Reviewerly/exHarmony huggingface dataset:
Efektivitas Penggunaan Nirs Bundle dan Double Skin Barrier Hydrocolloid terhadap Penurunan Nasal Injury pada Bayi Prematur dengan NCPAP

This study aims to analyze the effectiveness of using the NIRS Bundle and Double Skin Barrier Hydrocolloid to reduce nasal injury in premature infants using NCPAP. The method used is Quasy Experimental Design with Posttest-Only Control Design. The results showed that the p-value in the NIRS Bundle treatment group was p = 0.001 and p = 0.01 in the Double Hydrocolloid treatment. In conclusion, the NIRS Bundle intervention and the installation of Double Skin Barrier Hydrocolloid had a significant effect on reducing nasal injury in premature infants with NCPAP. &#x0D; Keywords: Hydrocolloid, Nasal Injury, NCPAP, Neonate, NIRS Bundle
Identifying a Student as a Subject of Educational Activity: Methodology and Web-based Application

The purpose of the article is to outline the core concepts of a subject intension, molding and development, being also important to the intensification of AI-enhanced university education. Cognitive development clearly suggests that it is inseparably linked with speech intellectual activity and because of it is reality reflection as if refracted in the light of linguistic meaning. The developed program reflects the logic of the teacher's activities in the formation of a student's personal qualities [participating in subject-subject relations; participating in a joint, discursive practice on the part of all participants, which leads to the active transformation of the students' “Image of the World”; contribution to the development of self-regulation, that is, the capacity for independent problem solving and self-directed activity]. This program is intended by the authors for estimating the accomplished results with the use of any textbook and course in any subject on condition of changing test materials for such a factor attribute as the quality of knowledge in the subject. This teaching methodology is more flexible and means developing a sensitivity to students' current abilities.
Aspect-Based Sentiment Analysis on Movie Reviews

Voice of the Customer (VoC) has gained traction over the past few years to understand the consumers' opinion, preferences, and expectation. Reviews that are posted online are one of the methods of communication between the company and the consumers. Therefore, companies can analyse the reviews posted online to identify the aspects and sentiments that are mentioned in the reviews. However, the process of analysing the reviews manually is inefficient and is prone to bias. One of the methods of tackling manually analysing is by using machine learning. This process is called aspect-based sentiment analysis, there are many aspect-based sentiment analysis studies and research has been done previously. However, majority of the previous studies focuses on other domains such as product reviews or restaurant reviews. Therefore, this research will focus on the movie industry where movie reviews will be used to train and predict the aspects and sentiment of the movie review using machine learning models. This research will perform both aspect prediction and sentiment prediction on different models. The aspect prediction will be done using Logistic Regression and Decision Tree whist the Sentiment Analysis will be done using Logistic Regression and Multinomial Naïve Bayes. Based on the findings of the study, Decision Tree was able to achieve a higher accuracy of 98% while Logistic Regression was able to score an accuracy of 92%. Additionally, Logistic Regression was able to score a better accuracy for Sentiment Prediction with an accuracy of 93% when compared to Multinomial Naïve Bayes which achieved an accuracy of 91 %. Therefore, Decision Tree is more suitable for Aspect Prediction whilst Logistic Regression is more suitable for Sentiment Analysis.
Cryptanalysis of Zhu et al.’s Identity-Based Encryption With Equality Test Without Random Oracles

Recently, Zhu et al. proposed a new identity-based encryption with equality test (IBEwET) in the standard model (IEEE Access, 2023). According to the authors, it was claimed that their proposed construction achieves the indistinguishability against adaptive identity and adaptive chosen ciphertext attacks (IND-ID-CCA) by adversaries who do not have trapdoors for equality tests and the one-wayness against adaptive identity and adaptive chosen ciphertext attacks (OW-ID-CCA) by adversaries who have trapdoors. In this paper, we propose adaptive chosen ciphertext attacks against Zhu et al.’s construction that break the IND-ID-CCA security and the OW-ID-CCA security. Subsequently, we discuss how to fix their scheme so that it achieves the security requirements, as claimed in the original paper, however we confirm that a simple modification is no longer superior to the currently existing IBEwET schemes over bilinear groups in the standard model, obtained by generic constructions for IBEwET. Furthermore, we point out another issue that some operations in the original encryption algorithm are incompatible.
Trusted Operations of a Military Ground Robot in the Face of Man-in-the-Middle Cyberattacks Using Deep Learning Convolutional Neural Networks: Real-Time Experimental Outcomes

Safe and secure operations of robotic systems are of paramount importance. Aiming for achieving the trusted operation of a military robotic vehicle under contested environments, we introduce a new cyber-physical system based on the concepts of deep learning convolutional neural networks (CNNs). The proposed algorithm is specifically designed to reduce the cyber vulnerability of the Robot Operating System (ROS), a well-known middleware platform widely used in both civilian and military robots. To demonstrate the efficacy of the proposed algorithm, we conduct penetration testing (real-time man-in-the-middle cyber attack) on the GVR-BOT ground vehicle, a military ground robot, developed by the United States Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center. The cyber attack also exploits the vulnerability of the Robot Operating System (ROS) employed in its onboard computer. We collect experimental data and train our CNN based on two different operating conditions, namely, legitimate and malicious conditions. We normalize and convert the network traffic data in the form of RGB or grayscale images. We introduce two different types of windowing techniques, namely, the independent and overlapping sliding epochs to efficiently feed the network traffic data to our CNN system. Our research indicates the efficacy of the proposed algorithm as our proposed cyber intrusion detection system can achieve reasonably high accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq 99$</tex-math></inline-formula> % and substantially small false-positive rates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> 2 % supported with minimum detection time. In addition, we also compare and demonstrate the relative merits of our proposed algorithm with respect to the performance of some well-known techniques, namely, ‘bag-of-features’ and Support Vector Machine (SVM) algorithms.
Data Reduction Network

Multidimensional categorical data is widespread but not easily visualized using standard methods.For example, questionnaire (e.g.survey) data generally consists of questions with categorical responses (e.g., yes/no, hate/dislike/neutral/like/love).Thus, a questionnaire with 10 questions, each with five mutually exclusive responses, gives a dataset of 5 10 possible observations, an amount of data that would be hard to reasonably collect.Hence, this type of dataset is necessarily sparse.Popular methods of handling categorical data include one-hot encoding (which exacerbates the dimensionality problem) and enumeration, which applies an unwarranted and potentially misleading notional order to the data.To address this, we introduce a novel visualization method named Data Reduction Network (DRN).Using a network-graph structure, the DRN denotes each categorical feature as a node with interrelationships between nodes denoted by weighted edges.The graph is statistically reduced to reveal the strongest or weakest path-wise relationships between features and to reduce visual clutter.A key advantage is that it does not "lose" features, but rather represents interrelationships across the entire categorical feature set without eliminating weaker relationships or features.Indeed, the graph representation can be inverted so that instead of visualizing the strongest interrelationships, the weakest can be surfaced.The DRN is a powerful visualization tool for multidimensional categorical data and in particular data derived from surveys and questionaires.
Bayesian Statistics with Python, No Resampling Necessary

TensorFlow Probability is a powerful library for statistical analysis in Python. Using TensorFlow Probabilityâs implementation of Bayesian methods, modelers can incorporate prior information and obtain parameter estimates and a quantified degree of belief in the results. Resampling methods like Markov Chain Monte Carlo can also be used to perform Bayesian analysis. As an alternative, we show how to use numerical optimization to estimate model parameters, and then show how numerical differentiation can be used to get a quantified degree of belief. How to perform simulation in Python to corroborate our results is also demonstrated.
STUDENTS’ PERCEPTIONS TOWARD THE DOMINANT USE OF ENGLISH AS A MEDIUM OF INSTRUCTION AT A UNIVERSITY IN JAMBI

The purposes of this qualitative research were to know what the students perceive, difficulties and students' strategies to overcome challenges listening to lecturers who use mainly English as a medium of instruction. In doing this research, the researcher used a basic interpretive approach. There were 10 participants participated in this research. Data collection techniques are by using interviews. The results of the research showed that the researcher found there is some student who agrees with using English and agree with the terms and condition. The result also indicated that two themes of difficulties were identified, including internal (lack of vocabulary and lack of knowledge) and external (lecturer accents, and lecturer's attitude). This result also revealed that there were three identified students’ strategies to overcome challenges listening to lecturers who mainly use English as a medium of instruction, including self-management (practice, reading the material before learning, less noise or distraction), asking for help (asking friends and asking lecturer), and the benefits of technology (listening to music, opening google translate, and opening dictionary) Keywords: Student, perceptions, Dominant, English, University
THE INCLUSION OF LOCAL CULTURAL CONTENTS IN ENGLISH CLASSES: TEACHERS' PERCEPTIONS

This research is a descriptive study with a quantitative approach using a survey method. This research seeks to analyze and explain the perceptions of English teachers who teach Junior High School students toward the integration of local cultures in English Language Teaching descriptively and supported by percentage figures. The population of this research is all the English teachers of JHS (Junior High Schools) in Jambi City. The total number is 105 teachers. The sample in this study is the representative of English Junior High School teachers in Jambi city. Those are 40 teachers as the participants. The data in this research are collected by sharing a questionnaire on a piece of paper. Based on the findings, Language teachers normally think that local culture inclusion is really helpful to create a cheerful classroom, to make learners feel comfortable in the classroom and motivate them to persist with the class. In this point, learners’ local culture assists English teachers as they share some sense of interest and humor.
Adversarial AI threats on digitization of Nuclear Power Plants

&lt;p&gt;The future digitalization of Nuclear power plants (NPP) involves uses of sensors data in digitalize formats and analysis by AI based techniques. The planned and existing network architectures used by U.S. nuclear facilities, particularly in high-security zones near the reactor cores, typically rely on a private intranet that connects multiple computers in a peer-to-peer (P2P) or similar network configuration. These intranets are considered highly secure due to their isolation from the outside world (Internet) and the implementation of data diodes to regulate one-way data flow. Additionally, these facilities employ various security measures, including physical security, authorized access, pre-screened employees, antivirus software, and supply chain verification. While these "air gap" systems and their security frameworks are effective in protecting against traditional threats like malware, ransomware, trojans, and intrusion detection, they may overlook a growing vulnerability related to AI models within these air gap systems. AI models or decision systems utilized within nuclear facilities collect sensor data through the secure network and make critical decisions. However, despite the network's robust security measures, the threat to AI models posed by adversarial attacks such as troj-AI, evasion-based attacks, backdoor attacks, and pre-trained poisoned attacks is often ignored by conventional virus scanners. These attacks exploit the vulnerabilities of AI models and can be difficult to detect without domain-specific knowledge related to the model data. As the security of nuclear power plants is paramount, it is crucial that we proactively scan and monitor AI models used in different sectors of these facilities. Unfortunately, there is currently no established framework to monitor and scan the behaviors and architectures of AI models, which poses a significant vulnerability for nuclear power plants. To ensure the comprehensive security of nuclear facilities, it is necessary to address this gap by developing specialized frameworks and mechanisms to monitor and assess the security of AI models. These frameworks should be capable of detecting and mitigating adversarial attacks targeting AI models, providing an additional layer of protection alongside existing security measures. By proactively addressing this emerging threat, we can enhance the overall security posture of nuclear power plants and better safeguard against potential risks.&lt;/p&gt;
Practical Medical Image Generation with Provable Privacy Protection based on Denoising Diffusion Probabilistic Models for High-resolution Volumetric Images

&lt;p&gt;Local differential privacy algorithms combined with deep generative models can enhance secure medical image sharing among researchers in the public domain without central administrators, but those were limited to the generation of low-resolution images, which are very insufficient for diagnosis by medical doctors. To enhance the performance of deep generative models so that they can generate high-resolution medical images, we propose a large-scale diffusion model that can, for the first time, unconditionally generate high-resolution ($256\times 256 \times 256$) volumetric medical images. We specifically adopted head magnetic resonance images, but we trained the model without knowledge intrinsic for medical images; hence, we can apply the models to natural images. This diffusion model has 19 billion parameters, but to make it easy to train it, we temporally divided the model into 200 submodels, each of which has 95 million parameters. Moreover, on the basis of this new diffusion model, we propose another formulation of image anonymization with which the processed images can satisfy provable Gaussian local differential privacy and with which we can generate images semantically different from the original image but belonging to the same class. We believe that the formulation of this new diffusion model and the implementation of local differential privacy algorithms combined with the diffusion models can contribute to the secure sharing of practical images upstream of data processing.&lt;/p&gt;
Review on Sentiment Analysis of Marathi Language of Maharashtra

Abstract: Sentiment analysis on numerous Regional languages is performed, and classification algorithms based on Lexicon, Dictionary, and Machine Learning are employed. Because of the widespread usage of social media platforms, people are rapidly turning to the internet to find and discuss information, thoughts, opinions, feelings, perspectives, facts, and suggestions, resulting in a plethora of user-generated emotion enormous amounts of text data available for analysis. A large number of individuals in India express themselves in multiple languages, resulting in a massive amount of Natural Language Processing text data for (NLP) researchers. Sentiment Analysis (SA) of code-mixed text provides valuable information in politics, education, services marketing, business, health, sports, and other sectors. Work on Indian Language Sentiment Analysis Textual Data, particularly in Hindi, has gained steam in the previous decade in comparison to code-mixed Indian language text. However, due to a lack of language and vocabulary (linguistic and lexical) tools and annotated resources, the process of Sentiment Analysis of Regional Languages becomes very difficult. The goal of this research was to present a complete summary of the Sentiment Analysis of Regional languages, with a focus on code-mixed Regional languages.
Cluster Analysis of Covid-19 in Indonesia Using K-means Method

These days technology are rapidly increasing and developing in various fields, especially data storage. The information that has been stored in a database usually called a dataset. Covid-19 is a new type of respiratory disease that attacks the respiratory system with rapid transmission, followed by the increasing number of Covid-19 cases that continues to increase every day in all provinces in Indonesia. This study aims to cluster the spread of Covid-19 in every province in Indonesia by using the data that obtained from the website named kaggle with many data variables. The method used in this research is K-Means. From many variables in the data, for this study only 3 variables were taken, which are: Number of Recovery, Number of Deaths, and Number of total Cases in Covid-19 in Indonesia. These 3 variables then will be applied using the K-Means method and formed 3 provincial groups. By using the clustering method and the K-means algorithm, this research can be carried out to find the characteristics of the distribution in each province in Indonesia by looking at the best clusters.
Comparison Analysis of Native Database Design with Object Oriented Design

Database design requires a structured database design, because the database contains data or information. The design method of the database design determines the structure of the designed design. Database design have two methods, either native or object-oriented method. Native database design has two stages, it is Data Flow Dia-gram and Entity Relationship Diagram, where as if it is object-oriented design using use case diagrams. It is ac-companied by class diagrams. Native designs tend to be more unstructured than object-oriented design. Native design focuses more on entity flow while object-oriented design focuses on database design entities. Another ad-vantage of using object-oriented design is the ease of explaining the database design to the client because of the simple design so that it can be easily understood. The method used in this research is prototype and relational algebra. The prototyping method is a technique to collect certain information about the user's information needs appropriately. This research focuses on comparing the native and object-oriented design.
An Expert System for Diagnosing the Impact of Traffic Accidents using the Forward Chaining Method

Unexpected events that we often hear about are traffic accidents caused by many factors. Accidents also cause impacts in terms of health. This study aims to provide information regarding the effects of traffic accidents in terms of health based on some visible symptoms that emerged from the victim's body at the scene using an expert system. The Expert System is designed on a website-based application. The forward chaining method is used to get a conclusion based on the facts. The results of this research users gain knowledge about the impact of traffic accidents and the diagnosis on the victim's body that is close to the knowledge of experts with accuracy 87.5%. The website is designed to be used as a guide for users to be able to provide appropriate first aid to accident victims.
Classification of Sleep Disorders Using Random Forest on Sleep Health and Lifestyle Dataset

This study aims to classify sleep disorders using the Random Forest method on the Sleep Health and Lifestyledataset. This dataset contains information about sleep, lifestyle, and relevant health factors. In this study, thedataset was processed and divided into training and testing subsets. The Random Forest model was trained usingthe training subset with sleep and health related features. The quality of the split in each decision tree wasmeasured using the Gini Index. The model was evaluated using the testing subset to measure its accuracy andclassification performance. The evaluation results showed that the Random Forest model was able to predictsleep disorders with good accuracy. Analysis of class distributions, correlation relationships between features,and visualization by gender provided insights into the factors that influence sleep disorders. This research has thepotential to contribute to the field of health and medicine, especially in the recognition and diagnosis of sleepdisorders.
Generating of hamilton full favoring apportionments

Aspects of full favoring of small beneficiaries in apportionments using Hamilton method are discussed. For this purpose, the requirements of full favoring Hamilton apportionments were defined and the AHS algorithm for determining such apportionments is described. Then, calculations which confirm the opportunity of using the AHS algorithm in this aim were performed.
Visual Place Recognition Model using Deep Learning with Arithmetic Optimization Algorithm

Visual Place Recognition (VPR) has engrossed the interest of many researchers in several domains like robotics and Computer Vision (CV). It is the process of identifying a location visited previously, depending on visual input like videos or images. DL techniques have witnessed a potential to solve this task. One method for VPR utilizing deep learning is Convolutional Neural Networks (CNNs) to extract features in visual input. The CNN is trained on a large dataset of videos or images, with each video or image corresponding to a diverse location. The CNN extract discriminative features for each location, allowing it to detect previously visited locations depending on their visual appearance. Therefore, this study presents an Arithmetic Optimization Algorithm with Deep Learning-Driven Robust Visual Place Recognition (AOADL-VPR) technique. The AOADL-VPR technique's purpose is to recognise the visual places using the DL model properly. In the AOADL-VPR technique, Gaussian Filtering (GF) based pre-processing is performed to remove the noise. Meanwhile, the MobileNet-v2model is utilized for generating a feature vector set.Furthermore, the AOA is exploited to adjust the hyperparameter values of the MobileNet-v2 model.At last, Minkowski Distance is exploited for effectual similarity measurement between two images, thereby recognising the places.A series of experimental analyses can be performed to ensure the improved performance of the AOADL-VPR approach.The simulation outcomes portrayed the enhancements of the AOADL-VPR system on the place recognition process.