Reviewerly/exHarmony huggingface dataset:
Knowledge graphs to an analysis and visualization of texts from scientific articles

Reviewing the literature is one of the key elements of scientific research that allows you to identify existing solutions and research niches. However, it can be difficult for researchers to find relevant scientific articles related to the research topic. A limited number of available sources of information, diverse ways of describing them, and a multitude of scientific publications mean that scientists often have to spend a lot of time and effort to find the information they need. For this reason, we propose a solution for text analysis and its presentation in the form of a graph visualization. In our research, we used Natural Language Processing (NLP) methods and word weighting measures such as TF and TF-IDF. In addition, knowledge graphs were used to present the results visually. The conducted research was based on the analysis of the content of scientific articles, which allowed to draw important conclusions related to the presentation of texts in graphic form. Experimental results also identified potential methods and suggestions for literature reviews in specific fields of science. The analysis of the methods used and the results obtained allowed for a better understanding of the potential of natural language processing and graph text representation in the analysis of scientific articles.
Image Caption Generator for Sinhala Using Deep Learning

In this study, for the image caption generation in the Sinhala language, we have implemented a Recurrent Neural Network based model consisting of an InceptionV3 model as an image feature extraction model and a Long Short Term Memory network for the language model by referring to the literature. The different variations of Sinhala versions of the Flickr8K and MS COCO datasets have been constructed and used to train experimental models. Evaluation of the generated captions has been done using both automated and manual approaches. The model trained on the MS COCO dataset with Google translated Sinhala captions has achieved the highest BLEU score of 0.592 and the highest METEOR score of 0.281. After doing the manual caption analysis, it was observed that there could be generated captions which could provide a good idea to the reader while having lower BLEU and METEOR scores.
Enhancing Students' Proficiency in English Grammar Through the Implementation of Snowball Throwing Pedagogical Model

This research endeavors to assess students' proficiency in English grammar pertaining to the past tense through a comparative analysis of their pre-test and post-test scores. Employing the innovative snowball throwing learning model as the intervention strategy, this study aims to augment students' grammatical competence in the past tense. The research methodology adopts a classroom action research approach conducted across two cycles. The subjects comprise Grade X students in the secretary class of the first state Vocational school in Manokwari, selected through purposive sampling from four classes. Research instruments include a reading test, observation sheet, and lesson plan. The initial data analysis of the pre-test yields a result of 29.2, indicating a classification in the poor category. However, following the implementation of the snowball throwing model through two cycles (treatment one and treatment two), noteworthy improvement is observed. Treatment one yields a result of 60.3 in the fair category, while treatment two results in an average score of 80.2, classifying as "Good." Importantly, this score serves as the post-test score. The findings underscore the efficacy of the snowball-throwing model in enhancing students' grasp of past tense grammar
The Evolution of Access Control in Cloud Security: A Survey of Key Literature

Cloud Computing is a distributed model that enables users to access, meet and exchange resources and resource requirements hosted by various service providers. Authentication of requesting users and the permitting of their rights of access are extremely necessary in order to avoid unauthorised or unlawful access to cloud services. In the cloud scenario, access control of distributed resources is most critical. Access control is a basic aspect of the security of information that is directly linked to the fundamental features of confidentiality, honesty and availability. This article provides a literature analysis of the Cloud Computing Access Control Mechanism.
Deep Logo Authenticity: Leveraging R-CNN for Counterfeit Logo Detection in E-commerce

In the rapidly evolving realm of electronic commerce, ensuring the accuracy and authenticity of merchandise assumes paramount importance in maintaining consumer trust and platform reliability. One of the prominent challenges encountered within this particular domain revolves around the pervasive prevalence of counterfeit products, often discernible through subtle deviations in brand insignias. This research paper introduces a novel approach to detect counterfeit logos on electronic commerce platforms using Region-based Convolutional Neural Networks (R-CNN). Traditional approaches often rely on manual verification or basic image comparisons, both of which have drawbacks in terms of scalability and consistent accuracy. The methodology utilized in our research capitalizes on the capabilities of deep learning algorithms to precisely identify and classify logos depicted in product images, proficiently distinguishing genuine logos from counterfeit ones with a significant degree of precision. A meticulously curated dataset was compiled, encompassing genuine and counterfeit logos sourced from renowned brands. By means of intensive training, our model demonstrated remarkable aptitude, surpassing the capabilities of contemporary methodologies. The current investigation not only offers a significant contribution to enhancing the security and reliability of electronic commerce platforms, but also establishes the foundation for the advancement of advanced counterfeit detection methodologies within the domain of digital marketplaces.
Feature Extraction Methods by Various Concepts using SOM

Image retrieval systems gained traction with the increased use of visual and media data. It is critical to understand and manage big data, lot of analysis done in image retrieval applications. Given the considerable difficulty involved in handling big data using a traditional approach, there is a demand for its efficient management, particularly regarding accuracy and robustness. To solve these issues, we employ content-based image retrieval (CBIR) methods within both supervised , unsupervised pictures. Self-Organizing Maps (SOM), a competitive unsupervised learning aggregation technique, are applied in our innovative multilevel fusion methodology to extract features that are categorised. The proposed methodology beat state-of-the-art algorithms with 90.3% precision, approximate retrieval precision (ARP) of 0.91, and approximate retrieval recall (ARR) of 0.82 when tested on several benchmark datasets.
Fractal interpolation-based micro-surface reconstruction method for composite pore walls

The pore wall morphology of carbon fiber reinforced composite (CFRP) laminates is one of the key factors to determine their service performance. However, due to the direction angle of the material fibers, the original micro surface morphology of the pore wall is complex and difficult to characterize. In this paper, a method to reconstruct the micro surface of the hole wall by fractal interpolation is proposed. By using a small number of data points extracted, the high-precision equivalent micro surface by fractal interpolation can be reconstructed, which can effectively characterize the composite joint surface. Firstly, the geometric features of micro surface are extracted by using the experimental measurement. Then, based on the model for fractal interpolation, an improved self affine fractal interpolation algorithm is used to reconstruct the original measured surface. Finally, the geometric and mechanical validity of fractal interpolation surface reconstruction is verified by combining geometric model and finite element model. The results show that when the sampling interval is 3~5, the geometric error and mechanical property error of the fractal interpolation method are below 10%, which can effectively characterize the original composite joint surface.
Supporting change management of UML class diagrams

Class diagram is an important model in object-oriented software development. A class diagram specifies the structure of a system with the system's classes, classes' attributes and methods, and relations among classes. Sometimes class diagrams should be modified in accordance with requirements changes or correction of errors. In such a modification, careless mistakes may lead to include another error in the diagram. In this paper, we propose a method of visualization of the difference between original class diagram and modified one by emphasizing the difference in order to avoid newly produced errors. We illustrate our method with examples.
UDP-YOLO: High Efficiency and Real-Time Performance of Autonomous Driving Technology

In recent years, autonomous driving technology has gradually appeared in our field of vision. It senses the surrounding environment by using radar, laser, ultrasound, GPS, computer vision and other technologies, and then identifies obstacles and various signboards, and plans a suitable path to control the driving of vehicles. However, some problems occur when this technology is applied in foggy environment, such as the low probability of recognizing objects, or the fact that some objects cannot be recognized because the fog's fuzzy degree makes the planned path wrong. In view of this defect, and considering that automatic driving technology needs to respond quickly to objects when driving, this paper extends the prior defogging algorithm of dark channel, and proposes UDP-YOLO network to apply it to automatic driving technology. This paper is mainly divided into two parts: 1. Image processing: firstly, the data set is discriminated whether there is fog or not, then the fogged data set is defogged by defogging algorithm, and finally, the defogged data set is subjected to adaptive brightness enhancement; 2. Target detection: UDP-YOLO network proposed in this paper is used to detect the defogged data set. Through the observation results, it is found that the performance of the model proposed in this paper has been greatly improved while balancing the speed.
Ancient Character Recognition: A Comprehensive Review

Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic.
A biobanking system for diagnostic images: architecture, COVID-19 related use cases and performance evaluation (Preprint)

Background Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes. Objective The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform. Methods Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay. Results Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD &amp; LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist. Conclusions This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.
Language sense classification model based on neural network

The common international language, English, is playing an increasingly important role in various fields with the rapid development of artificial intelligence in recent years. Artificial intelligence can improve students' English abilities as an additional teaching tool. Therefore, this study seeks the English language sense between different types of sentences based on Long Short-Term Memory (LSTM) and BERT model analysis sentences and generates a model to distinguish the types. This paper adapts the LSTM model and BERT model: first, this paper crawls the sentences from British Broadcasting Corporation (BBC) documentaries, podcasts, and YouTube and then constructs a data filter to remove the sentences with low quality and short. This paper analyzes the data set through the BERT module and LSTM model. this paper then compares the differences between different sentences in a large-scale corpus to generate a language model without long-term dependence. A model is expected to be generated after corpus analysis, and the model can be used to analyze new input statements and give their types. This study can help English learners improve their sense of the English language and the types of sentences they need to say in the face of different situations.
UTILIZATION OF VISUAL AND REALIA INSTRUCTIONAL MATERIALS IN ENHANCING ACQUISITION OF PRE-READING SKILLS AMONG PRESCHOOL LEARNERS IN NAROK COUNTY, KENYA

Early childhood education, also referred to as preschool education is essential to children’s social, physical, emotional, and cognitive growth. Learning can be made more effective when the teachers have the right instructional materials. The study was guided by the following research questions: how were visual instructional materials utilized in teaching pre-reading skills among preschool children? and how were realia instructional materials utilized in the teaching of pre-reading skills among preschool learners? The study adopted a descriptive survey design and data was collected using questionnaires, interviews, and observation schedules. The findings of the study revealed that visual instructional materials for teaching pre-reading skills were available but not adequate in most of the schools, hence affecting the effective teaching of pre-reading skills among the learners. The study further established that most preschools lacked adequate realia materials for teaching pre-reading skills. It was concluded that Preschool teachers should attend relevant workshops, seminars, vocational and teacher professional development courses, to acquire pedagogical skills, besides effective utilization of Visual and Realia instructional materials in teaching pre-reading skills to preschool learners. Emphasis should be on pedagogical content knowledge.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0245/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
METHODOLOGICAL SUPPORT FOR THE DEVELOPMENT OF SOFT SKILLS OF HIGH SCHOOL STUDENTS IN CHEMISTRY LESSONS (IN THE CONTEXT OF THE DIDACTIC SYSTEM OF V.A. SUKHOMLINSKY)

The article deals with modern requirements for the formation of skills that a school graduate should have in the context of the didactic system of V.A. Sukhomlinsky. These requirements are primarily due to the fact that modern life is very dynamic, rapidly changing, striving to simultaneously cover various areas of life. In this regard, school graduates should have well-formed skills that will help them later in adult life and help them achieve high results in the professional field. At the heart of the didactic system of V.A. Sukhomlinsky lies in the reorientation of the school of “learning” into the school of “thinking”. As a result, the child gets the opportunity to feel the joy of learning, learn to learn, and achieve success in learning. In the learning process, over-subject skills are formed, which are described as “Soft skills”. Based on the fact that a set of skills implies all the necessary competencies, soft skills are currently enjoying a high degree of interest.
The Features of Leading a Pupil Music Collective in the Context of Artistic Interaction

The article examines the problem of artistic communication as an important factor for the successful management in the work with the pupil art groups. The author proves: joint music-performance activity and dialogic artistic-empathic communication are the basis of collective artistic interaction. A number of pedagogical principles of leading musical groups in the context of productive relationships are proposed: voluntariness, creative activity, children's artistic creativity, positive interdependence, empathy, using playful forms in work, taking into account the age and individual characteristics of each student, availability materials of music, harmonization of all types of artistic activities.
LEARNING MODEL: MAMA (An Alternative to Improve Learning Outcomes English Finite Students of SMP Negeri 11 Palu City)

Based on the results of pre-observations in class and the results of informal interviews with English teachers at SMP Negeri 11 Palu several months ago, several experiences revealed that teachers do not yet have a clear model of how to synchronize methods and approaches to suit the students’ needs, especially teaching methods and techniques that suit students' needs to overcome the inability to understand and use 'English Finite' appropriately and correctly according to the procedures for using a sentence in English. Students experience difficulty in using 'English Finite' when practicing speaking and writing. Therefore, the aim of this research is to reveal and prove that through the 'MAMA' type cooperative learning model, students can improve their mastery of 'English Finite' in everyday English practice. This research is experimental research to test a 'MAMA' type cooperative learning model. The research sample was 18 students from class VIII A as the control class group and 20 students from class VIII C as the experimental class group, at SMP Negeri 11. Furthermore, treatment or application of the 'MAMA' type cooperative learning model was only given to the experimental group, while the control class underwent the application of conventional learning. Data collection was carried out by giving tests to both groups. Based on the pre-test results, the experimental group was 51.5, while the control group was 50.83. Furthermore, after implementing MAMA type learning, the experimental class was given a posttest and the result was 65.78, while the control class went through conventional learning and the posttest result was 58.23. This indicates that the increase in the 'mean score' of the experimental group was much higher than that of the control group. It can be concluded that by implementing 'MAMA' type cooperative learning can improve students' ability to write precise and correct English Finite in making descriptive texts.
Object Capability Model for Tee: A Cheri Based Compartmentalization Approach

In this paper, we introduce a capability-driven approach to bolster security and granularity within Trusted Execution Environments (TEEs) [1]. By delivering precise privilege control and fine-grained compartmentalization, we aim to improve TEE security standards. To address vulnerabilities within Trusted Execution Environments (TEEs) and enable selective privilege management and secure object sharing between secure and normal worlds, we introduce a TEE compartmentalization framework based on the CHERI object-capability model. Leveraging DSbD technologies, our framework provides an efficient prototyping environment for developing trusted applications while safeguarding against existing threats. At Verifoxx Ltd, our architecture relies on TEEs to handle sensitive data, encompassing tasks such as extracting client secrets, managing commitments, sharding and executing cryptographic operations for zero-knowledge responses. The proposed approach holds promise where TEEs can enhance transaction security and enterprises seeking data protection. Our approach introduces in-enclave compartments with controlled communication, facilitating domain transitions through sealed data capability delegations and hardware-assisted call/return mechanisms. This enables application layer compartmentalization by modularly separating concerns within the secure world, emphasising single responsibility, least privileges, and information hiding from unprivileged compartments. Furthermore, we ensure the integrity of lower-layer hardware and OS properties, effectively thwarting compromise attempts.
PENERAPAN SISTEM INFORMASI AKUNTANSI DALAM SIKLUS PRODUKSI SEBAGAI PENENTUAN BIAYA PRODUKSI PADA UMKM MELIA CAKES

Based on the current phenomena, the UMKM does not correct for record and count production costs and is still not completely used in the production cost statement. This study aims to describe the production cost determined after the implementation of the Accounting Information System of Production Cycle database. A qualitative method was used, and the types of data used were observations, interviews, and documentation. The results of this study indicate that Melia Cakes business activity still used the manual of record and count production costs. The production cost was determined almost correctly by Melia Cakes, but there are still costs not included in the production cost. Therefore, implementation of the Accounting Information System of Production Cycle is required for business activity. Because the Accounting Information System makes it easier to count, record, and determine production costs, it also provides an effective and efficient control of production costs.