Reviewerly/exHarmony huggingface dataset:
Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space

Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.
Pembelajaran Mesin untuk Sistem Keamanan - Literatur Review

Security systems are one of the crucial topics in the era of digital transformation. In the use of digital technology, security systems are used to ensure the confidentiality, integrity, and availability of data. Machine learning techniques can be applied to support the system's adaptability to the environment, so that prevention, detection and recovery can be carried out. Given the importance of these things, it is necessary to review the literature to find out how machine learning is applied to security systems. This paper presents a summary of 31 research papers to determine what machine learning techniques or methods are the most promising for prevention, detection and recovery. The research stages in this paper consist of 6 stages, namely: formulating research questions, searching for articles, documenting search strategies, selecting studies, assessing article quality, and extracting data obtained from articles. Based on the results of the study, it was found that the K-means method was the most promising for prevention, while for detection, SVM could be used, and for security recovery, machine learning could be implemented using NLP-based features.
Developing Multi-Label Breast Cancer Screening using Efficientnetb3 Feature Extractor

Breast cancer is the first new case occurring and the fifth death-causing cancer disease in 2020, which collapses the families as women are the pillar of the family members. 3D image acquisition technology increases treatment methods and decreases the rate of breast cancer death. Furthermore, single-label breast cancer classifications are deployed to solve the problem. However, it cannot convey more information about mammogram images. In the area where the shortage of datasets is there as in the medical image, data augmentation and transfer learning are the best solution. A Multi-Label Breast Cancer Screening is a deep learning model using a transfer learning model that gives information on finding, density, and pathology of patients' breast mammograms to the radiologists, aiming for life-saving and supporting the family. The existing model shows low accuracy on feature extraction and more layers with a very large number of nodes in a layer in the classification part. In this study, the best feature extractor for this particular work is investigated and identified as efficientnetb3, and ONN is used for the classification part. The proposed model outperforms the previous work in all evaluation metrics with a 13.25% f1_score, 53% hamming loss, 36.7% coverage error, and 12.5 an exact match. And , the number of parameters were decreased from 134M to 20M which was resulted from the optimizing of classification part of the model. The developed model with ONN in the classification part has made the best improvement over the existing model in terms of evaluation metrics and network performances.
A Comparison of Document Similarity Algorithms

Document similarity is an important part of Natural Language Processing and is most commonly used forplagiarism-detection and text summarization. Thus, finding the overall most effective document similarity algorithm could have a major positive impact on the field of Natural Language Processing. This report setsout to examine the numerous document similarity algorithms, and determine which ones are the mostuseful. It addresses the most effective document similarity algorithm by categorizing them into 3 types ofdocument similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-basedalgorithms. The most effective algorithms in each category are also compared in our work using a series of benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
Question Generator and Text Summarizer Using NLP

Abstract: The main objective of this project is to develop an application to generate the questions from a given passage or paragraphs. This project is an application of Natural Language Processing (NLP). The application generates different types of questions such as MCQ’s. The current project helps the teachers to prepare questions to the examinations, quizzes etc. It also helps the students to get summary from the text they provide. The summary would be generated from given text then, the application identifies key concept in summary. It identifies key words from sentences and generates MCQ’s. The other options other than correct option called as Distractors. The application also paraphrases the input text. It allows the extraction of summary from the text is done by T5 Transformer Technique. We generate distractors using WordNet. The key words can be extracted by using Key BERT Technique. The questions are displayed or GUI is developed with the help of the Gradio (A user friendly Web-Interface). The application majorly targets every organization. The application develops Frequently Asked Questions (FAQ’s) of the customers to the particular organizations, which in terms gives the customer more information of the organizations. This application overrides the traditional method of preparing questions, and the complexity involved in making questions.
Review on Smart Thermoware - A Development in Assistive Self-Health Care Manangement

Abstract: Numerous applications and uses for thermal flasks are found by millions of people across the world. In this article, the developments of the vacuum flask's design and construction are covered. It now has technology to perform other tasks in addition to serving as a receptacle holding liquids, such as controlling temperature and serving as an alarm system. Consumers now anticipate that their vacuum vessel will preserve the freshness of the stored beverages. The vacuum flask should be able to sustain both hot and cold temperatures so that users can enjoy their beverages wherever they are and whenever they choose. People who have demanding and unpredictable work schedules are increasingly looking for reminders about how much fluid they should be consuming at all times to maintain a balanced diet
Pelatihan Web Log Sebagai Media Publikasi Sekolah dan Kegiatan Belajar Mengajar di SDN 1 Ngampel

The pandemic makes the utilization and competition of information and communication technology has increased. One of the ways to maintain the existence of schools are publication media of more varied school information and teaching-learning activities media. The purpose of this training program is to provide additional expertise for creating inexpensive and fast publications media and teaching-learning media. The offered solution is the use of web logs from Google which is often known as Blogger. Blogger offers a free and easy web log feature. The training was carried out directly at the Sekolah Dasar Negeri 1 Ngampel by using lectures and demonstrations method. There are several parts in this training, web log making, web log management, and also web log analysis. The results of this training show that the participants have succeeded in having their own web logs which are ready to be further managed and analyzed on a regular basis. The use of web logs can be used for various needs, including as a publication media and supporting teaching-learning activities.
A Study on the Usage of Personal Data for Public Interest by the Press and on Partial Exclusion of Application in the PIPA

When the personal data is reported through media, the Personal Information Protection Act(PIPA) should set forth standards for the extent to which this can be be permitted. In this article, I study a post-ex post legislative evaluation of the Personal Information Protection Act and to propose amendments.&#x0D; On Korean PIPA, Article 58, excludes Personal information collected or used for its own purposes of reporting by the press, only Chapters 3 to 7 of PIPA, so the provisions of Chapter 9(Supplemental Provisions) and Chapter 10(Penal Provisions) are all applied. Therefore, there is a problem that can be punished as a violation of the PIPA when the public reports for the public interest as well as media reports.&#x0D; Article 6 of the EU's GDPR (Lawfulness of processing) recognizes processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller(e). Article 85 stipulates that the Processing and freedom of expression and information should be balanced with the right to freedom of information, and that the application of Chapters 2 to 7 and 9 of the GDPR be excluded for journalism purposes. And the legality of processing in Article 8 of the UK Data Protection Act (2018) further elaborates this. Article 57 of the PIPA of Japan, excludes application, does not impose any obligations on personal information handling business operators for reporting purposes. In addition, the Japanese Public Interest Notified Person Protection Act requires the person making the public interest notification to submit a public interest notification to the Personal Information Protection Commission if there is a concern about violating the PIPA.&#x0D; In conclusion, on Korea's PIPA, it is necessary to stipulate that the PIPA is not applied in the case of public interest whistleblower. For this purpose, the provision of personal data should stipulate in Article 17 of the Amendment Act, “in case of reporting facts related to crime or facts related to crime for the purpose of public interest or reporting,”. and It is necessary to expand the exclusion of media coverage in the application exclusion rule of Article 58 to some punishment provisions, and to amend the direction to protect the public interest whistleblower protection law in the Public Interest Whistleblower Protection Act. At the same time, research should be continued so that relevant laws and regulations can present clear standards so that individual honor or privacy and public interest information can be guaranteed at the same time.
AN INTEGRATED APPROACH TO THE TEACHING OF IT ENGLISH FOR ESL STUDENTS

All students have objectives when taking an English class, but this is especially true of IT English students. Whether their objective is set by their working place or by themselves, it commonly involves learning the English they need to get ahead at work, connect with international clients, earn more money or maybe even keep their job. How is teaching IT English differs from teaching general English? Learning vocabulary for the work context is the number one priority for many ESL learners in IT English classes. One might be thinking, “But teaching IT English classes requires special qualifications, doesn’t it?” It helps them to communicate their message in a simple, effective way. This makes it important to teach common words and set expressions for everyday work situations. In order to achieve these goals, they need specific types of knowledge. Whatever it is, students will not be successful unless the teacher focus on what they need to know. Many teachers of English as a foreign language feel intimidated by the prospect of teaching IT English. This often stems from the perception that teaching IT English is the same thing as teaching IT studies. In fact, it's more about helping learners develop their English skills for use in an IT context. The demand for English teachers in the IT world is high, and if one knows how to teach English at all, he can fill that demand at ease. It is highly possible to have lively and effective classes using the wide range of vocabulary and excellent comprehension. All in all, IT English teaching can be very fascinating and rewarding. Although teaching ESL students requires a variety of skills and techniques, it mostly boils down to good preparation and a professional approach. Armed with really good knowledge, the IT English class will not only be a great learning experience and fun for students, but it will be pleasure for teacher as well.
Statistical reconstruction of heterogenous microstructures based on 2D images

Polymer nanocomposites typically possess heterogeneous microstructures that significantly affect structure-property relationships of these material systems. Various microscopic imaging techniques, such as optical microscopy, scanning electron microscopy (SEM), and X-ray microscopy, are essential for characterizing nanocomposite material systems and have provided informative insights of microstructural features. However, microscopic imaging through experiments can be expensive when large amounts of microstructural data are needed. One promising approach to address the imaging limitation and more efficiently generate large microstructural dataset is to statistically reconstruct similar images from a single original input image. A common method used to generate statistically equivalent images is the simulated annealing optimization algorithm. However, due to the high computational cost associated with the stochastic search path used in the simulated annealing algorithm, it can be challenging to reconstruct images with a high degree of agreement. Thus, in this study, a novel and more efficient image reconstruction method was developed by optimizing the simulated annealing algorithm through the manipulation of search path domain and available statistical information. The optimization technique was implemented to reconstruct several example two-dimensional (2D) images to evaluate its capabilities.
Awareness and Compliance of Information Security Policy in Organizations: Case from Libya

According to a review of the literature, Many employees are unaware of information security policies or choose to disregard them, which can lead to non-compliance.Lack of compliance with the intended policy results from a failure to understand the complicated relationships in the design and implementation of information security rules.This paper assesses the gaps in information security policy compliance.The paper aims to assess the existence of any gaps in the compliance and awareness of employees in the company.In this study, A questionnaire method was utilized to provide an understanding of the compliance within the organization.The questions were carefully selected to cover several factors of the subject areas.The outcome of the questionnaire is important to assess any hypothetical noncompliance among employees, and to specify who is more responsible, the management or the employee.The result finds that many employees are unaware of disregarding information security policies, which can lead to security breaches.The results show that employees are often unaware of information security policies and that they may not understand the importance of compliance.The paper concludes with recommendations for improving employee awareness and compliance with information security policies.
Improving MSAProbs Algorithm performance and Parallel Computing using GPU

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU).Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented).To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU).This idea helps in enhancing the performance of our algorithm (MSAprobs).To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs.The NIVIDIA API is used to investigate the GPU's computing power.The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench.The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.
The Application of the Technology Readiness Acceptance Model on Education

The development of science and technology has changed the work system, including in educational institutions. Rapid information and communication technology changes are now essential factors in changing education management. Improving the quality of education can leverage the quality of a nation. Technology in the teaching field provides many opportunities in various aspects of education and learning, such as academic services, access to learning materials, educational evaluation, management of lesson content, reports on student learning outcomes and the use of digital-based learning media by teachers. The use of technology in learning can create a competitive environment for students and teachers to be more creative and innovative. Education in Technology Study (Edtech) has become a global concern, especially during a pandemic. The revitalization and development of teacher and student synergy in implementing digital technology-based education will accelerate the education revolution 4.0.
Web Based Machine Learning Automated Pipeline

Abstract: With the increasing volume, velocity, veracity, and variety of data, it has become critical to have efficient techniques and tools for managing and analyzing data in machine learning. Abstraction is a powerful concept that allows users to interact with machine learning algorithms without understanding their technical implementation details. In this project the user will provide the dataset in .csv format the dataset is then processed further to different machine learning preprocessing steps like removing unwanted columns, handling missing values, label encoding, outlier detection and removal, normalization, model building, model prediction, and the result can be downloaded as pdf, tracable pdf and CSV, this all processes gives a result of different model and their respective accuracy so that we can choose the best model for that particular dataset. tracable pdf will be containing all the timestamp of the processes done with their respective result, Apart from client-server model user is also provided a api so that all processes can be implemented in different platforms like c++, java, ruby etc. Overall, this paper highlights the critical role of abstraction in managing the complexity of data and machine learning algorithms, enabling more efficient and effective analysis of large and complex datasets.
Measuring Machine Learning Robustness in front of Static and Dynamic Adversaries*

Adversarial machine learning brought a new way of understanding the reliability of different learning systems. Knowing that the learning confidence depends significantly on small changes, such as noise, created a mind change in the artificial intelligence community, who started to consider the boundaries and limitations of machine learning methods. However, if we can measure these limitations, we can improve the strength of our machine learning models and their robustness. Following this motivation, this work introduces different measures of robustness for machine learning models based on false negatives. These measures can be evaluated for either static or dynamic scenarios, where an adversary performs intelligent actions to evade the system. To evaluate the metrics I have applied 11 classifiers to different benchmark datasets and created an adversary that performs an evolutionary search process aiming to reduce the classification accuracy. The results show that the most robust models are related to K-Nearest Neighbours, Logistic regression, and neural networks, although none of the systems is robust enough when the target is to reach a single misclassification.
PWPROP: A Progressive Weighted Adaptive Method for Training Deep Neural Networks

In recent years, adaptive optimization methods for deep learning have attracted considerable attention. AMSGRAD indicates that the adaptive methods may be hard to converge to optimal solutions of some convex problems due to the divergence of its adaptive learning rate as in ADAM. However, we find that AMSGRAD may generalize worse than ADAM for some deep learning tasks. We first show that AMSGRAD may not find a flat minimum. So how can we design an optimization method to find a flat minimum with low training loss? Few works focus on this important problem. We propose a novel progressive weighted adaptive optimization algorithm, called PWPROP, with fewer hyperparameters than its counterparts such as ADAM. By intuitively constructing a “sharp-flat minima” model, we show that how different second-order estimates affect the ability to escape a sharp minimum. Moreover, we also prove that PWPROP can address the non-convergence issue of ADAM and has a sublinear convergence rate for non-convex problems. Extensive experimental results show that PWPROP is effective and suitable for various deep learning architectures such as Transformer, and achieves state-of-the-art results.
FACTORS AFFECTING AGRIBUSINESS SUCCESS AMONG THE PARTICIPANTS IN INTERNATIONAL INSTITUTE OF TROPICAL AGRICULTURE YOUTH AGRIBUSINESS PROGRAMME IN NIGERIA

The study examined the factors affecting agribusiness success among participants of International Institute of Tropical Agriculture (IITA) youth agribusiness programme in Nigeria. For the purpose of this research, non-probability sampling technique (Convenience) was adopted and the entire population of 117 was given the questionnaire but 110 respondents responded adequately. The study concludes that the following are the major factors (constraints) affecting the participants of IYA programme: lack of adequate storage facilities, lack of adequate processing facilities, lack of adequate government support, technological factors, power interruptions, loan application procedures of banks and other lending institutions are too complicated, high interest rate charged by banks and other leading institutions, and inadequacy of credit institutions. It was recommended that storage and processing facilities for agricultural/agribusiness inputs and products should be adequately provided, electricity (power/energy) that will adequately enhance the agribusiness activities should be provided, and also Government should be more involved in reducing the interest rate on bank loans advanced to agribusinesses through commercial banks.