Reviewerly/exHarmony huggingface dataset:
Coherent control of the causal order of entanglement distillation

Indefinite causal order is an evolving field with potential involvement in quantum technologies. Here we propose and study one possible scenario of practical application in quantum communication: a compound entanglement distillation protocol that features two steps of a basic distillation protocol applied in a coherent superposition of two causal orders. This is achieved by using one faulty entangled pair to control-swap two others before a fourth pair is combined with the two swapped ones consecutively. As a result, the protocol distills the four faulty entangled states into one of a higher fidelity. Our protocol has a higher fidelity of distillation and probability of success for some input faulty pairs than conventional concatenations of the basic protocol that follow a definite distillation order. Our proposal shows the advantage of indefinite causal order in an application setting consistent with the requirements of quantum communication.
Virtual Reality Collaboration Platform for Agile Software Development

Nowadays, most software teams use Scrum as their software development process framework. Scrum highly values collaboration and communication inside the team and aims to make them more flexible to spontaneous changes. However, due to the rise of working from home, many developers experienced a significant decrease in communication, social interactions, and a general feeling of social connectedness with their colleagues, which might impede the effectiveness of Scrum teams. To overcome these issues, we present a VR collaboration platform for Scrum meetings, called Virtual Reality-based Agile Collaboration Platform (VRACP). VRACP provides the visualization of and interaction with Scrum artifacts inside a realistic virtual office, the integration and synchronization of external data sources, remote collaboration, and human-like user representations. To evaluate whether VR can increase social connectedness in agile software development teams, we conducted a user study where our solution was compared to common web/desktop applications for Scrum meetings. The results suggest that although efficiency and effectiveness were reduced, it could indeed increase the feeling of being together, collaborating more naturally, and having more fun and motivation.
Applications of Blockchain: A Healthcare Use Case

Data transparency, traceability, immutability, flexible access, trust, privacy, and security are the main problems that today’s healthcare data management systems need to tackle. As many of the current healthcare data management systems are centralised, a single point of failure is more likely to occur in the event of a natural disaster. The way data is handled in the healthcare sector might alter significantly. Blockchain is a new, revolutionary, and decentralised technology. We emphasize in this essay how blockchain for healthcare data management systems may foster innovation and lead to substantial breakthroughs. We list the Blockchain’s primary characteristics and features. We discuss the advantages of applying blockchain technology and the possibilities for the healthcare industry.
META-ANALISIS: PENERAPAN E-MODUL BERBASIS EXE-LEARNING DALAM PEMBELAJARAN IPA

This study aims to estimate the application of exe-learning-assisted e-modules in science learning in Indonesia. This meta-analysis research method includes problem formulation, data collection (study), data coding, data analysis, and interpretation. After searching for journals, 16 journals were found that discussed the application of exe-learning-assisted e-modules in science learning. Of these, 6 articles meet the established criteria, namely research using exe-learning-assisted e-modules in science learning in Indonesia. The results of this study revealed that there was a significant positive effect in the application of exe-learning-based e-modules in science learning. The research findings showed that the results of the meta-analysis showed an effect size value of 0.9, which indicates a high level of effectiveness of 82% in improving learning abilities and outcomes.
PENGARUH PENETAPAN WAKTU HUJAN TERHADAP BESARNYA DEBIT BANJIR RENCANA

The aim of this study is to determine the discharge of rainwater runoff from different durations of rain, to determine the magnitude of the differences in the discharge of rainwater runoff produced, and to determine the differences in channel dimensions resulting from differences in the duration of rain used. This study uses 6 years of daily rainfall data recorded at Bengel Station, to calculate the planned flood discharge as an alternative for controlling rain runoff in the Green Asia Housing area with a land area of ±6 hectares, a test time was used with rain times selected as 24 hours and 5 hours. Flood discharge is calculated using the Rational formula with a rain return period of 2 years according to the design area. The results of data analysis will be presented in the form of tables, discussed and concluded descriptively.The results of the study show that there is indeed a quite large difference in flood discharge, where the use of a 24-hour rain period produces a planned discharge that is around 32% lower than the use of a 5-hour rain period. The dimensions of the channel produced for a discharge with a rain duration of 24 hours are also smaller than the dimensions produced by a flow discharge with a rain duration of 5 hours. The fact is that rain events with a duration of shorter than 24 hours are more common than rain events lasting a full day.
The Effect of Roleplay Methods toward Students’ Speaking Skills in Grade XI Senior High School Number 9 Jambi City

This Study aimed to find out whether or not there was any significant effect of roleplay method toward students’ speaking skills in grade XI Senior High School Number 9 Jambi City. This research used quantitative research method with pre-experimental research design which implicated only experimental class. The total population of this research was the eleventh grade students of Senior High School Number 9 which consisted of 240 students. The samples of this research were chosen by using simple random sampling. The samples were only experimental class consisted of 36 students. The technique of distributing data was speaking test where the students have to choose a topic, make a dialogue and do the role playing with pre-test, treatments, and post-test. This research used t test as the data analysis technique with statistical package for the social sciences (SPSS) for Windows Version 21 Program. The result were found on this research are: (1) Students’ speaking skill taught by normal discussion reached the mean score of 6; (2) students’ speaking skill taught by using role play method reached the mean of 8; (3) students’ speaking skill taught by using role play method is higher than the students’ speaking skill taught by using normal discussion method with a value of sig 0.000 &lt;0.05. Therefore, there are no satisfactory results from this method because the t count is obtained with a value of -4,363.
Effect of Spot Color on Extraction Quality of Holographic Watermark

To transplant the digital watermark into the field of packaging and printing anti-counterfeiting, the robustness of watermarks in different color spaces and channels against spot color printing was investigated. Taking holographic watermarking algorithm in discrete wavelet transformation domain as an example, the watermarks were embedded into the B, C and spot color channels of the host image in the form of hologram, and the effects of simulated screening and color space conversion attacks on the invisibility and robustness of the watermarks in different channels were investigated. Experiments shown that although spot color printing improved the PSNR value of the watermarked images, it reduced the robustness of the watermark to the color space changing attack, and the robustness of embedding the watermark in the spot color channel was the lowest. Spot color had different effects on different color spaces and color channels. It was suggested to embed watermark in the non-spot-color channel of CMYK color mode.
Performance of Graph Database Management Systems as route planning solutions for different data and usage characteristics

Graph databases have grown in popularity in recent years as they are able to efficiently store and query complex relationships between data. Incidentally, navigation data and road networks can be processed, sampled or modified efficiently when stored as a graph. As a result, graph databases are a solution for solving route planning tasks that comes more and more to the attention of developers of autonomous vehicles. To achieve a computational performance that enables the realization of route planning on large road networks or for a great number of agents concurrently, several aspects need to be considered in the design of the solution. Based on a concrete use case for centralized route planning, we discuss the characteristics and properties of a use case that can significantly influence the computational effort or efficiency of the database management system. Subsequently we evaluate the performance of both Neo4j® and ArangoDB depending on these properties. With these results, it is not only possible to choose the most suitable database system but also to improve the resulting performance by addressing relevant aspects in the design of the application.
Automated Preselection of Regions of Interest By Deep Learning Facilitates Rapid Whole Slide Image Analysis of Bone Marrow Smears

The analysis of bone marrow smears (BMS) serves as a critical diagnostic tool in hematology, offering valuable insights into the cellular composition and distinctive morphology of bone marrow cells. It enables the accurate diagnosis, classification, and monitoring of various hematologic diseases. Currently, both clinical routine diagnostics and scientific research, especially for the implementation of digital technologies, rely heavily on the manual selection of regions of interest (ROI) suitable for cell-level evaluation. Current guidelines recommend the manual evaluation of up to 500 cells per sample making the process time-consuming, cost-ineffective, subjective, and prone to human error. Consequently, significant inter-observer variability and inconsistencies in diagnosis often arise. Automated analysis of BMS using whole slide images (WSI) offers a promising approach to alleviate financial and time-related burdens while promoting standardization and streamlining of routine diagnostics. We have developed a machine learning approach utilizing a fully convolutional neural network (CNN) specifically designed for semantic segmentation based on the algorithm by Long et al. (IEEE Computer Society, 2015). The model is capable of learning deep semantic representations in its classification backbone combined with shape features in a shallow layer to produce detailed segmentations. The classification backbone consists of an adapted residual neural network with 50 layers (Resnet50) by He et al. (arXiv:1512.03385, 2015). To train the model, we curated a diverse dataset consisting of 60 different WSI BMS, each manually and independently annotated by at least two experts providing a robust ground truth (figure 1). Data augmentation was performed via horizontal flipping, random resizing, and random cropping. A train-test split of 80:20 was implemented with 5-fold internal cross-validation. The model's accuracy was evaluated as mean intersection-over-union ratio yielding high congruency between ground truth and model prediction (figure 2). The final model uses downscaled images effectively reducing data storage size and data transfer time decreasing fully automated ROI prediction time to &amp;lt;300 ms for one WSI. The model allows for in-depth examination at the cellular level, harnessing the potential of thousands of relevant cells present in a WSI, in contrast to the limited range of 200 to 500 cells typically examined in conventional clinical routine diagnostics. Thereby, the model solves an essential bottleneck in computer vision in microscopy minimizing human manual labor and standardizing BMS processing results.
Translating Iacopone da Todi in Romanian: A Noble Journey toward and inward amor d’esmesuranza

This paper is a confession about how I felt illuminated by Iacopone’s noble poetry and, most of all, about my duty of not betraying the spirit of his lines while translating them into Romanian. A lot of examples will illustrate the five major levels of difficulty I had to cope with during the translating process: the lexical level (the Franciscan friar is one of the first names of Early Italian literature, with almost no literary tradition before him, pioneer of a mixtum linguisticum made of Latin-like forms, Provençal and Gallic borrowings, Umbrian vernacular forms, bold lexical inventions, etc.), the inherent incongruities between the poet’s non-standardized neo-Latin and the Romanian language (aspects regarding the length of the Romanian words in the meter scheme), the necessity of adapting the target language to the diaphasic peculiarities of the source text (mostly belonging to the Scriptures’ vocabulary of Latin origins used by the Catholic Church, which should be adapted to their Slavic correlatives used in the Orthodox tradition), the diachronic linguistic marks of a 13th century text (and the consequent choice of an archaic Romanian vocabulary, in recreating the forgotten medieval times with their old medical terminology) and, last but not least, the difficulties related to translating poetry in meter and rhymes (preserving the precise original prosodical and metrical patterns of each and every Lauda, for instance in a ballata form with the ancient kind of rhyme called rima zagialesca).
Studies for a Graphics Engine with an Artificial Intelligence System

The potential of Artificial Intelligence and Deep Learning technology has been explored in various sectors through ongoing research projects. It is proposed in this chapter to use a Deep Q-Network (DQN) to train a convolutional neural network using Q-Learning. The idea is to develop an algorithm that enables a virtual character to learn through trial and error, similar to the way humans learn. The aim of the study is to achieve a more natural and effective interaction between humans and virtual entities and also to cultivate a thorough comprehension of the fundamental ideas and methodologies associated with the integration of artificial intelligence into graphics engines. This research represents a significant step towards creating a self-learning virtual character that can interact seamlessly with humans. If successful, this approach can provide a basis for automating and optimizing time-consuming tasks entities perform without a preprogrammed intellectual capacity. The authors demonstrated a learning process similar to a "match to sample task." In this process, the character must recall precisely what tasks were rewarded. As the character repeats the tasks, they seek the same events that lead to rewards. This paper presents a well-researched concept that could pave the way for automating laborious processes that entities without a preprogrammed intellectual capacity perform. The success of this research could lead to a more efficient and effective way of interaction between humans and virtual entities, ultimately enhancing the quality of life.
Meanings of Questioning in Art Appreciation Education: A National Art Curriculum Content Analysis Study

This study investigates the changes in content related to ‘questioning’ within the development process of the national level art curriculum. It also assesses the significance of recently introduced question-related contents within the domain of art appreciation. Through a comprehensive literature review, this study delves into the nuances and implications of ‘questioning’ in school art appreciation classes. Furthermore, a content analysis was conducted to compare the meanings of ‘questioning’ in the 2022 revised national art curricu-lum with those of the previous curricular. Consequently, this study reveals several key findings. First, teach-ers are encouraged to incorporate questioning strategies given the transformation of students’ emotional responses to a work of art from passive to active appreciation. Second, the concept of ‘understanding’ within the 2022 curriculum provides the basis for introducing ‘essential questions’ in art appreciation classes. Third, the notion of ‘inquiry’ may be linked to students’ authentic curiosity, yet it necessitates fur-ther scholarly discourse to clarify its meaning. Fourth, teacher-initiated questions can help students ‘explain’ a work of art. Fifth, the 2022 curriculum assumes that a work of art serves catalysts for inspiring students’ new inquiries with the expectation that a diverse range of student-generated questions during art apprecia-tion classes will enhance their learning experience.
The Implementation of Snowball Throwing Strategy to Develop Students Ability in Reading Comprehension Social Function of Descriptive Text

This study aims to analyze the implementation of the Snowball Throwing strategy to develop students' ability in Reading Comprehension social function of descriptive text and to analyze students' responses the implementing of Snowball Throwing strategy to develop students' ability in Reading Comprehension of descriptive text. This study used a qualitative research method based on Classroom Action Research. The writer uses observation, field notes, and interviews as instruments. The participants in this study were 20 students of class X MIPA 3 MA ASSHIDDIQIYAH 3 Karawang. Research data in cycle 1 shows that the snowball-throwing strategy encourages students to understand reading descriptive texts. Students can perform better reading skills. This data is supported by interviews with student data showing positive responses in teaching reading using the snowball throwing strategy. In cycle 2, it showed increased student participation when conducting discussions; students seemed to enjoy and enjoy the teaching and learning process more. This data is supported by student responses which say that students like learning to do reading comprehension using the snowball-throwing strategy. Based on the study's results, the snowball-throwing strategy can encourage students to understand descriptive texts and make them actively participate in learning to read.
PENGGUNAAN MEDIA POHON PINTAR UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA MATERI OPERASI HITUNG PEMBAGIAN SISWA KELAS IVA SD NEGERI GIRIAN INDAH

This research examines students' learning outcomes on Mathematics subjects of calculating operating material division in class IVA SD Country of Girian Indah. The problem in this study is that students' learning outcomes in Mathematics are still low. This research aims to improve the learning outcomes of Mathematics of Indah Girian Girian Country IVA Elementary students. The method in this study uses a class action research method. The subject of this study was 23 students in IVA. Data collection techniques are carried out through observations, interviews, tests, and documentation. Data analysis techniques use descriptive statistical analysis to illustrate about increasing the value of student learning outcomes. The results of this study indicate that the results of Mathematics learning increased, where in cycle I students were due to 17 students with a percentage of 74% and students who were not due to 6 students with a percentage of 26% with an average of 77. In cycle II student learning outcomes increase with a total number of students who have fallen by 22 students and students who are not due to 1 student, so that the percentage of student completion in cycle II reaches 96%.
Research on Application Automation Operations Based on Win32com

This article aims to explore the method of utilizing the win32com module to interact with application API for achieving automation operations. Taking the AutoCAD application as an example, it introduces the automation operations of common tasks (such as file opening, property retrieval, etc.) by invoking the API interface. By studying the API documentation of AutoCAD and combining it with the win32com. client. Dispatch function, sample code is written to demonstrate the application of this technique in automation operations.
A Method for Anti-Leakage in Car Insurance Based on the Co-Occurrence Relationship Between Auto Parts

Car insurance fraud is a high-risk area in insurance and it accounts for as much as 80% of all insurance fraud, with a significant portion of it stemming from the risk of parts leakage. Current anti-leakage techniques in auto insurance mainly rely on the analysis of individual parts data such as vehicle accident records and parts damage lists, which neglects the consideration of the correlation among parts and leads to difficulty in identifying leaked parts effectively. This study proposes a method based on the co-occurrence relationship and density clustering of auto parts to detect parts leakage. In this research, an undisclosed dataset on car insurance fraud was utilized to conduct experiments, and the detection results of parts leakage were obtained. This method takes into account the correlation among auto parts, and possesses higher accuracy and practicality.
Fast Algorithm of Brightness and Contrast Enhancement Based on HSV

By analyzing RGB-to-HSV conversion algorithms, this paper proposes a fast algorithm for enhancing brightness and contrast based on HSV. Unlike traditional algorithm that convert RGB to HSV, enhance the brightness V, and then convert back to RGB, our approach allows for enhancing brightness and contrast directly in HSV without any conversion to RGB. Avoiding using the floating point multiplication and division, it used integer operations, shift operator and look-up table methods in this algorithm, so that improved the procession suggest admit the look-up table methods. Finally, the experiments show that the fast algorithm can save 70% of the computing time than the traditional algorithm. It can process the 720p video images real time.
Improved TF-IDF-Based LDA Topic Clustering Model for Specific Commodity Contexts and Different Sentiment Tendencies

As Natural Language Processing (NLP) is increasingly used in Internet content platforms, more businesses and organizations are concentrating their research and development efforts on NLP. Effective user review data mining has the potential to greatly accelerate the transition of digital products, particularly in e-commerce. The information represented by the review texts of various shopping categories differs, but due to the “generality” of their construction models, many traditional NLP text libraries frequently make judgments about a given context that are incorrect. This error is primarily due to a lack of a precise understanding of the “characteristics” of various domains. This research chooses to review data for three different product categories (clothing, cosmetics, and laptops) with distinct contexts on the Jingdong e-commerce platform based on the aforementioned problems. To analyze and examine the classification of consumers’ subject terms under different emotional tendencies for two different contexts of negative text and positive text, we propose the ED-LDA (Emotion discrimination - Latent Dirichlet Allocation) topic model. The findings demonstrate the significance of subject word grouping under various sentiment trends and context-specific characterized natural language analysis for social media review analysis of niche goods and subcultural circles.