Reviewerly/exHarmony huggingface dataset:
Advances in quantum computing: A comprehensive review of quantum algorithms and applications in physics

Quantum computing, an innovative fusion of quantum mechanics and computer science, has emerged as a transformative force in the field of physics research. This comprehensive review article explores the advancements in quantum computing, focusing on quantum algorithms and their applications in various areas of physics. The review begins with an introduction to the fundamentals of quantum computing, elucidating the principles of qubits, quantum gates, and quantum algorithms. Subsequently, the article delves into the latest developments in quantum algorithms, including Shor&#39;s algorithm for factoring large numbers, Grover&#39;s algorithm for quantum search, and quantum phase estimation for simulating quantum systems. Furthermore, the review explores the practical applications of quantum computing in physics, encompassing quantum simulations of complex systems, quantum chemistry calculations, and quantum optimization for problem-solving. Additionally, the article presents case studies that demonstrate the practical implications of quantum computing in specific physics applications. The review also addresses the challenges and prospects of quantum computing in physics, including quantum error mitigation, hardware scalability, and quantum-classical interfaces. Ethical and societal considerations related to quantum computing&#39;s cryptographic impact, access, and responsible development are also discussed. Furthermore, the review explores the future directions of quantum computing in physics, from quantum supremacy to quantum machine learning and quantum gravity. The article concludes with reflections on the transformative potential of quantum computing in physics research and the importance of collaborative efforts, interdisciplinary research, and responsible development in shaping the future of quantum technologies. Overall, this comprehensive review offers valuable insights into the current state of quantum computing in physics and the exciting possibilities that lie ahead for unravelling the mysteries of the universe.
The formalization of interlanguage: the example of object clitic pronouns acquisition in French L2*

The interlanguage of second and foreign language French learners presents certain patterns that offer a formal framework within the context of the Applicative Combinatory Categorical Grammar. These patterns manifest in the acquisition of object clitics which presents considerable difficulties for French second language learners. To address this, we thoroughly analysed the object clitic pronouns that appeared in a corpus of texts by examining the different facets of these pronouns. A formal analysis was then proposed. The objective of this research is to establish an automated system for processing learners’ interlanguage in terms of the ACCG.
English Performance of Grade 3 Learners of Tandang Sora Elementary School

The study aimed to identify the English performance of Grade 3 learners in Tandang Sora Elementary School for the school year 2021 – 2022.
TO THE PROBLEM OF PROJECT LEARNING IN MODERN RUSSIAN HIGHER EDUCATION

The article outlines the relevance, goals and objectives of the work. Particular attention is paid to the changes in the educational sphere of the Russian Federation. This undoubtedly implies the importance of understanding the relationship between traditional teaching skill (experience) and new ideas in the conditions of modification, reforming of system relations in society, including in the educational sphere, and qualitative expansion of alternative educational opportunities for citizens of our country. The choice of the topic under study was determined by the need to know what new vectors affect the central processes in the educational sphere. The object of this study was the system of higher professional education. The subject of the study is a set of factors that predetermined project-based learning as an innovative basis for additional knowledge, experience through a system of independent research, analysis, evaluation set before the scientific research tasks. The purpose of the study was designated by the task of briefly defining the main innovative directions of project-based learning in the system of modern higher education. The set research tasks were solved on the basis of analysis and study of existing and hypothetical pedagogical innovations, and state regulatory documents governing the activities of the educational sphere. The methodological basis of the study is built on the principles of analytical, comparative, comparative and logical approaches. The basis of the analysis of the material was the problem and empirical methods, used the values of the ratio of the general and the specific, fact and theory in the study. The essence of the concept of "project-research work of a student", criteria of the subject-content side of a research project were analyzed, the basis of the concept of "systematic design" and optimal terms of project implementation were considered, the most acceptable requirements for the implementation of student research works were analyzed. The prospects of implementation, development and final values of innovative technologies of student's project research are determined. The paper presents the author's personal view on the specifics of student research papers taking into account the basic requirements for the quality of training of young professionals
Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System

Recommender systems (RSs) are personalization tools that reduce information overload and help in decision-making in many real-life situations. RSs are applied in various domains for recommending products and services. Healthcare recommender systems (HRSs) are designed to recommend various health-related services like drug recommendations, doctor recommendations, and disease identification by analyzing the health indicators of the people. During the COVID-19 pandemic, there was a severe shortage of doctors and healthcare facilities, which necessitated the development of intelligent techniques to handle such situations. In this work, a personalized HRS is developed based on the intuitive premise that patients with comparable diseases and health conditions may be exposed to the same risk factors. The developed HRS takes the disease symptoms and previous health records of a patient and matches them with the symptoms of other patients to identify similar patients. After finding similar patients, the HRS recommends the treatment to active patients based on the medicine or treatment prescribed to similar patients in the past. Multiple experiments have been conducted to signify and validate the usefulness of the developed recommender system.
Indonesian Sentiment Analysis towards MyPertamina Application Reviews by Utilizing Machine Learning Algorithms

This paper is a report of experiment analysis on sentiment analysis in application review that explored the methods and the data. Application review contains a large amount of raw data that has been published by users in the form of text, image, audio, and video. The data can be converted into valuable information by using sentiment analysis. In this work, around 5000 Indonesian review in MyPertamina google play application are analyzed. The goal of this study was to investigate the effectiveness of using sentiment analysis to extract valuable insights from application reviews. Some techniques were applied during this work, such as data collection, pre-processing, feature extraction, TF-IDF text representation, machine learning modelling, and evaluation phase. The machine learning algorithms that we used are Linear Support Vector Classification (Linear SVC) and Multinomial Naive Bayes (Multinomial NB). The result shows both machine learning models present good performance in this data. The accuracy of Multinomial NB reaches 95%, while Linear SVC obtains 96% of accuracy. The results of the experiment suggest that both Linear SVC and Multinomial NB are well-suited for sentiment analysis tasks on Indonesian language data. Future work could include expanding the dataset to include reviews from a broader range of applications, or evaluating the performance of additional machine learning algorithms. In addition, word cloud analysis also performed in this experiment. The word cloud shows that positive and negative sentiment present some popular words which appear inside the review. It would also be interesting to conduct a deeper analysis of the word cloud results to identify common themes and trends in the positive and negative sentiments expressed in the reviews
Attitude of Undergraduate Students in Rural Colleges of Kashmir Valley towards Experiential Learning

Experiential learning, which is the process of learning through direct experience and reflection, is a core component of contemporary education system. The present study attempted to explore the attitude of undergraduate students in rural degree colleges of Kashmir valley in the state of Jammu &amp; Kashmir, towards experiential learning. Altogether, 150 undergraduate students pursuing bachelor’s programme in various rural government degree colleges, were identified for the study through randomization. The 25 item- questionnaire was administered to measure the attitude towards experiential learning. Various statistical techniques and graphical representations such as mean, standard deviation, percentage, 2x2 factorial design were employed. The study established that attitude towards experiential learning differs significantly with respect to gender and academic stream among the undergraduate students.
Implementasi Pembelajaran Teknologi Informasi Berbasis Augmented Reality di Sekolah Menengah Kejuruan

&lt;p&gt;&lt;em&gt; Augmented Reality&lt;/em&gt; (AR) is a technology that combines virtual objects with real objects. One of the fields that uses AR technology is the field of education, especially for learning information technology, computer installation materials. used as a learning aid to make students better understand the material provided. This study describes the use of AR technology in learning about the human excretory system using AR technology while aiming to increase students' digital literacy and optimize the use of digital media in the learning process. In making learning applications using the AR method, the authors use the Marker Based Tracking AR method. The purpose of this research is to make it easy for students to learn and understand the material, so that learning becomes more interesting and not boring. In this application each object is displayed with a visual image in 3D using animation, sound and attractive colors. The research methods in this study are literature studies, field studies, and sample calculations. The results of this study are mobile applications that are used as a supporting tool to assist the learning process.&lt;/p&gt;
Modelling Adverse Events with the TOP Phenotyping Framework

The detection and prevention of medication-related health risks, such as medication-associated adverse events (AEs), is a major challenge in patient care. A systematic review on the incidence and nature of in-hospital AEs found that 9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs are considered to be preventable. Adverse events can be identified using algorithms that operate on electronic medical records (EMRs) and research databases. Such algorithms normally consist of structured filter criteria and rules to identify individuals with certain phenotypic traits, thus are referred to as phenotype algorithms. Many attempts have been made to create tools that support the development of algorithms and their application to EMRs. However, there are still gaps in terms of functionalities of such tools, such as standardised representation of algorithms and complex Boolean and temporal logic. In this work, we focus on the AE delirium, an acute brain disorder affecting mental status and attention, thus not trivial to operationalise in EMR data. We use this AE as an example to demonstrate the modelling process in our ontology-based framework (TOP Framework) for modelling and executing phenotype algorithms. The resulting semantically modelled delirium phenotype algorithm is independent of data structure, query languages and other technical aspects, and can be run on a variety of source systems in different institutions.
Concept Graphs: A Novel Approach for Textual Analysis of Medical Documents

The task of automatically analyzing the textual content of documents faces a number of challenges in general but even more so when dealing with the medical domain. Here, we can't normally rely on specifically pre-trained NLP models or even, due to data privacy reasons, (massive) amounts of training material to generate said models. We, therefore, propose a method that utilizes general-purpose basic text analysis components and state-of-the-art transformer models to represent a corpus of documents as multiple graphs, wherein important conceptually related phrases from documents constitute the nodes and their semantic relation form the edges. This method could serve as a basis for several explorative procedures and is able to draw on a plethora of publicly available resources. We test it by comparing the effectiveness of these so-called Concept Graphs with another recently suggested approach for a common use case in information retrieval, document clustering.
Satisfaction and Feasibility Evaluation of an Electronic Massager—Expert Manipulative Massage Automation (EMMA): A Pilot Study

Background: Electronic massagers have gained popularity for their non-invasive and cost-effective approach to healthcare. However, existing electronic massagers require manual manipulation, leading to inconsistency. Objective: To investigate the feasibility and acceptability of the Electronic Massager—Expert Manipulative Massage Automation (EMMA) among the American population. Methods: A single-arm pilot study was conducted at Mayo Clinic Rochester campus. Participants received massage therapy with the EMMA treatment model and completed a demographics and Satisfaction Survey Questionnaire. Data were collected using REDCap, and statistical analysis was performed. Results: Twenty healthy volunteers (13 women, 7 men; median age, 47 years) participated and completed the intervention within 2 weeks. No adverse events were reported. Overall, the acceptability and satisfaction of massage utilizing EMMA were high, with all participants finding the study worthwhile, willing to participate again, and willing to recommend the study to others. Regarding the overall experience, 65.0% reported a better-than-expected experience. Responses to open-ended post-treatment questions showed that most participants enjoyed the warmth (35%), pressure (25%), and relaxation (20%) provided by EMMA, with the majority suggesting no changes or improvements were needed. However, some participants expressed a desire for an expanded massage area (20%). Conclusions: This study suggests that the EMMA massager has high acceptability and satisfaction among healthy volunteers, thus demonstrating its feasibility. However, further research is needed to optimize EMMA and assess its clinical effectiveness.
Counterfactual reasoning in space and time: Integrating graphical causal models in computational movement analysis

Abstract Movement analysis is distinguished by an emphasis on understanding via observation and association. However, an important component of movement from the human and computer modeling perspective is the processes that bring about movement behavior in the first place. This article contextualizes the graphical causal modeling framework (for association, intervention, and counterfactual causal analysis) in GIScience, and more specifically within movement analysis studies. This is done by modeling the movement behavior of football players, applied to spatiotemporal data generated by an agent‐based simulation. The movement dataset is thoroughly analyzed to infer the statistical associations among its variables, to estimate the effect of an intervention on some of those variables, and to answer a few counterfactual questions from the observations. We conclude that causal graphs (i.e., directed acyclic graphs), if implemented correctly, can assist analysts in infering causal relations from movement data. This research suggests the integration of causal graphs and agent‐based paradigms as one solution for computational movement analysis.
QR Food Ordering System with Data Analytics

As the epidemic starts to slow down and Malaysians are more confident about containing the outbreak with the norm of vaccination, diners have been aching to return to dining rooms, with many restaurants functioning at full capacity, but staffing is an entirely different story. As restaurateurs try to keep their businesses running at full speed and solve limited staff issues, there is only one solution: process automation. This paper aims to design a food ordering system that covers the benefits of automating the ordering process using the QR code and provides visualised insightful information based on the business data. Customers place the food order by scanning the QR code on the restaurant table, and it is then brought to a digital version of the restaurant's menu and make orders. The proposed system automates customer bills after the order, and it helps reduce human error in calculating bills. On the other hand, the proposed system has an admin interface that enables restaurant owners to modify the restaurant's menu, generate QR codes for the new dining table, receive orders from customers, and get automated bills generated by customers' orders. Most importantly, the system allows restaurant owners to have an insightful view of their business data such as visualised charts on sales data, highlighted crucial data and so on to improve decision-making and forecasting future demand using data analysis techniques which are not populated in similar systems currently. Machine learning has become a huge trend nowadays, it is also included to in the proposed system to forecast more valuable data for the business.
SSRL: A Safe and Smooth Reinforcement Learning Approach for Collision Avoidance in Navigation

This paper addresses the collision avoidance problem of autonomous robots using reinforcement learning (RL) techniques, with a focus on ensuring both safety and smoothness. To achieve stability guarantees, a data-based Lyapunov function is incorporated into the model-free RL framework for training policies. Furthermore, constraints on action increments and action distributions are introduced, which effectively mitigate the differences between adjacent actions and ensuring smoothness in the learned policies. Subsequently, a safe and smooth reinforcement learning algorithm is proposed for training navigation policies, and its superiority in terms of safety and smoothness are validated by using a ground mobile robot in a simulated environment.
Indoor Navigation Using Augmented Reality For PENS Postgraduate Building

The use of navigation systems is widespread in assisting individuals with finding their desired destination, visualizing the route, and measuring the distance traveled. In the specific context of navigating indoors, the postgraduate building at Politeknik Elektronika Negeri Surabaya lacks real-time digital directions, which can lead to confusion among users. To address this issue, we developed an Augmented Reality (AR) based navigation system that aims to provide a user-friendly solution and eliminate any potential confusion. Our research focuses on the development of an interior navigation system that leverages AR technology to enhance the user experience. We implemented QR codes to establish the initial position of each room, and conducted a confusion matrix test to validate the accuracy of scanning each QR code, resulting in a high accuracy rate of 96.6 percent. These findings confirm the reliability and effectiveness of our navigation system.
A Study on the Effects of Online Video Extracurricular Program Activity Perception on Self-Efficacy and Career Decision among College Students Majoring in Food Service and Culinary Arts

This study attempted to examine the influence of online comparison and program activity perception on self-efficacy and career decision of college students majoring in restaurant cooking, and to present the direction of online comparison and activities. A survey was conducted on college students majoring in eating out and cooking who have experienced online comparative activities. A total of 208 copies were used for the final analysis. The results of this study were as follows: first, it was found that competency promotion recognition, major linkage recognition, and earning opportunity recognition had a positive effect on self-efficacy. Second, as a result of empirical analysis of the influence relationship between self-efficacy and career decision level, it was found that self-efficacy had a positive effect on the career decision level.
Digital engineering audit management strategy based on machine learning combined with wireless communication network

In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform’s functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system’s efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management.
Police and Cybercrime: Evaluating Law Enforcement’s Cyber Capacity and Capability

The COVID-19 pandemic has thrown the international community into disarray, resulting in a significant impact both on the rate at which digital technologies are incorporated into organisations’ processes and on the cyber threat landscape. The pandemic led society and institutions to a global accelerated digitalisation, and in doing so reshaped the landscape in which cybercrime and cybersecurity operate (Horgan et al. in J Crim Psychol 11:222–239, 2020). Furthermore, Russia’s recent invasion of Ukraine has had significant impact on the cyber threat landscape. Therefore, threats posed by cybercrime should be considered as the top priority for research attention, which is foundational to broader research in other fields such as Cyber Security, National and International Security, and Foreign Policy. To this end, this chapter aims to provide a critical analysis of the challenges that police and the wider law enforcement community encounter when responding to cybercrime. In view of this, the chapter also aims to assess law enforcement’s cyber capacity and capability concerning their fight against cybercrime.