society.
CSR performance practices and COVID-19 – a qualitative study of SME owners in UAE

Purpose The purpose of this paper is to examine the corporate social responsibility performance among small and medium enterprise (SME) owners in the United Arab Emirates (UAE) before and during COVID-19. This study shows insights into the barriers that could affect the CSR performance practices of UAE SMEs, and related implications are discussed in the study. Design/methodology/approach This study used a longitudinal qualitative research design. The research comprised 30 interviews from 15 SME owners that were studied prior to and during the pandemic. The 15 interview participants are the sole owners of these SMEs and are Emirati citizens. They were identified through personal contacts and referrals. Findings Study findings show that there was a clear shift during the pandemic to place more focus on employees. CSR performance practices are dominated by strategies for caring for the environment and employees. During the pandemic, the priority was paying employees at least a portion of their salary despite financial hardships and ensuring their health and well-being. Prior to the crisis, the key barrier to the implementation of CSR performance practices was a lack of knowledge. During the pandemic, barriers identified included uncertainty and financial constraints. Furthermore, all the owners believe that big corporations should be more socially responsible and SMEs should be the recipients of CSR practices. Lastly, the owners expressed their belief in the values of transparency, integrity, commitment, efficiency and responsibility. Originality/value This study is a novel attempt to gain an in-depth understanding of CSR among SMEs in the UAE in the context of a pandemic. It looks specifically at the performance practices pursued by SME owners in the UAE before and during the COVID-19 crisis.
“Let’s Go,” A Mobile Learning Application for Social Studies Subjects: Prototype of Application Development

The value of social studies learning for junior high school students in several schools still needs to be higher. One of the factors of low student scores is the need for more variety in learning methods used by teachers, low student motivation, and the use of learning media that could be more optimal. One way to do this is to develop a learning model with engaging learning media. This research aims to develop mobile understanding by integrating artificial intelligence at the evaluation stage. The methodology used in this research is the waterfall approach method with requirements analysis, design, implementation, testing, operation, and maintenance. The mobile learning prototype was tested on media experts and junior high school student respondents. The dimensions of expert assessment are reviewed from media appearance, quality and use of media, navigation, product strength, media interface, product attractiveness, and material delivery in the media. The dimensions of questions for students consist of 4 points: content, interface, feedback and assessment, and personalization. The research results show a mobile application’s results, including a brain gym menu, problem identification, materials, discussion forums, assignment results, and quizzes. The menus presented make it easier for students to learn. The questionnaire results from experts show that the highest average is 5.00, with a standard deviation of 0.00. The questionnaire results from student respondents had the highest average of 49.714, with a standard deviation of 0.169. The results of the scores obtained in the expert and student respondent questionnaires show that mobile learning is acceptable.
Porting a Native Android App to iOS

The two mobile operating systems Android and i(Pad)OS have dominated the smartphone and tablet market for years and app providers have to offer their apps for both systems in most cases in order to be competitive or to be able to reach the majority of potential customers. In native app development, separate applications have to be written and maintained for each platform. Often, apps are developed for one platform first and the second app is developed at a later stage, after some feedback could be collected. This porting from one system to the other can be either (partially) automated or manual, but in any case, it has its challenges. Both systems were designed with different approaches and differ greatly in some parts from each other – not only visually, but also in terms of the underlying structure. To illustrate the porting process, the Android app “Schoolstart Screening App”, which was developed for the Federal Ministry of Education, Science and Research of Austria by Graz University of Technology at the OU Educational Technology, was ported so that it can be used also on iPads. Automated approaches were discussed and the chosen process is explained to get a good overview of the topic.
Community based Residential Firefighting Strategy: A Case Study of Malang City

Fires that occur in residential areas have a negative impact on the community, from both social and economic perspectives. In urban areas, where the high density of buildings and people is evenly distributed, the chances of loss of life and property are considerably high. Those changes are even higher in residential areas or villages located far from fire brigade access. The occupants should not wait for the assistance of the city fire brigade but should act to terminate the flame themselves to save their properties and lives. This study aimed to determine the awareness of fire safety measures in residential areas and fire safety behavior and the capability of residents to independently extinguish fires. A total of 122 participants participated in filling out an online questionnaire to find answers to possible strategies for handling fires in residential areas. Statistical analysis was carried out to see behavioral trends and some of the potential in the community to prevent fires in residential areas. This study discovered that the availability of water is mostly preferred for fighting fires in residential areas, that there is a lack of awareness of electrical as the primary source of fires and that a level of disbelief in the society’s ability to fight fires on their own. This study includes both individual and communal self-extinguishing strategies.
AUTOMATED MACHINE LEARNING: EMPOWERING DATA-DRIVEN DECISIONS WITH TPOT AND AUTO-SKLEARN

This chapter explores the fast-developing topic of automated machine learning (AutoML), which aims to democratize and streamline the machine learning process. We examine the idea of AutoML and its importance in lowering the entry barriers for machine learning so that people with different degrees of competence can use it. TPOT (Tree-Based Pipeline Optimization Tool) and Auto-sklearn, two prominent AutoML tools, are thoroughly investigated. While Auto-sklearn makes efficient use of Bayesian optimization, TPOT uses genetic programming to automate the development and optimization of machine learning pipelines. For those wishing to employ these potent tools in their machine learning projects, we also evaluate the advantages and disadvantages of AutoML while offering real-world use cases and automation advice.
FUTURE-ORIENTED NAVIGATION: PATTERNS AND SHIFTS IN NATURAL LANGUAGE PROCESSING (NLP)

Mohd Hyder Gouri; Prof. (Dr.) Pramod Kumar
HARNESSING DEEP LEARNING AND NATURAL LANGUAGE PROCESSING (NLP)

This chapter embarks on a journey through the dynamic landscapes of Deep Learning and Natural Language Processing (NLP) with a focus on the TensorFlow and Keras frameworks, as well as spaCy and NLTK libraries. In this chapter, we demystify the world of deep learning, exploring neural networks, activation functions, and model training. We introduce TensorFlow, a powerful open-source framework, and Keras, a user-friendly high-level API that simplifies deep neural network creation. Case studies illuminate deep learning's real-world impact, spanning image classification to healthcare applications. We conclude by delving into transfer learning and pre-trained models, essential tools for modern deep learning. This chapter also dives into NLP, emphasizing its pivotal role in today's technology landscape. We introduce spaCy and NLTK, two Python libraries that equip you with versatile NLP tools. Sentiment analysis, text classification, and Named Entity Recognition are highlighted, showcasing their applications across various domains. By the end, you'll have a robust foundation in both deep learning and NLP, equipped to tackle real-world challenges and opportunities in these cutting-edge fields.
EXPLORATORY DATA ANALYSIS USING PYTHON

Data scientists and analysts can analyze, display, and get important insights from their datasets through exploratory data analysis (EDA), a critical phase in the data analysis process. With its extensive data manipulation and visualization module ecosystem, Python has become a potent tool in this situation. This chapter provides a thorough introduction of Python-based EDA techniques, highlighting the value of EDA in the pipeline for data analysis and presenting different approaches to data visualization, summary statistics, and statistical testing.
Common Ground: The Importance of Standards and Recommended Practices

Standards are used throughout the e-resource lifecycle by all information professionals, whether vendor, publisher, or librarian. They contribute to the adoption of technical solutions that improve data quality and streamline workflows. Members of the NASIG Standards Committee showcased the following three standards that are key for managing e-resources across their lifecycle: Electronic Data Interchange (EDI), Knowledge Bases and Related Tools (KBART), and Counting Online Usage of Networked Electronic Resources (COUNTER).
SENTIMENT ANALYSIS AND NLP MODELS AS THE LANGUAGE OF DATA

NLP models and sentiment analysis are two core facets of natural language processing (NLP) that are examined in this chapter. It starts by exploring the several approaches used to create NLP models, such as semi-supervised learning, supervised learning, unsupervised learning, and the crucial function of deep learning. It then delves into the field of sentiment analysis, illuminating its importance, variety of approaches, and practical uses. These subjects taken together offer a thorough overview of the rapidly changing field of NLP and its wide variety of applications.
EVALUATING BLOCKCHAIN ADOPTION IN INDIAN E-COMMERCE SUPPLY CHAINS: A CASE STUDY ANALYSIS

This study focuses at the significant effect of blockchain technological innovation on web-based business in India with a unique spotlight on supply management network. The report sheds light on an examination of the current literatures for the research topic, arguing about both the possible benefits and impediments related with blockchain innovation. The exploration technique utilized in this study incorporates a mix of online reviews and meetings with directors and business specialists. With this review, we aim to acquire knowledge into the acumens and temperaments of Indian retail organizations towards blockchain innovation. The consequences of our examination show that a few respondents have a good understanding of blockchain innovation and know about its expected advantages in further developing security, guaranteeing information respectability and diminishing extortion. In any case, there are other people who have restricted mindfulness or more prominent interest in embracing the innovation. Taken together, the outcomes recommend that further exploration is expected to work with the broad adoption of blockchain innovation for supply network deriving the Indian online business in retail space. Likewise, obviously there are as yet exceptional issues in the implementation of blockchain innovation in e-commerce sector.
PROTECTING DIGITAL FORTRESSES: CYBER SECURITY AND DIGITAL PRIVACY IN THE 21ST CENTURY

The dynamic panorama of cyber dangers is examined in Chapter 7, "Protecting Digital Fortresses: Cyber Security and Digital Privacy in the 21st Century," along with the measures used to protect our digital life. The chapter explores the emergence of cyber dangers and changing attack vectors while navigating key defense strategies like cyber hygiene, encryption, and AI-driven security. It explores the conundrum of digital privacy in the era of big data, the Internet of Things, and social media while illuminating governing policies and cutting-edge innovations like quantum-safe cryptography. The chapter ends by imagining the future and highlighting ethical issues and global cooperation as the cornerstones in defending our digital fortresses against the constantly changing danger scenario.