Reviewerly/exHarmony huggingface dataset:
NAISC-L

A fascinating feature on Linked Data (LD) for libraries, archives and other cultural heritage institutions. Lucy McKenna is in the final year of her PhD in the ADAPT Centre, Trinity College Dublin. The Centre, funded by Science Foundation Ireland, ‘focuses on developing next generation digital technologies that transform how people communicate by helping to analyse, personalise and deliver digital data more effectively for businesses and individuals. ADAPT researchers are based in four leading universities: Trinity College Dublin, Dublin City University, University College Dublin and Dublin Institute of Technology. ADAPT's transformative tools allow you explore video, text, speech and image data in a natural way across languages and devices, helping companies unlock opportunities that exist within digital content to re-imagine how to connect people, process and data to realise new economic value.’ Lucy invites library and information professionals to evaluate an LD interlinking framework that she has developed as part of her research. She is also looking for information professionals who would like to trial the framework locally to create interlinks from their organisation’s LD datasets.
Intermediate grade school learners’ sex, word recognition, and reading comprehension

Unquestionable are the immeasurable benefits of reading to an individual's mental growth. Consequently, scholars have long established the importance of developing reading skills at the elementary level. Although reading is a recurrent research subject, the prevalence of reading difficulties among learners remains. This necessitates the use of periodic assessments of learners' reading proficiency to serve as the basis for remediation programs. In this study, the researchers used the descriptive correlational design to measure the reading level of pupils' using the Philippine Informal Reading Inventory (Phil-IRI), focusing on word recognition and reading comprehension. Data were gathered from 800 sampled public school grade school intermediate level pupils (431 males and 369 females) enrolled in the School Year 2016-2017 in two Philippine provinces; specifically: Pangasinan and La Union. Inferential statistics were used to show how sex and geographic location could be determiners of reading performance and how word recognition can affect reading comprehension. Findings revealed that the majority of the pupils are in the frustration reading level and males are better readers compared to females. Findings further showed that pupils from La Union have better reading Proficiency than those from Pangasinan. Though word recognition can be attributed to reading comprehension, it is not a single factor affecting reading comprehension, implying that other strong factors may influence pupils' reading comprehension. The researcher strongly recommends schools in the provinces implement intensive reading remediation programs to address students' reading difficulties. Future researchers are likewise encouraged to explore other factors that could be influencing reading comprehension like socio-demographic factors and biological factors.
Comparative Analysis of Image Classification Algorithms

Abstract: Image classification is a hot research topic in today's society and an important direction in the field of image processing research. Image classification is a supervised learning method used to classify images. Paper analyses four common image classification algorithms: convolution neural network, support vector machine, artificial neural network and logistic regression. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of these four algorithms when tested on same dataset, in same environment and on same system. Research paper proves that results obtained from theoretical analysis are same as results obtained from experiments. The study found out that best results were given by convolution neural network, followed by support vector machine, artificial neural network and at last logistic regression.
PERANCANGAN DAN UJI KINERJA SPRINKLER SEDERHANA

AbstractSprinkler irrigation is a way of watering plants by spraying water into the air andfalling on the ground to resemble rain. Sprinklers are generally applied to commodities withhigh economic value, so sprinkler prices are also quite expensive. Sprinklers currentlycirculating in the market are sold in various models, some can rotate 180 to 360 degrees withdifferent beam distances. The purpose of this research is to design and manufacture a simplesprinkler prototype and examine the performance of the sprinkler prototype that is made. Themethod used is the experimental method and the descriptive analysis method, which focuseson performance testing using the prototype sprinkler that is made. This study used severalcomponents of the equipment such as 6 sprinklers , water pumps and piping networks in aland area of 18 m × 12 m. The variables observed in the study included: water discharge,pump rotation (rpm), uniformity coefficient (CU), jet distance, use of pump fuel. The resultsof this study indicate that the application of sprinkler irrigation water uses 6 sprinklerprototypes that were made and a Korobe WR 20X water pump, with a time of 2 minutes, 6sprinklers can release 154.12 L/minute of water with an average pump speed of 3,758. 33RPM and spent as much as 45 ml/minute of fuel. The farthest distance of the sprinkler beamis 3.5 meters.Keywords: design, performance test, sprinkler, radiance, water discharge
Security challenges in cloud computing: A comprehensive analysis

The security issues surrounding cloud computing, a quickly developing technology that is now essential to both personal and business computing, are thoroughly examined in this study. Cloud computing presents serious security risks that require careful attention, despite its many advantages such as scalability, cost-effectiveness, and flexibility. In-depth discussions of a number of important security topics are covered in this paper, including network security, access control, data breaches, legal and regulatory framework compliance, and new threats and vulnerabilities. The paper illuminates the complexities of data and application security in the cloud environment by thoroughly examining these subjects. The study also looks at actual case studies to show how security breaches in cloud computing affect things and how to fix them. The study intends to offer insightful analyses of security challenges with practical implications by examining these cases and providing lessons learned from incidents that have happened in various cloud computing scenarios. Our comprehension of the complex nature of security threats and the tactics used to counter and mitigate them is improved by looking closely at these cases. This study looks into the potential benefits of cloud security enhancements from emerging technologies like artificial intelligence and machine learning, in addition to identifying existing challenges. The research investigates the potential benefits of these technologies in terms of automated security responses, adaptive access controls, and proactive threat detection. Cloud environments may be able to improve their security posture against new and sophisticated threats by utilizing the powers of AI and machine learning. Furthermore, by projecting future trends and challenges in cloud security, this paper offers a forward-looking view of the changing field. Comprehending these possible obstacles is crucial to creating proactive and flexible security approaches that can successfully tackle the ever-changing landscape of cloud computing. This paper's ultimate objective is to offer insightful information about practical approaches and industry best practices for safeguarding cloud environments. Organizations can navigate the complexity of the cloud landscape while protecting their data and applications from the ever-evolving threat landscape by striking a balance between the enormous potential of cloud computing and the necessity of maintaining strong security measures.
The Role of Age in Second Language Development

This article delves into the ongoing debate surrounding the role of age in second language acquisition, examining developments over the past three decades and highlighting contentious issues. We argue that the commonly held belief in age’s pivotal role is frequently contradicted by empirical evidence. Additionally, we examine the age-related debate with respect to foreign language teaching. A brief review is provided on advances in neuroscientific techniques, which show that the adult brain retains substantial plasticity for language learning, followed by a discussion of these findings in relation to lifelong language learning. Finally, we turn to Complex Dynamic Systems Theory, which offers a dynamic perspective that emphasizes the evolving interplay of age, context, and individual factors, pointing to the need for more qualitative research.
Application of Machine Learning techniques to improve detection, diagnosis &amp; prediction of breast cancer: A Comparative Analysis

There is an increasing cancer burden in India across ages and sexes. The most significant cancer incident rate in females is ‘Breast Cancer’. Early detection and treatment are the key to lower mortality rate and better survival rates for cancer patients in the country. This review paper provides an understanding of the various types of breast cancers, their symptoms, causes, current detection and diagnosis methods. The paper presents different Machine Learning (ML) techniques that are in development for the detection and diagnosis of breast cancer. The objective of the paper is to highlight outcomes of some select previous studies between 2016 to 2020 using various ML techniques and summarize the selected algorithms which can be used for breast cancer prediction and diagnosis. In our paper, we have also made an attempt to implement the Convolutional Neural Networks (CNN) model on the Breast Cancer Wisconsin (Diagnostic) dataset, whose results are presented and discussed.
Numerical method for clustering climate data

Описан разработанный метод кластеризации данных, пригодный для анализа и систематизации климатической информации, представленной массивами средних численных значений климатических характеристик. Описан алгоритм применения теории аналитического сигнала для подготовки исходных данных. В качестве критериев кластеризации использованы амплитуды и фазы исходных сигналов. С применением предложенного метода кластеризации получены климатические классы для Байкальской природной территории для разных временных интервалов. В качестве исходной информации использовались массивы данных о приземной температуре и атмосферном давлении. Summary. The purpose of this research is to identify climate classes for the Baikal natural territory during various time intervals. Methods. The study uses climatic characteristics such as surface temperature and atmospheric pressure. A mathematical model of the climate signal was constructed in the form of a quasi-periodic oscillation caused by the global annual cycle of the Earth’s rotation, modulated in amplitude and phase. The carrier oscillation has a period equal to one calendar year. An essential task is to establish patterns of changes in the Earth’s climate system based on the parameters of the amplitudes and phases of climate characteristics. For this purpose, a dynamic clustering algorithm was employed. Experiment. An array of climate data was generated, which included the following meteorological characteristics: temperature and pressure, measured at weather stations located in the Baikal Natural Territory (BNT). The purpose of the experiment is to construct a characteristic for classes of BPTs and assess their variability with regard of the amount of spatial structure over time using the developed algorithm. Results. The presented results have shown that the composition and structure of climate clusters is changed during various time intervals. On average, for all data and periods, it was possible to determine two conditional places of stable formation of clusters. Conclusions. The advantage of the proposed method is that it allows, based on the determination of typical patterns, identifying unique clusters without any a priori information about the number or other parameters of clusters. The resulting climate classifications can be used to assess changes in regional climate at various time and spatial scales.
Impact of Staff Engagement on the Safety and Quality Standards of Health Services Organizations in Erbil: An analytical study

The health-care sector leadership views staff engagement as a critical issue affecting the safety and quality standards provided by health service organisations. Various studies emphasize a strong association between staff engagement and the performance of the healthcare sector in the working environment. The aim of the research was to evaluate the relationship between staff engagement and the performance of the healthcare sector. By using the quantitative method, a total of 106 questionnaires were collected from the staff who work for private hospitals. The findings revealed that there is significant association between staff engagement and the performance of the healthcare sector.
Annotated Translation from Indonesian into English: The Tourism and Culture Office’s Website of Bekasi

This research aims to analyze the annotated translation from indonesian into english: the culture and tourism office’s website of bekasi. The data will be analyzed by the theory of Chesterman about translation strategy. This research is qualitative research with descriptive analysis. The writer describes the translation by giving some annotations (notes) on the selected equivalents in the target language which are the solution of translation problem found out in the source language. The data of the research is in some articles on website of Tourism and Culture Office of Bekasi City. The research finding shows that the writer used syntactic strategy for 55%, about 33% by pragmatic strategy, and 12% by semantic strategy. The finding of this research can be concluded that translation theories are still needed or even they are very important in the process of translation to produce a good translation.
REVIEW OF CYBERSECURITY STRATEGIES IN PROTECTING NATIONAL INFRASTRUCTURE: PERSPECTIVES FROM THE USA

This review analyses cybersecurity methods in the USA, focusing on protecting national infrastructure, with lessons and implications for global cybersecurity practices. In an era dominated by digital interconnectedness, the protection of national infrastructure from cyber threats is of paramount importance. This paper provides a comprehensive review of cybersecurity strategies employed by the United States to safeguard critical national infrastructure. As the nation's reliance on technology continues to grow, so does the complexity and sophistication of cyber threats. The paper examines key frameworks, policies, and initiatives implemented by the USA to fortify its critical infrastructure against cyber-attacks. It outlines the current threat landscape, the evolving nature of cyber threats and the potential consequences of successful attacks on critical sectors such as energy, transportation, and finance. It also analyzes the strategic approaches adopted by the USA, including the National Infrastructure Protection Plan (NIPP), the National Institute of Standards and Technology (NIST) Cybersecurity Framework, and collaborative efforts between the public and private sectors. The paper explores the role of government agencies, regulatory bodies, and private enterprises in implementing and enforcing cybersecurity measures. It assesses the effectiveness of information sharing initiatives, public-private partnerships, and international collaborations in enhancing the resilience of national infrastructure against cyber threats. Furthermore, the review addresses emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) in the context of cybersecurity. It evaluates how these technologies are leveraged to detect, prevent, and respond to cyber threats in real-time, considering both their benefits and potential risks. Ethical considerations, privacy concerns, and challenges associated with the implementation of robust cybersecurity strategies are also discussed. The paper concludes with insights into the evolving nature of cyber threats and recommendations for continuous improvement in national cybersecurity strategies, emphasizing the need for agility, adaptability, and collaboration to stay ahead of an ever-changing threat landscape. This study contributes to the broader understanding of cybersecurity practices, offering insights that are not only relevant to the USA but also applicable to other nations facing similar challenges in safeguarding their critical infrastructure in an increasingly interconnected and digital world.&#x0D; Keywords: Cybersecurity, Strategies, Protecting, National Infrastructure, USA, NIST, Artificial Intelligence.
CYBERSECURITY RISK ASSESSMENT IN BANKING: METHODOLOGIES AND BEST PRACTICES

Cybersecurity risk assessment in banking is the process of identifying, analyzing, and evaluating the cyber threats and vulnerabilities that may affect the confidentiality, integrity, and availability of the information systems and data of banks and their customers. Cybersecurity risk assessment in banking helps banks to prioritize and implement appropriate controls and measures to mitigate the cyber risks and to comply with the relevant regulations and standards. This study focusses on identifying effective risk assessment strategies, highlighting how they can be adapted and applied in various banking environments, especially in developing economies like Nigeria. As the banking industry continues to evolve in the digital era, the significance of robust cybersecurity measures cannot be overstated. This paper delves into the critical domain of Cybersecurity Risk Assessment in Banking, exploring various methodologies and best practices employed to safeguard financial institutions against evolving cyber threats. The dynamic landscape of cyber risks faced by banks, ranging from sophisticated malware and phishing attacks to insider threats and system vulnerabilities are examined. The paper provides an in-depth analysis of established and emerging methodologies for conducting effective cybersecurity risk assessments in the banking sector. It explores quantitative and qualitative risk assessment approaches, threat modeling, and scenario analysis, shedding light on their respective strengths and limitations. Moreover, the document highlights the importance of aligning risk assessment methodologies with industry regulations and compliance standards to ensure a comprehensive and regulatory-compliant cybersecurity framework. Best practices for cybersecurity risk management in banking are scrutinized, emphasizing the integration of proactive threat intelligence, continuous monitoring, and incident response planning. The role of advanced technologies, including artificial intelligence and machine learning, in enhancing the efficiency of risk assessment processes is also discussed. Furthermore, the paper addresses the human element in cybersecurity, emphasizing the significance of training and awareness programs to mitigate risks associated with human error and social engineering attacks. By synthesizing insights from industry practices, regulatory guidelines, and technological advancements, this paper offers a comprehensive guide for banking professionals, cybersecurity practitioners, and policymakers involved in fortifying the resilience of financial institutions against cyber threats. Ultimately, the research aims to contribute to the ongoing discourse on cybersecurity risk assessment in banking, providing actionable insights to navigate the complex landscape of digital risks and ensuring the continued trust and security of the financial ecosystem.&#x0D; Keywords: Cybersecurity; Risk Assessment; Banking; Methodologies; Cyber Threat; Artificial Intelligence; Best Practices
An autoethnographic study: Self-growth approach for teacher educators in training tech-effective teachers

I hold the view that teachers have a significant impact on students’ academic performance, and in the current digital educational environment, this impact has become much more significant. Although the technology available to teachers remained largely unchanged since before the pandemic, their struggles with online teaching highlighted the shortcomings of their technology training in teacher education programs. The lack of technological proficiencies among teachers frequently stems from insufficient training in digital skills within teacher education programs, which is often due to the teacher educators’ own deficiencies in digital proficiency. Therefore, I contend that teachers’ development in technology use should start with teacher educators. In this qualitative autoethnographic study, I, as a teacher educator, critically examine how I improve my digital literacy and technological pedagogical skills, utilizing a collection of data sources including reflective accounts, lecture notes, teaching diaries, and student feedback. By sharing this journey, I aim to offer insights that other teacher educators may perceive as beneficial for their own professional development. I argue that adopting a non-formal self-growth approach is a useful way for teacher educators to equip future teachers with the required knowledge and skills for effective technology integration in their future practices.
SAMP: Sub-task Aware Model Pruning with Layer-Wise Channel Balancing for Person Search

The deep convolutional neural network (CNN) has recently become the prevailing framework for person search. Nevertheless, these approaches suffer from the high computational cost, raising the necessity of compressing deep models for applicability on resource-restrained platforms. Despite of the promising performance achieved in boosting efficiency for general vision tasks, current model compression methods are not specifically designed for person search, thus leaving much room for improvement. In this paper, we make the first attempt in investigating model pruning for person search, and propose a novel loss-based channel pruning approach, namely Sub-task Aware Model Pruning with Layer-wise Channel Balancing (SAMP). It firstly develops a Sub-task aware Channel Importance (SaCI) estimation to deal with the inconsistent sub-tasks, i.e. person detection and re-identification, of person search. Subsequently, a Layer-wise Channel Balancing (LCB) mechanism is employed to progressively assign a minimal number of channels to be preserved for each layer, thus avoiding over-pruning. Finally, an Adaptive OIM (AdaOIM) loss is presented for pruning and post-training via dynamically refining the degraded class-wise prototype features by leveraging the ones from the full model. Experiments on CUHK-SYSU and PRW demonstrate the effectiveness of our method, by comparing with the state-of-the-art channel pruning approaches.