Reviewerly/exHarmony huggingface dataset:
The Evaluation of Computer Science Curriculum for High School Education Based on Similarity Analysis

The government is currently developing regulations to regulate education curriculum For High School Students. In this regulation, curriculum standards have been created that can be developed by educators in schools. Computer science teachers at the school level develop a curriculum that has been set as a standard curriculum. However, measurable evaluation to optimize the development of the new curriculum has not been available yet. This research proposes a form of evaluation that can be used as a benchmark by analyzing the similarity of curriculum content developed by teachers using a text mining approach. This is conducted by comparing computer science documents with applicable documents, namely knowledge field documents. It is expected that the results of optimizing competency development in the computer science curriculum can be achieved better. The average similarity checking performances using Cosine Similarity and Word2Vec are 40.9850 and 97.3558 respectively. Meanwhile, in the process of fulfilling the knowledge sector, with Cosine Similarity an average percentage of 40.98% was obtained, and with Word2Vec an average percentage of 97.36% was obtained. The results of this trial will be used as a basis for measurable evaluation of teacher contributions to be able to develop the curriculum better according to the applicable curriculum. The results of this evaluation are also used by the government to make future curriculum evaluations more measurable and the standards used are clear and help facilitate curriculum development in schools.
Hoax Detection News Using Naïve Bayes and Support Vector Machine Algorithm

Websites and blogs are well-known as media for broadcasting news in various fields such as broadcasting news. The validity of news articles can be valid or fake. Fake news is also known as hoax news. The purpose of making hoax news is to persuade, manipulate, and influence news readers to do things that contradict or prevent correct action. This study proposes to experiment with the Support Vector Machine and Naïve Bayes classifications to detect hoax news in Indonesian. This study uses a dataset from public data, namely news between valid news and hoaxes. The system can classify online news in Indonesian with the term frequency feature the machine vector Support algorithm and naïve Bayes classification. While the evaluation model used is the Confusion Matrix. The results of the comparison of the two models as a Support Vector Machine have an accuracy rate of 75,5%, and Naive Bayes has an accuracy rate of 88%. Therefore, for the classification of hoax news, we recommend the Naive Bayes model because it has a better level of accuracy than the Support Vector Machine.
Order of Magnitude Increase in Storage Time of Photonic-Phononic Memory

With the recent developments in photonic computing platforms, the need of novel optical memories becomes increasingly important. Optoacoustic memories have shown great potential due to their quick access time, coherent nature, and broad bandwidth. These continuously tunable memories can be controlled entirely optically, but so far their intrinsic storage time is limited by the acoustic life time to approximately 10 ns. In this work, we experimentally demonstrate a cryogenic optoacoustic memory with an intrinsic storage time beyond 135 ns.
Experimental Demonstration of a Photonic Extreme Learning Machine with an Array of Microresonators

The training process of a feed-forward neural network is typically power- and time-consuming because it requires optimization of the output response through a gradient descent algorithm. An alternative to these approaches is the extreme learning machine, which is a feed-forward neural network composed of a single hidden layer in which training occurs only in the readout. Here, we propose and experimentally validate an extreme learning machine architecture based on an array of 18 silicon microresonators. We provide a proof-of-concept demonstration of the network by solving the nonlinear logic operation XOR, the iris flower classification, and banknote authentication.
Noise-Tolerant Gradient Measurement of Matrix Norm for Programmable Unitary Converters

Programmable unitary converters are powerful tools for realizing unitary transformations, essential in the fields of quantum computing, machine learning, and optical communication. The precision of these unitary transformations is crucial for maintaining high fidelity in such applications. However, various physical artifacts can impair the accuracy of these transformations. A commonly employed approach utilizes the system's gradient to restore accuracy. Although this gradient can indeed be physically measured using external equipment, it leads to a rather bulky optical system. Alternatively, some studies propose approximating the gradient by finite difference, which demands precise parameter control and measurement. However, such precision leaves this approach vulnerable to noise. In this study, we propose a gradient measurement method that is resistant to noise and does not require any supplementary equipment. Our numerical analysis demonstrates that our method exhibits orders of magnitude higher tolerance to noise than the prior approach, thus considerably reducing the system requirements.
Comparative Analysis of Software Development Lifecycle Methods in Software Development: A Systematic Literature Review

In the last decades, various Software Development Lifecycle (SDLC) models have been developed to meet the different needs and challenges in the software industry. The purpose of this research is to analyze and compare some of the most common SDLC methods. After the selection and evaluation process is complete, a literature review is carried out by collecting articles, books, and other sources related to the SDLC method. Several main SDLC methods were selected for thorough analysis. Waterfall, Agile and Scrum are some of the methods. Important factors such as flexibility, speed of development, ability to adapt to changing requirements, and project risk are evaluated. The results of the analysis show that each SDLC method has strengths and weaknesses, and that they are appropriate for a variety of situations. While Agile and Scrum methods emphasize flexibility and teamwork, the Waterfall method provides greater structure and clarity to plans. This study aims to determine the best process method for software development. This literature review provides an in-depth understanding of the features, strengths, and weaknesses of various existing SDLC methods. With a better understanding of these methods, organizations can choose the SDLC method that best suits their project needs, thereby increasing the efficiency and effectiveness of software development. This research resulted in a process method that is widely used in software development, namely the Agile method.
Підготовка майбутнього вчителя фізики до формування дослідницької компетентності учнів із застосуванням інформаційно-комунікаційних технологій

The article discusses the topical issue of developing the research abilities of primary and high school students in the context of implementing a conceptual pedagogical model aimed at training physics teachers. The article analyzes the development of physical education and the possibilities of using information and communication technologies to achieve didactic goals, in particular the use of ICT tools, computer equipment, digital and cloud technologies, digital measuring complexes and systems, electronic resources, STEM technologies, and others. It is analyzed and found that «e-learning» is becoming an increasingly important component of the higher education system due to innovative technologies that expand its scope and significance. This leads to the introduction of a hybrid model of education, where the main emphasis is on e-learning, and traditional learning is gradually decreasing. In this regard, the article proposes a methodological system for the development of information and communication competencies in students studying physics in pedagogical institutions of higher education. The main purpose of this system is to develop methodological competencies of future physics teachers. Among the functions of the system are methodological, which promotes the use of professional concepts and theoretical knowledge of physics in the educational process; professionally oriented, which implements the content of physical knowledge in the practical side of learning; integrative, which promotes a systematic understanding of physical aspects; developmental, aimed at developing students' thinking, activity and creativity; prognostic, which focuses on the further development of the methodological system. The article proposes the content of the methodological system, which takes into account the content components of general physics, theoretical physics, methods of teaching physics and special courses. The possibility and feasibility of implementing the developed methodological system in the educational process, taking into account pedagogical, organizational and methodological conditions, are discussed.
Can the Clinical Utility of iBreastExam, a Novel Device, Aid in Optimizing Breast Cancer Diagnosis? A Systematic Review

PURPOSE A portable, cost-effective, easy-to-use, hand-held Intelligent Breast Exam (iBE), which is a wireless, radiation-free device, may be a valuable screening tool in resource-limited settings. While multiple studies evaluating the use of iBE have been conducted worldwide, there are no cumulative studies evaluating the iBE's performance. Therefore this review aims to determine the clinical utility and applicability of iBE compared with clinical breast examinations, ultrasound, and mammography and discuss its strengths and weaknesses when performing breast-cancer screening. METHODS A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Four electronic databases were searched: PubMed, Cochrane Library, Web of Science, and Google Scholar. RESULTS The review included 11 studies with a total sample size of 16,052 breasts. The mean age ranged from 42 to 58 years. The sensitivity and specificity of the iBE ranged from 34.3% to 86% and 59% to 94%, respectively. For malignant lesions, iBE demonstrated a moderate to higher diagnostic capacity ranging from 57% to 93% and could identify tumor sizes spanning from 0.5 cm to 9 cm. CONCLUSION Our findings underscore the potential clinical utility and applicability of iBE as a prescreening and triaging tool, which may aid in reducing the burden of patients undergoing diagnostic imaging in lower- and middle-income countries. Furthermore, iBE has shown to diagnose cancers as small as 0.5 cm, which can be a boon in early detection and reduce mortality rates. However, the encouraging results of this systematic review should be interpreted with caution because of the device’s low sensitivity and high false-positive rates.
Human values in a digital age <b>We, the Data: Human Rights in the Digital Age</b> <i>Wendy H. Wong</i> MIT Press, 2023. 280 pp.

Big Tech must center human rights in data decisions, argues a political scientist
Automated Risk Assessment Process for Government Agencies &amp; Industry Leaders: Cybersecurity Protection for Operational Technology

As Federal ever-changing requirements for cybersecurity protection increase so has the importance and need to demonstrate their compliance and effectiveness within implemented/deployed IT (Information Technology)/ OT (Operational Technology) systems'. These requirements have placed a heavy burden on Cybersecurity Risk Assessment tools, such as the need to support the RMF (Risk Management Framework), CSF (Cyber Security Framework), CSA (Cyber Survivability Attributes), and MRAP-C (Mission-based Risk Assessment Process for Cyber). Since time is of essence, these tools need to produce systematic and comprehensive assessment results in near real-time, support T&E (Test and Evaluation) teams in their evaluation and propose a course of action to harden protection of systems to ensure a secure supply chain, protection of sensitive information, and readiness to mitigate cyber-attacks. The objective is to cover how to prepare your IT/OT to comply with today's government requirements and the necessary artifacts through automation of risk assessment process.
Machine Learning-Based Gesture Recognition for Communication with the Deaf and Dumb

A deep learning model specifically designed to recognize signs in sign language is the foundation of the Sign Language Recognition system. Sign Language is a visual language used by the deaf and hard of hearing community to communicate with one another and the general public. Sign language is a kind of nonverbal communication based on the use of hand gestures. The ability to communicate socially and emotionally is greatly aided when the speech and hearing challenged have access to sign language. The model developed in this paper captures the images through live web cam and displays the sign language meaning on the screen as text output. The model is trained and built by deep learning framework using Convolution Neural Networks (CNN) in this work. The model is trained with images of hand gestures captured through webcam using Computer Vision and then after successful training, the system performs recognition process through matching parameters for a given input gesture and finally displays the sign language meaning of the gesture as text output on the screen.
Identify and Prioritize the Challenges of Customer Knowledge in Successful Project Management: An Agile Project Management Approach

The main advantage of Agile Project Management (APM) lies in its ability to investigate and resolve issues that arise during the project period, making timely adjustments to save resources and deliver successful projects on time and at a lower cost. Customers play a crucial role in determining many of these changes, highlighting their special involvement in managing projects using an agile approach. This paper prioritizes the factors influencing challenges related to customer knowledge in APM, initially identifying three categories: individual, organizational, and technological factors. Expert opinions from the software development industry verified these factors, while the DANP method explored their causal relationships and importance. The analysis revealed organizational factors’ impact on the other two categories, with individual factors ranking highest, followed by technological factors. Notable challenges related to customer knowledge include lack of time for knowledge sharing, reluctance to adopt information technology systems, ineffective communication between knowledge-holders and seekers, inadequate training on new technology, and a lack of awareness regarding knowledge benefits for project partners. These findings are presented as suggestions to project teams for effectively managing agile projects and addressing customer knowledge challenges. By implementing these recommendations, projects can achieve greater efficiency and success.
Keystroke Dynamics-Based Analysis and Classification of Hand Posture Using Machine Learning Techniques

Keystroke dynamics, sometimes known as typing biometrics, is an automated method of recognizing or verifying a person's identity based on the style and rhythm of their keyboard strokes. It alludes to the precise timing data that shows when each key was pressed and when it was released during keyboard typing. Keystroke Dynamics assists in identifying a specific person's hand biometric template. This research uses a wide range of dwell time and flight time attributes to ascertain the hand posture of a specific person. To determine a user's hand posture at any given time, an Android application was created to record about 13 distinctive and unquestionably important attributes. Through this application, information was gathered by having users participate in a typing session. A variety of keystroke-related data, including Pressure, Finger Area, Uptime, and Downtime for each key, and motion-based data, including RawX, RawY, GravityX, GravityY, and GravityZ, were collected. Additionally, to achieve higher levels of accuracy, multiple Machine Learning models were used including ensemble classification methods like Bagging and Boosting to achieve conclusive results. It was observed that the Random Forest classifier obtained the highest accuracy score of 97.30%. The model was integrated with a mobile application and was utilized to identify the hand involved in the typing process. This work can be extended to include the field of surveillance, Multi-factor Authentication (MFA), and to help improve the one-hand mode layout.
Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning

Although surveillance cameras are used in ATM cells, we face some problems of robbery and theft at ATMs due to lack of security; however, the monitoring capacity of law enforcement agencies has not kept pace. ATM spoofing attacks can be carried out to break or damage the ATM by stealing the machine and taking cash from the ATM. To reduce this problem, we arm the ATM with a camera module mounted in the room to perform continuous video observation. The camera detects the human and his activity in the ATM and attempts to breach the ATM. It detects unusual activities and immediately sends an alert notification to the police. Therefore, the system handles the application developed to automate video surveillance and detect any potential criminal activity at ATMs. Therefore, in this work, abnormal behavior is observed using CNN and RNN in surveillance videos. These algorithms can be used to recognize faces, detect and track camera movements, and detect and identify the action required to prevent such activity.
An Innovative New Open Computer Vision Framework Via Artificial Intelligence with Python

Computer vision has emerged as an important subject of study, with several practical applications in a wide range of domains. OpenCV, a widely used framework, has played an important role in allowing computer vision tasks. This study presents an AI-driven Python implementation of the OpenCV framework to expand its capabilities. Data collecting and pre-processing, feature extraction, model selection and training, and Python-based system implementation are all part of the proposed system. A thorough examination of system performance indicates its advantage over competing approaches. The system's flexibility enables it to handle a variety of computer vision tasks, such as gesture recognition, face detection, and object recognition. This paper advances AI-powered computer vision systems by providing significant insights into the implementation of OpenCV with Python. The focus of future work will be on improving system accuracy and broadening its functional range.
Philosophical Problems in the Theoretical Heritage of K.D. Ushinsky (to the 200th Anniversary of the Birth)

The article provides a historical and philosophical analysis of the theoretical her­itage of one of the most significant figures of the “golden age” of Russian cul­ture, K.D. Ushinsky. Ushinsky entered intellectual history primarily as a “teacher of teachers”, the founder of scientific pedagogy and philosophy of education in Russia. At the same time, he paid great attention to the philosophical substan­tiation of his views on education, and a number of his works contain a fairly de­tailed exposition of general issues of philosophical anthropology, philosophy of history and philosophy of science. In philosophical terms, Ushinsky was sig­nificantly influenced by Hegel, the Russian Hegelians (P.G. Redkin), Feuerbach and the first positivism; however, as it is shown in the article, at the same time he freely combined and interpreted ideas coming from different sources, taking a generally independent theoretical position. The originality of Ushinsky’s philo­sophical thinking was especially clearly manifested in his development of the ca­tegory of “narodnost” (a concept that in the Russian intellectual tradition meant national identity, preserved in its pure form mainly in the lower social strata). Ushinsky significantly modified the meaning of this category which had a key importance for the Russian culture of the “golden age”, giving it not only an evaluative, but also an instrumental character. In addition, in Ushinsky’s works, this concept received a certain sociological meaning and interpretation within the framework of Ushinsky’s ideas about the stages of the socio-historical development of mankind and the modernization of post-reform Russian society, which was experiencing difficult collisions characteristic of the process of the for­mation of capitalism.
On the Rationality of Argumentation in Contemporary Pedagogics (Methodological Aspect)

Discussions, analysis of hypotheses, critical review of publications, polemics, and other forms of immanent scientific criticism are a condition for the development of scientific, and even more so, philosophical knowledge. It is important, how­ever, that these forms of critical communication of scientists are realized in the form of a constructive dialogue, the participants of which strictly follow the ra­tional correctness of their argumentation and adequately respond to criticism. The reason for writing this collective article was the appearance in the field of pedagogical researches of works that contain conceptually incorrect and practi­cally unsubstantiated statements. Meanwhile, we are talking about a research area in which errors can provoke very serious negative consequences for Russia. The intentions of the authors by no means include imposing their point of view on cer­tain problems of modern education – the article demonstrates, using specific ex­amples, elements of incorrect logical and substantial constructions used by partic­ipants in scientific discussions and assesses their possible consequences for education and upbringing, as well as their theoretical understanding of pedagogi­cal problems. The authors proceed from methodological guidelines that direct re­searchers toward a “harmonizing dialogue” (A.A. Potebnja), and in this context, special attention is paid, so to speak, to the constructive role of “mistakes” which are found in scientific discussions, and their transformation into “growth points” of knowledge, as soon as a researcher is ready for their reflective evaluation.