Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Transfer Learning Networks and Siamese Networks for Acute Lymphoblastic Leukemia (ALL) Diagnosis

Acute Lymphoblastic Leukemia (ALL) is a type of blood cancer that primarily affects the white blood cells. ALL is a serious problem in our society, accounting for approximately 25% of all pediatric cancers. Another major issue of ALL is that the healthy and cancerous cells look extremely similar to the human eye, leading to high chance of misdiagnosis. A novel method was proposed to solve a two-fold problem using Few Shot Learning (FSL), a machine technique that uses a small support set to perform binary classification. Traditional machine learning methods require extremely large medical training datasets, which can lead to high computation expenses and are difficult to access due to patient privacy. Convolutional neural networks (CNNs) such as VGG19 and GoogLeNet were also compared to investigate if there was any significant improvement with FSL on the CNMC dataset. Currently, our Siamese Network has a 85% testing accuracy when training it on 10 Epochs. However, transfer learning has also shown to be a way to use a small amount of data to receive a high accuracy rate, and require a less computationally intensive training process.An accuracy rate of 99% and 96% was achieved on GoogLeNet and on VGG-19, which were trained for 50 epochs with image transformation preprocessing. Overall, traditional CNNs continue to outperform FSL methods, but methods are still a viable option for second opinions.
Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

Foreground-background image segmentation has been an important research problem.It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences.It provides candidate objects for further attentional selection, e.g., in video surveillance.In this paper, we introduce an automatic and efficient Foregroundbackground segmentation.The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter.Then, each point in the maps classifies as salient or non-salient using a binary threshold.Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image.For better separation of the foreground and background, dilation and erosion operators are also used.Erosion and dilation operators are applied for shrinking and expanding the achieved region.Afterward, the foreground and background samples are achieved.Because the number of these data is large, Kmeans clustering is used as a sampling technique to restrict computational efforts in the region of interest.K cluster centers for each region are set for training of Support Vector Machine (SVM).SVM, as a powerful binary classifier, is used to segment the interest area from the background.The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.
CHALLENGES OF ONLINE LEARNING DURING PANDEMIC COVID-19 IN JUNIOR HIGH SCHOOL

Coronavirus has moved people's learning patterns from the classroom to the internet. This article explores the challenges of online learning during the pandemic: hardware and internet connectivity concerns, perspectives of student, teacher and parent, and school system. Interview was used to collect the data. The descriptive analysis was implemented to categorize and classify the claims. It is discovered that certain students do not have a smartphone, that internet data is costly, and that the network is bad based on hardware and internet access. Students' perspectives on online learning include making a lot of tasks, difficult-to-understand content, being unable to access the internet, and not being able to use any phone apps. The viewpoints of the teachers are also listed. They include a lack of IT comprehension, a poor connection, a lack of student answers, and a loss of direct contact. Other experiences shared by parents include the inability to afford internet data, a lack of expertise, and inadequate time to assist their children in online learning, and the parents' complaint about their children's online gaming distraction. The education system, which dictates the lack of IT preparation and the minimal budget for promoting online learning, is the final challenge. The results suggest that teachers, parents, and schools should exert greater control over the development of online learning for students.
Patch based Classification using ResNet for Land Cover changes detection of Batu City

The purpose of this study is to analyze the variations in land cover in Batu City, East Java Province, Indonesia, utilizing a patch-based classification strategy and deep learning. This study provides a preliminary estimation of land cover change in Batu City. The research also highlights the possibility of using deep learning techniques to analyze land use and land cover (LULC) variations in other urban areas with greater precision and efficiency. The EuroSAT dataset is used to train a classification model for patch labeling using the ResNet-50 architecture. Comparing the land cover of Batu City in 2001 and 2022 allows us to detect LULC changes, with almost 50% of the patch changing. The results indicate that ‘Housing’ and ‘Road’ become the most changed categories, while the vegetation areas decrease in number. The results demonstrate that the ResNet-50 architecture is capable of classifying patches and detecting LULC changes with an accuracy of 88% and an execution time of approximately 126.53 seconds.
Knowledge Management and Its Role in Organizational Effectiveness and Decision Making: A Descriptive Study

For firms looking to preserve a competitive advantage in the fast-paced commercial climate of today, knowledge management is an essential task. This descriptive study aims to investigate the function of knowledge management in decision-making and organizational effectiveness. The study covers the core ideas and elements of knowledge management and how they might be used to improve organizational performance through a survey of the literature. The study also explores the role that knowledge management can play in assisting decision-making at the organizational and individual levels. The results of this study demonstrate the significance of knowledge management as a tactical instrument for managing intellectual capital and adding value in businesses. According to the study's findings, a knowledge management system that is effective can increase organizational effectiveness by promoting information sharing and innovation.
Virtual Reality and Mental Health Care

The current state of virtual reality is confined to the realm of video games, movies, and other entertainment forms. In recent years, however, VR has begun to make its way into the therapeutic realm. Virtual reality therapy is an emerging treatment for those with psychological disorders or social anxieties. This new form of therapy seeks to benefit from the immersive properties of virtual reality. VR treatment seeks to treat phobias by placing the patient in a virtual environment where they would come into contact with their anxiety-causing object or event. The aim is that, over time, patients would begin to show improvement in regards to their anxiety response when faced with real-world objects or events that invoke their fear. Further, VR treatment seeks to treat post-traumatic stress disorders by allowing patients to relive their trauma in a safe environment where they can watch themselves reenact the traumatic event. The goal is that patients would begin to build skills through practice that would be able to help them cope with actual situations of danger. The technology to create VR environments is already available, and the treatment methods are refined through clinical experiments with patients. VR has opened up a new avenue of understanding and treatment for those who suffer from psychological disorders, and it has the potential to be more effective than traditional therapeutic methods.
New Ideas for Brain Modelling 7

This paper updates the cognitive model, firstly by creating two systems and then unifying them over the same structure. It represents information at the semantic level only, where labelled patterns are aggregated into a 'type-set-match' form. It is described that the aggregations can be used to match across regions with potentially different functionality and therefore give the structure a required amount of flexibility. The theory is that if the model stores information which can be transposed in consistent ways, then that will result in knowledge and some level of intelligence. As part of the design, patterns have to become distinct and that is realised by unique paths through shared aggregated structures. An ensemble-hierarchy relation also helps to define uniqueness through local feedback that may even be an action potential. The earlier models are still consistent in terms of their proposed functionality, but some of the architecture boundaries have been moved to match them up more closely. After pattern optimisation and tree-like aggregations, the two main models differ only in their upper, more intelligent level. One provides a propositional logic for mutually inclusive or exclusive pattern groups and sequences, while the other provides a behaviour script that is constructed from node types. It can be seen that these two views are complimentary and would allow some control over behaviours, as well as memories, that might get selected.
Scale-Space Hypernetworks for Efficient Biomedical Imaging

Convolutional Neural Networks (CNNs) are the predominant model used for a variety of medical image analysis tasks. At inference time, these models are computationally intensive, especially with volumetric data. In principle, it is possible to trade accuracy for computational efficiency by manipulating the rescaling factor in the downsample and upsample layers of CNN architectures. However, properly exploring the accuracy-efficiency trade-off is prohibitively expensive with existing models. To address this, we introduce Scale-Space HyperNetworks (SSHN), a method that learns a spectrum of CNNs with varying internal rescaling factors. A single SSHN characterizes an entire Pareto accuracy-efficiency curve of models that match, and occasionally surpass, the outcomes of training many separate networks with fixed rescaling factors. We demonstrate the proposed approach in several medical image analysis applications, comparing SSHN against strategies with both fixed and dynamic rescaling factors. We find that SSHN consistently provides a better accuracy-efficiency trade-off at a fraction of the training cost. Trained SSHNs enable the user to quickly choose a rescaling factor that appropriately balances accuracy and computational efficiency for their particular needs at inference.
A Review of Machine Learning and AI-Based Approaches to Detecting Cyberbullying on Social Media

Abstract: With rapid rise in the use of social media, cyber-bullying has become a significant concern, affecting individuals of all ages and backgrounds. Finding cyberbullying on social media is essential to building a secure and welcoming online community. The various approaches and techniques used to identify cyberbullying on social media are examined in this research, ranging from traditional keyword-based strategies to more sophisticated machine learning and NLP-based algorithms. We also draw attention to the difficulties and restrictions posed by the use of current techniques, such as their poor accuracy, lack of standardization, and privacy issues. We also go over the significance of addressing social and cultural disparities as well as the necessity of ethical concerns when identifying cyberbullying. Our review provides a comprehensive understanding of the current state of cyberbullying detection on social media, along with recommendations for future research. Ultimately, combating cyberbullying and fostering a more supportive and positive online environment depend on the development of more precise and reliable detection techniques
Pengembangan Game Kuis Berbasis Android Menggunakan Kahoot Untuk Siswa Kelas V SDIT Darul Falah

Technological advances in the form of smartphones are currently an example of how technical sophistication can be used to support learning activities. The use of gadgets, especially android operated by children, includes games. One alternative learning media that is considered capable of overcoming problems in the learning process is an android-based quiz game using kahoot, because this quiz game learning media students are able to play, learn, and be active in learning so that students' understanding in learning thematic material becomes better and memory students become more refined. The android-based quiz game using kahoot has a purpose, namely to determine the design of android-based quiz game development using kahoot, the quality of android-based quiz game products using kahoot, and the effectiveness of android-based quiz games using thematic subjects. This research procedure uses a 4D model, namely define, design, development, and dissemination. SDIT Darul Falah Jombang is the location chosen to carry out research on a large scale, with 18 students enrolled in the 2021/2022 academic year. Based on this research, it was found that the application of android-based quiz game media using Kahoot was included in the very good category with a total score of 89% according to three expert experts, including media experts with a percentage of 94% in the very good category, material experts with a percentage of 86% in the very good category, and RPP experts with a percentage of 88% in the very good category. While the effectiveness through the results of the student response questionnaire obtained a score of 88% including the very good category. Thus, it can be concluded that the android-based quiz game media using kahoot is feasible and effective in the learning process of the fifth grade students of SDIT Darul Falah Jombang.
SE-VFC: A Secure Framework for Computation Outsourcing in Vehicular Fog Computing Environments

Abstract: SE-VFC is a proposed computing scheme that addresses the challenge of providing secure and efficient outsourcing computing in vehicular fog computing. Vehicular fog computing is a paradigm that involves the utilization of computing resources in vehicles to provide various computing services to end- users. However, since fog vehicles may be untrusted, there is a potential for malicious operations that can cause serious accidents. To address this challenge, SE-VFC proposes the use of a secure and efficient outsourcing computing scheme that involves fog vehicles with computing resources. The scheme utilizes lightweight Boneh-Lynn-Shacham (BLS) signature and group signature for batch anonymous authentication of fog vehicles while preserving their privacy in multiple outsourcing tasks. This means that fog vehicles are authenticated and verified without revealing their identities, which helps to protect their privacy. Additionally, SE-VFC ensures the correctness of the computing results by verifying them. This helps to ensure that the results produced are accurate and reliable. Compared to existing schemes, SE-VFC has relatively low communication and computation overhead, making it efficient for batch authentication in multiple computing tasks. The effectiveness and practicality of the proposed scheme have been verified through extensive simulation results. Overall, SE-VFC is an important contribution to the field of vehicular fog computing, as it addresses the challenge of providing secure and efficient outsourcing computing while also ensuring the privacy and correctness of the computing results.
Using machine translation for explicit knowledge assessment in translation education : Focusing on the use of metalanguage.

The purpose of this study is to present Machine Translation (MT) metalanguage learning activities and to examine whether the degree of metalanguage utilization shown by undergraduate students majoring in translation is related to their explicit knowledge, and their ability to identify and solve translation problems. Metalanguage helps learners to learn foreign languages by activating the metalanguage knowledge they have about their native language and their ability to explain specific language phenomena or translation problems. Unlike the previous undergraduate machine translation education research focused on post-editing, this study attempts to use machine translation as a tool for translation learning with the purpose of promoting understanding and knowledge of translation and explaining theoretical approaches to translation problems. Using the machine translation result of the novel Pachinko, students conducted the following learning activities with “Translation Problem Judgment Tests”: 1) Understand the translation problems, 2) Indicate the translation problems, and 3) Explain and correct the translation problems. The analysis found that although the use of metalanguage was active, there was a case where the translation problem was not accurately identified and analyzed. This meant that the two sub-categories of explicit knowledge, metalanguage and analyzed knowledge, were not highly correlated. On the other hand, the higher the frequency of using the metalanguage, the higher the explicit knowledge needed to explain the translation problem. Taken together, these results suggest that meta-linguistic education enhances students' awareness of translation and ultimately helps expand explicit knowledge. The findings of this research have significant implications because they show that metalanguage learning activities using machine translation in undergraduate translation education can help students increase their explicit knowledge of translation and extend their analytical skills.
THE USE OF MOBILE APPS TO ENHANCE READING SKILLS OF LANGUAGE LEARNERS: A REVIEW

Reading activities are generally compromised owing to the time constraints in the classroom. As an outcome, students’ inability to understand a passage has an adverse effect on their enthusiasm and confidence despite knowing that reading acquisition plays a crucial role in language learning. In this regard, mobile apps seem to be the powerful support for acquiring reading skills and interpreting comprehension which are the two challenging aspects of language learners to be knowledgeable enough. They also enrich readers with vast opportunities and provide a stress-free environment that aids them to improve their reading skills. The paper reported here explores the effectiveness and the role of mobile applications in acquiring reading skills. Reviewed articles and dissertations used in this paper focus on the development, analysis, examination of the mobile apps for enhancing reading skills. The paper also explores the research design, methodology and theory that support the usage of chosen mobile apps for the research. This review article concludes that reading skills can be enhanced better through mobile apps than that of the conventional academic setting.
Phenotyping Genetic Diseases Through Artificial Intelligence Use of Large Datasets of Government-stored Facial Photographs: Concept, Legal Issues, and Challenges in the European Union

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future &amp;quot;Orwellian&amp;quot; ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.
Securexa: A Facial Recognition-Based Security System – Review of Literature, Research Analysis &amp; Methodology

Recent years have seen increased usage of facial recognition technology across many industries. Among the industries that have adopted this technology is the security industry. These systems are becoming more and more popular as a result of their high level of security, user-friendliness, and capacity to do away with conventional physical keys or access cards. Securexa can be an upcoming facial recognition-based security system, producing state-of-the-art door locking systems based on facial recognition that offer high-tech security features for residential and commercial premises. Systems for facial door locks are now the go-to option for access control in a variety of locations, including residences, workplaces, hotels, and other public spaces. For the aim of identification and authentication, these technologies capture and analyse facial features using advanced algorithms. The accuracy and dependability of facial recognition systems have greatly improved with advances in machine learning and artificial intelligence. With Securexa facial recognition door locking, restricted areas can be secured with advanced facial recognition technology.