Reviewerly/exHarmony huggingface dataset:
A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communications network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.
Development of online directory of Islamic higher education in Java Island

Currently, we have entered the industrial era 4.0 where technology and information are developing very quickly so that a lot of data can be obtained easily by the public, but the credibility of this information cannot be accounted for because the source of information is unclear. It is known, with these problems and supported by sophisticated technology, a new search medium has emerged which is called an online directory. The contents of online directories include important information such as names of organizations, institutions, or individuals, addresses, telephone numbers, as well as credible sources. Many people, especially high school graduates, and the equivalent want to continue their education in tertiary institutions, one of which is an Islamic university. Thus the online directory of Islamic universities in Java made by this author can be used as a reference for people who wish to continue their education at Islamic universities in Java but users also need to find further information through the website link that is already listed in the directory because the information is in the directory is not 100% complete.
Preliminary Study on Speaker Intimacy Focusing on Topic Continuity

In recent years, several studies have been conducted to estimate and quantify speaker intimacy based on information obtained from dialogue to improve the usability of dialogue systems. These studies have identified linguistic features (e.g., the presence or absence of honorifics) that contribute to estimating speaker intimacy. However, because these features can only superficially recognize intimacy, intimacy estimation based solely on these features may be less robust. Therefore, this study searched for features that could capture speaker intimacy without being limited by factors such as speaker attributes. In particular, we focused on topic continuity based on the similarity of topics between utterances rather than on-topic content. The results suggest that these features contribute to the estimation of speaker intimacy.
Analisis Kemampuan Menulis Peserta Didik Kelas I Sekolah Dasar

This research is a classroom action research, where the study in this journal is one of the elementary schools in Batanghari Regency, Jambi Province. The purpose of this research is to look at the writing skills of the class and what efforts we will make to improve students' writing skills. The research was carried out on class I students at SDN 95/I Olak with the number of students studied, namely there were 15 people. With this research, we really hope that it will have a better impact in the future for students, especially in the part that our team researches about students' writing abilities. The method used in this research is a qualitative method. The purpose of using this method is to describe the results of the data in the form of values obtained by students. Within this scope, class I students at SDN 95/I Olak were examined by observation or observation, as well as interviews with related teachers. In the early stages of the research, a pretest was carried out by writing the full name of each student in a post-it. At the beginning of the test there were 8 students who could write their full names besides that they could only write their nicknames and there were even 2 students who could not write their names except copying.
Analyzing the Performance of Bitcoin to Gold Prices, the Telecommunications Market, the Stock Price Index, and Insurance Companies’ Performance from (March 1, 2021–September 4, 2023)

Managing cryptocurrencies by financial intermediaries offer numerous benefits to global financial markets and the economy. Among all cryptocurrencies, Bitcoin stands out with the highest market capitalization and a weak correlation to other assets, making it an attractive option for portfolio diversification and risk management. This research aims to examine the impact of Bitcoin on the NASDAQ gold price (GC), the telecommunications market (IXUT), and insurance company performance (IXIS) through the analysis of secondary data from March 1, 2021, to September 4, 2023. The data were obtained from https://www.investing.com; statistical software E views applied various econometric methods to the data. The results suggest a positive correlation between Bitcoin and the other variables, indicating that Bitcoin can significantly expand investment opportunities and drive economic growth. This study highlights the importance of considering cryptocurrencies, especially Bitcoin, as a viable option for investment diversification and risk management in financial markets.
Forecasting TAIEX and FITX with Affirmative and Doubtful Investor Sentiments

Abstract The existing literature have used media messages as a proxy variable for investor sentiment, but they mainly classify sentiment into positive or negative categories based on the words used in news articles, without much attention to the degree of affirmative or doubtful conveyed by the words used. Thus, in addition to classifying news content into positive or negative sentiment, this study also measures the degree of affirmative or doubtful expressed in the news articles in order to achieve more accurate predictive results. The study converts qualitative text to two quantitative scores (sentiment ratio and affirmative ratio) and investigates the predictive ability of these two variables on stock index returns and volatility in Taiwan’s case. The empirical findings indicate that only affirmative ratio exhibits a significant and negative impact on the one-day ahead returns of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Taiwan Stock Exchange Index Futures (FITX). Additionally, the volatility of returns in both future and spot markets is significantly influenced by both sentiment ratio and affirmative ratio. JEL Classifications: C22; G10; G40. Keywords: Investor sentiment; Text-mining; TAIEX; FITX; Affirmative ratio.
SMART DOCUMENT CLASSIFICATION

Smart document classification using Naive Bayes is a method used for organizing large volumes of unstructured data into categories or classes. This method uses the Naive Bayes algorithm's probabilistic approach to determine the likelihood that a text falls into a specific category based on the frequency of its words. A machine learning approach called Naive Bayes is used to forecast the likelihood of a particular category depending on the characteristics of the document. For text classification applications including spam filtering, sentiment analysis, and topic modeling, this method has been frequently employed. In this method, the algorithm is trained on a labeled dataset, which contains a set of documents and their corresponding categories The algorithm can be used to categorize fresh documents into the appropriate categories after it has been trained. The standard and applicability of the features used to represent the documents will determine how accurate the classification is. The Naive Bayes classifier is a popular option for smart document categorization since it is simple to use and needs little training data to attain high accuracy. Overall, automatic document classification using naive Bayes is an effective and efficient approach for organizing and managing large volumes of textual data. Key Words: classification, Naïve Bayes, machine learning, textual data
Effective suppression of dark counts in superconducting microstructures with grid of pinholes in a magnetic field

Abstract In a magnetic field, vortices significantly contribute to the dark counts of single-photon detectors made of superconducting wires, and they are also limiting the critical current of such devices. To address this issue, we prepared superconducting microwires with a pinhole grid from WSi thin films and report on corresponding critical-current and count-rate measurements in an external magnetic field B . When compared to corresponding devices without pinholes, the critical current only weakly depends on the magnetic field at B &lt; 16 mT and it is even larger already at B &gt; 10 mT. Moreover, dark counts are not only suppressed in zero field, but particularly in magnetic fields B &lt; 16 mT, while photon counts are virtually field insensitive in the same range of the magnetic field.
The Influence of Self-efficacy on Organizational Commitment and Ethical Behavior: The Role of Job Satisfaction

Objective: This study aims to analyze the effect of self-efficacy on organizational commitment and ethical behavior. Furthermore, this study also examines the mediating role of job satisfaction in determining the relationships. Design/Methods/Approach: This study was carried out on healthcare and administrative staff. The sample size and respondents were determined using the Slovin formula and selected through random sampling. The data collected was analyzed using PLS analysis. Findings: The findings reveal that self-efficacy positively and significantly affects ethical behavior and organizational commitment. Additionally, job satisfaction plays a role in mediating the connection between self-efficacy and organizational commitment. Similarly, the analysis shows that job satisfaction mediates the link between self-efficacy and ethical behavior. Originality: The novelty of this study lies in the influence of self-efficacy on organizational commitment and ethical behavior. Previous studies mainly analyzed the influence of self-efficacy on other variables, in which none of them had combined the two variables, along with job satisfaction as a mediation variable. In particular, this study is novel as it analyzes job satisfaction for workers in a Regional General Hospital, in which prior studies only focused on other non-health institutions or companies. Practical/Policy implication: Optimizing communication and institutionalizing ethical and moral values will improve workers' self-efficacy. Institutions should focus on maintaining job satisfaction, including work, rewards, supervision, co-workers, and promotion opportunities, to promote employee commitment and ethical behavior.
DESAIN TATA KELOLA REKAM MEDIS ELEKTRONIK KUNJUNGAN PASIEN UNIT GAWAT DARURAT GUNA MENUNJANG PELAPORAN KUNJUNGAN PASIEN DENGAN V-MODEL

This research aimed to determine of patients’ visit information system in emergency unit using V-Model. In this research method used is qualitative method with descriptive approach. Data collection techniques used is by observations, interviews, and literature studies which had relation to the subject. Software methods used is V-Model. From the research and observations made there were problems in the reporting section of the visit, namely the processing of patient visits an emergency unit is still done by looking at the data from the registry books patient emergency unit and data to be processed using Microsoft Excel so it took a long time. Suggestions given were by: 1) providing a computer unit at the emergency unit in particular for reporting of patient visits did not to be done manually, and 2) designing information system which help job quickly and accurately. &#x0D; Keywords: Designing, Information System, Patients Visits, Emergency Unit, V-Model.
Text-to-Image Generation using Generative AI

Abstract—This survey reviews text-to-image generation by using different approaches. One of the approaches identified in this study is Cross-modal Semantic Matching Generative Adversarial Networks (CSM-GAN), which is used to increase semantic consistency between text descriptions and synthesised pictures for fine-grained text- to-image creation. This includes other two modules, Text Encoder Module and Textual-Visual Semantic Matching Module. We further discussed about Imagen which is a text- to-image diffusion model with photorealism and deep language understanding, which is used on the COCO dataset. Lastly, we discussed about Text to image synthesis used to automates image generation using conditional generative models and GAN, enhancing artificial intelligence and deep learning. Based on these approaches we present a review of text to image generation using generative AI. Keywords— Generative AI, Diffusion model, Text-to- image, Imagen, CSM-GAN
On the Design of Maze Wanderer Robot

This paper focuses on designing, programming and implementing a maze wonderer robot that takes the commands todictate its direction of motion based on the obstacle avoidance. A program is to be written to give the robot its intelligence. Thepaper will as well be restricted to a motorized car which will be given the intelligence to be able to navigate through a given maze.
ANALISIS KEAKURATAN KODIFIKASI DIAGNOSIS TERHADAP KUALITAS LAPORAN MORBIDITAS RAWAT JALAN (RL 4b) DI RUMAH SAKIT UMUM HERMINA PASTEUR BANDUNG

The guidelines for writing major diagnoses have an impact on how they are coded; each diagnostic statement must be clear and informative in order to place the disease in the ICD-10 category with the highest level of specificity. The accuracy of the diagnosis, the readability of the doctor's writing, the professionalism of the doctor and the coding, and other factors all influence the quality of the coding findings. The literature review was used as the research design. The codification analysis has not been carried out ideally so that there is still inaccurate code and makes the reports that have been made less accurate and seen from the preparation and delivery of reports that have been carried out on time so that the level of relevance of the report is 92%.&#x0D; Research analysis on the accuracy of codification of diagnoses on the quality of outpatient morbidity reports (RL 4b), based on a sample of 100 medical records examined, 100 medical records that were accurate 60 or 60% and 40 medical records that were inaccurate or 40%, Reliability test results with using the Cronbachs Alpha method with a significance level of 10% (0.1), namely 0.923, was then taken into consideration. This value is greater than the r table value of 0.4409 which indicates that the reliability test was successful and the questionnaire results are reliable.&#x0D; .Keywords: Codification; diagnosis; Medical records
ANALISIS KEBUTUHAN RAK FILE DOKUMEN REKAM MEDIS GUNA MENUNJANG EFEKTIVITAS PELAYANAN DI RUMAH SAKIT UMUM PUSAT DR HASAN SADIKIN BANDUNG

The aim of the study was to find out the need for medical record document file shelves to support the effectiveness of services at the Dr. Central General Hospital. Hasan Sadikin Bandung.The research method used is descriptive method, namely a research conducted to describe or describe a phenomenon that occurs.Based on the results of research and observations the authors found that: 1). data on the number of patients increases each year, 2) The need for shelves according to the anthropometric data filling is 272 shelves, 3) There has been a shortage of medical record storage shelves since 2019 so that files have piled up and many DRMs have been damaged, 4) The service has not been effective this is because the unit medical records have not been maximized in carrying out DRM storage because the available shelves are not sufficient, the shelf design is too high and not suitable for filling anthropometric data so that it can cause work accidents.The suggestions that the author conveys include the Central General Hospital Dr.&#x0D; Keywords: Workload Analysis, Effectiveness
A Rotation &amp; Scale Invariant CNN Model to Detect Humans in Disaster Area from Aerial View

Human detection in aerial images is incredibly challenging because of the characteristics aerial images possess. One of the main applications of human detection in aerial images is for search and rescue operations after a disaster. An efficient human detection approach will hugely optimize the search and rescue operation, which helps reduce the loss of lives due to disasters. A custom dataset has been made for this study using an Unmanned Aerial Vehicle (UAV) or drone. The dataset is made to simulate real-life disaster scenarios. Various types of preprocessing are done on the dataset considering the wide range of aerial images, i.e., different scales, orientations, and postures of the human body cover different areas in aerial images. A new convolutional neural network (CNN) based model has been proposed for classification in this paper. The model has been made both scaling and rotation invariant. A comparative study has been done among 12 pretrained models and compared to the proposed model. You Only Look Once (YOLOv3) has been used for transfer learning for the task of localization. The proposed classification model has shown satisfactory results with accuracy as high as 94.18%.
IMPROVING STUDENTS SPEAKING SKILLS THROUGH DEBATE STRATEGY

In this research, the authors utilize the debate technique to improve students' speaking ability. The main goals of this study were to describe the implementation of the debate technique in teaching speaking and to identify how much the students' speaking ability was improved after being taught by using the debate technique. This study is classroom action research conducted in two cycles. Data were collected using observation, tests, and interviews. Data from observations, tests, and interviews taken from each cycle were analyzed quantitatively and qualitatively. The application of the debate technique in teaching speaking in class XI IPS 1 the high school in Maumere was conducted in two cycles, namely cycle one and cycle two. The participants of this study were 16 students. The result of this study shows that the use of debate techniques can improve students' speaking skills. This is evidenced by the students' test scores which have increased in each cycle. In the first cycle, the student’s average score was 54.37% and in the second cycle, the student’s average score was 69.37%. It can be inferred that the debate technique can increase students' interest in speaking and can also upgrade the class to be more favorable
Detection of OFDM modulations based on the characterization in the phase diagram domain

Signal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments.
P125/278 Application of 3-D vascular model augmented reality in endovascular treatment of intracranial aneurysms

<h3>Introduction</h3> Safe and complete treatment of intracranial aneurysm requires a thorough understanding of the complex three-dimensional vascular structure specific to each patient. We introduced a 3D vascular model mixed reality (MR) in endovascular treatment to enhance anatomical confidence. <h3>Aim of Study</h3> This study aims to validate the usefulness and feasibility of a MR application in the endovascular treatment of intracranial aneurysms. <h3>Methods</h3> Developed an application software called Object Loader using the Unity program, enabling the upload and manipulation of a 3-D vascular model on the Hololens. From September 2021, endovascular procedures were performed on consecutive patients with intracranial aneurysms, utilizing MR assistance. A score system composed of total score 7 was employed to evaluate the merits of MR application in each case. <h3>Results</h3> Between September 2021 and March 2023, 54 MR-assisted endovascular treatments were performed on 51 patients. No immediate complications were observed. Favorable occlusion was achieved in 80% of cases. Discrimination of overlapped vasculature and rechecking dangerous regions were the most useful merits. <h3>Conclusion</h3> In conclusion, the utilization of mixed reality with a 3-dimensional vascular model digital twin enhanced the anatomical confidence and safety of endovascular procedures for the treatment of intracranial aneurysms. The discrimination of overlapped vascular structures and real-time alarming of the physician regarding dangerous areas during the procedure demonstrated the usefulness of this technology. The continuous evolution of augmented reality has the potential to revolutionize daily procedures. <h3>Disclosure of Interest </h3> nothing to disclose
Effective Mean Square Differences: A Matching Algorithm for Highly Similar Sheet Metal Parts

The accurate identification of highly similar sheet metal parts remains a challenging issue in sheet metal production. To solve this problem, this paper proposes an effective mean square differences (EMSD) algorithm that can effectively distinguish highly similar parts with high accuracy. First, multi-level downsampling and rotation searching are adopted to construct an image pyramid. Then, non-maximum suppression is utilised to determine the optimal rotation for each layer. In the matching, by re-evaluating the contribution of the difference between the corresponding pixels, the matching weight is determined according to the correlation between the grey value information of the matching pixels, and then the effective matching coefficient is determined. Finally, the proposed effective matching coefficient is adopted to obtain the final matching result. The results illustrate that this algorithm exhibits a strong discriminative ability for highly similar parts, with an accuracy of 97.1%, which is 11.5% higher than that of the traditional methods. It has excellent potential for application and can significantly improve sheet metal production efficiency.