Reviewerly/exHarmony huggingface dataset:
Security and Safety in Cyber-Physical System (CPS): An Inclusive Threat Model

A Cyber-Physical System (CPS) is a combination of computational algorithms and physical processes that are integrated together. Cyber-Physical Systems (CPS) integrate computer and communication capabilities with the monitoring and management of physical parts, establishing a mutually beneficial interaction between the cyber and physical components. Industrial Control System (ICS) is one example of CPS which integrates the physical (OT) and cyber domains (IT), which makes them more vulnerable to attacks. Two essential characteristics of Cyber-Physical Systems (CPS) are safety and security. Threat models are methods for identifying, analysing, and proposing security control countermeasures for threats and their capabilities. However, the threat model methods which are used for the traditional IT systems are not sufficient as they do not include the physical interactions, consequences and impacts to the safety aspects in the Operational Technology (OT). On the other hand, a risk assessment analyses attack scenario, examines cybersecurity from the attacker's point of view, and gives cost-benefit data to support the expenditure on security measures. This study proposes an inclusive attacker’s centric threat model and pro-active risk assessment model for CPS using Mamdani Fuzzy Inference System (FIS). The outcomes of the threat model prove that the lateral propagation of the threat is possible and threat may also propagate from the CPS assets to the IT segment. The risk assessment by using FIS shown that the safety and security risk for the CPS is significant and calculated as medium level. Hence, the risk factors that are considered in calculating the overall risk for a CPS need to be immediately addressed and mitigated.
Effect of typos on text classification accuracy in word and character tokenization

To train a machine to “sense” a users’ feelings through writings (sentiment analysis) has become a crucial process in several domains: marketing, research, surveys and more. Nevertheless in times of crisis like COVID. Typo is one of the underestimated challenges processing user-generated text (comments, tweets, ..etc), it affects both learning and evaluation processes. Word tokenization outcome changes drastically even with a single character change, hence as expected, experiments have shown significant accuracy decreases due to typo. Adding a spelling correction as preprocessing layer, building one for every language, is a very time and resources expensive solution, a huge challenge against large data and real-time processing. Alternatively, a CNN model consuming the same text, once tokenized on characters level and once on words level while inducing typo, showed that as the typo percentage approaches 10% of the text, the results with characters tokens surpasses words tokens. Finally, on %30 typo of the text, the model consuming characters tokenization outperformed itself with the word level by a significant %22.3 in accuracy and %24.9 in F1-Score, using the same exact model. This approach in solving the inevitable typo challenge in NLP proved to be of significant practicality, saving huge resources versus using a spelling-correction model beforehand. It also removes a blocker challenge in front of real-time processing of user-generated text while preserving acceptable accuracy results.
BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems

Machine Learning (ML) systems are now widely used across various fields such as hiring, healthcare, and criminal justice, but they are prone to unfairness and discrimination, which can have serious consequences for individuals and society. Although various fairness testing methods have been developed to tackle this issue, they lack the mechanism to continuously monitor ML system behaviour at runtime. In this study, a runtime verification tool called BiasTrap is proposed to detect and prevent discrimination in ML systems. The tool combines data augmentation and bias detection components to create and analyse instances with different sensitive attributes, enabling the detection of discriminatory behaviour in the ML model. The simulation results demonstrate that BiasTrap can effectively detect discriminatory behaviour in ML models trained on different datasets using various algorithms. Therefore, BiasTrap is a valuable tool for ensuring fairness in ML systems in real-time.
Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a student network is trained to reproduce the activity of a ground-truth teacher, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here both networks have the same connectivity but they have different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently infer unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different, and provides a framework to determine when such models yield consistent dynamics.
Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

&lt;strong&gt; Improving the performance and application of Large Language&amp;nbsp;Models &lt;/strong&gt; &lt;strong&gt; Author: Amanda Kau &lt;/strong&gt; &lt;strong&gt; https://orcid.org/0009-0004-4949-9284 &lt;/strong&gt; Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.
Development of English Teaching Media Using Macromedia Flash 8 to Improve Students' Learning Motivation

This study aims to develop a learning media with the Macromedia Flash 8 application to increase the learning motivation of class X MA PKP Al-Hidayah Jambi City. The researcher chose the Macomedia Flash 8 application because it makes it possible to create interactive learning media which consists of complete features so that the developed media is able to actively involve students' senses of sight and hearing so that the material absorption process becomes more optimal. And in the end the learning objectives can be achieved. This study aims to develop a learning media with the Macromedia Flash 8 application to increase the learning motivation of class X MA PKP Al-Hidayah Jambi City. The researcher chose the Macomedia Flash 8 application because it makes it possible to create interactive learning media which consists of complete features so that the developed media is able to actively involve students' senses of sight and hearing so that the material absorption process becomes more optimal. And in the end the learning objectives can be achieved. The results of this study are English teaching media in terms of validity, display quality, presentation quality and attractiveness aspects. Based on the results of the assessment questionnaire, media expert lecturers, design experts, and material experts gave the results of the assessment questionnaire in the very good category. The results show that aspects of convenience, attractiveness, efficiency and effectiveness of the product have been achieved. This proves that this product fits the needs and increases student motivation. Based on this research, learning subjects using macromedia flash 8 teaching media is in accordance with the needs of class X MA PKP Al-Hidayah Jambi City students. The development of this product can increase students' learning motivation.
Pixee knee+ augmented reality assisted navigation for total knee arthroplasty in an ambulatory surgical center

Total knee arthroplasty (TKA) requires precise alignment for optimal post-operative outcomes and prosthesis longevity. Recently, augmented reality (AR) has emerged as a promising technology in surgical procedures, including TKA. This case series evaluates the feasibility and accuracy of the knee+ augmented reality-assisted navigation (ARAN) system by Pixee Medical in an ambulatory surgical center (ASC) setting. Our study involved 17 consecutive TKA patients performed with the knee+ ARAN system at an ASC from August 2022 to October 2022. Demographic data, including sex, age, ASA score, height, weight, and BMI, were recorded. Postoperative measurements of the mechanical distal femoral angle (MDFA), mechanical distal tibial angle (MDTA), posterior tibial slope (PTS), femoral-tibial angle (FTA), and posterior femoral flexion (PFF) were compared to the ideal intraoperative angles. Outliers were defined as deviations greater than 3° from the planned angles. In this study, 15 out of 17 TKAs utilizing the Pixee knee+ ARAN system were analyzed. All mean post-operative radiographic measurements were within clinically acceptable ranges. The study also found that surgeries using the knee+ system had a slightly longer incision-to-closing time relative to the control group of patients undergoing normal TKA. Our results indicate clinically acceptable accuracy and precision in alignment with the knee+ ARAN system, albeit with a slight increase in surgery duration. This is the first study evaluating the knee+ ARAN system in an ASC setting indicates its suitability for outpatient centers, highlighting its precision, portability, and cost-effectiveness. Larger studies utilizing outcome measures can further assess the system’s advantages and disadvantages.
Construction of Simple Modules over the Quantum Affine Space

The coordinate ring [Formula: see text] of quantum affine space is the [Formula: see text]-algebra presented by generators [Formula: see text], [Formula: see text], [Formula: see text] and relations [Formula: see text] for all [Formula: see text], [Formula: see text]. We construct simple [Formula: see text]-modules in a more general setting where the parameters [Formula: see text] lie in a torsion subgroup of [Formula: see text] and show that analogous results hold as in the uniparameter case.
F ¯ p as a Discrete Metric Space

We define a metric that makes the algebraic closure of a finite field [Formula: see text] into a UDBG (uniformly discrete with bounded geometry) metric space. This metric stems from algebraic properties of [Formula: see text]. From this perspective, for [Formula: see text] we explore common research themes in metric spaces, reveal how peculiar properties naturally arise, and present it as a new type of example for certain well-studied questions.
English Teaching Assistance Employing the Multiple Intelligences Theory to Kasih Bangsa Elementary School Students

Considering that English is not the main language in Indonesia, an effective teaching method at school is crucial to students' ability to speak English fluently. However, in reality, many teachers still depend on conventional teaching methods. Therefore, this teaching assistance aims to facilitate Kasih Bangsa Elementary School students with a method that considers students' overall intelligence adapted from the Multiple Intelligences theory proposed by Howard Gardner, who argues that a person can have more than one intelligence. Furthermore, this teaching assistance is based on the Community-Based Participatory Research (CBPR) method, which focuses on the collaboration between researchers and communities. The program consists of three main processes: an interview with teachers of Kasih Bangsa Elementary School, a workshop on Multiple Intelligences Theory for the teachers, and a teaching demonstration by some of the students of the English Department, Maranatha Christian University. From this teaching assistance, the Multiple Intelligences theory is proven to improve the learning quality and boost students' motivation effectively.
Research on the Reform of School-Enterprise Cooperation Teaching and Education Mode for Computer Majors

This paper discusses the innovative methods of school-enterprise cooperation education mode in computerapplied talent training. An innovative training model based on school-enterprise cooperation is proposed to promote the cultivation of students’ practical and innovative skills, so as to better adapt to the needs of society. By analyzing the key links and influencing factors of the training mode, this paper puts forward some concrete suggestions and measures to provide guidelines for universities and enterprises in personnel training.
Research on the PDSIPE Teaching Mode for English Teaching in Applied Undergraduate Colleges

PDSIPE (Planning, Design, Scene, Implementation, Product, Evaluation) is the teaching concept and teachingoperation mode implemented by Professor Jungang Duo, which is promoted and tested in Heilongjiang University of Finance and Economics since 2022. The teaching mode meets the needs of application-oriented professional personnel training, and is compatible with local undergraduate colleges in terms of educational positioning, development strategic objectives, and teaching reform. It plays an important role in promoting the teaching reform of application-oriented undergraduate colleges, which is discussed from three aspects: teaching idea, practical operation, and overall evaluation mechanism.
An Emergency Plan Selection Method for Emergency Latency

Drawing upon a characteristic analysis of the latency period in emergencies, this paper proposes an emergency plan selection method based on interval language variables and information entropy to address the challenge of acquiring critical information during this crucial stage. Initially, decision-makers employ interval language variables to express the preference information regarding emergency plans, while also introducing an enhanced information entropy theory to derive the weight coefficients of key indicators. Subsequently, the weighted arithmetic average operator of interval language is applied twice to aggregate the preference information alongside the relative importance of decision-makers and the weight coefficients of key indicators. Finally, the ranking coefficients of each emergency plan are sorted to determine the optimal scheme. The feasibility and effectiveness of this method are demonstrated through a case study involving the selection of an emergency plan for a flood disaster in a specific location.
Deep Learning-Based Image Colorization using AI

Abstract -This project aims to explore the application of deep learning techniques in image colorization using artificial intelligence (AI). Leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), our objective is to develop a robust system capable of accurately and efficiently adding color to grayscale images. By training the model on a diverse dataset of grayscale images paired with their corresponding color versions, we aim to create a colorization tool that can replicate human-like color choices and produce visually appealing results. The proposed methodology involves preprocessing the dataset, designing and training the deep learning model, and evaluating its performance on test datasets. The anticipated outcome is a user-friendly image colorization tool that can be integrated into various applications, enhancing the visual experience and productivity of users. keywords: Deep Learning, Image Colorization, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Artificial Intelligence (AI), Grayscale Images..
Real-Time Sentiment Analysis

Natural Language Processing (NLP) is a computerized method to text interpretation that is founded on a set of ideas as well as a set of technology. This is a very active field of study and development, there is no universally accepted definition that satisfies everyone, but there are several elements that any intelligent person would consider a person's interpretation. Sentiment analysis or opinion mining is one of the important aspects of NLP (Natural Language Processing). In recent years, sentiment analysis has received a lot of attention. Sentiment analysis has a wide range of applications, including social media analytics, which simply means creating opinions for individuals on social media by analyzing their sentiments or ideas, which they express through text. It is also an example of how you can review customer feedback and responses. Thus identify the negative comments and reasons why the customers have issues with your product or service. Sentiment analysis enables you to respond to matters promptly before the customer leaves you altogether! In this paper, we aim to tackle the problem of Real-Time sentiment analysis, which is one of the dominant applications of sentiment analysis. Real-time sentiment analysis is an AI-powered solution to analyze the input provided by the user with the help of a pre-trained Machine Learning model. We have used a dataset of 1600000 tweets with 6 different attributes to build our model with the help of machine learning algorithms such as Naïve Bayesian classifier, Neural networks, and VADER. This paper is about the techniques and machine learning algorithms used to analyze the effectiveness of such models. Keywords: Sentiment Analysis; Machine Learning Algorithms; Naïve Bayesian classifier; Neutral Networks; VADER.
Multi-modal Emotion Recognition Using Neural Networks: Deploy on Cloud

This paper explores recent developments in emotion recognition, focusing on methodologies employing Multilayer Perceptron (MLP) classifiers. Emotion recognition is crucial for human-machine interaction across various domains, including healthcare, surveillance systems, and intelligent learning environments. There are many deep learning models that have been employed for emotion detection, this emphasizes the effectiveness of MLP classifiers. The project at hand centers on predicting emotions such as happiness, calmness, fear, and disgust solely utilizing MLP classifiers. The system is trained on audio data, with recordings matched in frequency, time, and speed to the raw data used for training. Additionally, Convolutional Neural Network (CNN) models are used here to analyze facial expressions in real-time video streams by providing neat and complete approach to emotion recognition. Upon deployment on the AWS cloud platform, the system provides real-time emotion predictions in textual format via a web interface created using Flask. Furthermore, the system offers the capability to analyze emotions from live video streams, presenting continuous feedback on emotions through a face-outlined box updated every three seconds. This functionality serves to confirm individuals' emotions and provide self-awareness regarding their emotional states. This project focuses on giving efficiency by using MLP classifiers in conjunction with CNN models for emotion recognition tasks, thereby contributing to the advancement of human-machine interaction. Key Words: MLP Classifier, CNN, AWS Cloud, Flask, emotion, audio, video
Implementing Interdisciplinary Teaching Approaches: Challenges and Strategies in Teacher Education under NEP 2020

This paper explores the challenges and strategies associated with implementing interdisciplinary teaching approaches in teacher education, aligning with the objectives of the National Education Policy (NEP) 2020 in India. Emphasizing the holistic development of students, NEP 2020 advocates for interdisciplinary education to nurture critical thinking, creativity, and problem-solving skills.
Problematika Kesulitan Membaca pada Anak Kelas Rendah: Peran Guru dalam Mengatasi Kesulitan Membaca

Reading is a major focus in lower education, especially at primary school level. This study discusses the factors of initial reading difficulties faced by students as well as the role of teachers in solving reading challenges. Research shows that groups of students with reading difficulties tend to have lower learning outcomes. Internal and external factors such as students' physical condition, family environment and school environment are factors that hinder reading. The teacher's contribution in solving reading problems is very crucial. Teachers are not only instructors, but also mentors or guides, motivators, and facilitators. The research method uses literature study, where the data collection system in this research is obtained from literature analysis through book and journal references that are relevant to the problems identified. Relevant research results include the role of teachers in overcoming reading difficulties, such as being a demonstrator, class manager, facilitator, and evaluator. Factors causing reading difficulties include the student's physical condition, lack of family attention, and an unsupportive school environment
Perancangan Aplikasi E-Learning Pada SMP Swasta Arridha Dengan Menggunakan Bahasa Pemograman PHP dan Database Mysql

This study elaborated on the design of E - Learning at Junior High School Arridha using the programming language PHP. From the research that has been conducted at Junior High School Arridha can be concluded that the system was not effective because the learning process is still done on the condition that the face to face between teachers and students . After doing some research on Junior High School Arridha , collecting data through interviews and other methods. Then the analysis of data collected by the system design tools . After the designed output design , design input , design files and E -learning . The results of the analysis have been carried out on the website of E -Learning in the PHP programming language , and the new system is expected to be able to provide facilities in the learning process in the First State School for more effective and efficient.
Regression Analysis of The National Cyber Security Index In The Southeast Asia Region

In the current era of the Industrial Revolution 4.0, information and communication technology is rapidly developing in all sectors. In the matrix of non-military defense implementation, technology is one of the dimensions that influence the achievement of a country's general defense policy. Technological development does not always have a positive impact. Cyber threats and attacks are an example of the negative impact in technological development. In the "Cyber Defense Guidelines," national cyber security is defined as all efforts to maintain the confidentiality, integrity, and availability of information and all its supporting facilities at the national level, which are cross-sectoral in nature. According to data from the National Cyber Security Index (NCSI) survey agency, a country's National Cyber Security Index (Y) is measured based on 12 indicators, including: cyber security policy development (X1), cyber threat analysis and information (X2), education and professional development (X3), contribution to global cyber security (X4), protection of digital services (X5), protection of essential services (X6), e-identification and trust services (X7), protection of personal data (X8), cyber incident response (X9), cyber crisis management (X10), fight against cybercrime (X11), and military cyber operations (X12). In this study, a regression analysis will be conducted on the 12 indicators of the National Cyber Security Index against the Total Assessment of the National Cyber Security Index (Y) in Southeast Asia, so that the type of equation in the regression analysis can be identified.