Reviewerly/exHarmony huggingface dataset:
Scalable Multipartite Entanglement Distribution in Quantum Networks

Multipartite entangled states (MES) are useful resources for a wide range of quantum applications, including quantum secret sharing, and quantum sensing. In this work, we propose a scalable architecture for distributing multipartite entangled states (MES) using arbitrary bipartite quantum networks. In particular, we address the problem of joint tree selection and rate allocation in multipartite entanglement distribution networks in order to optimize the weighted aggregate MES generation rate across different user groups. To obtain optimal solutions, we develop a mixed-integer programming (MIP) model and employ a heuristic approach based on the Genetic algorithm (GA) to obtain feasible solutions efficiently.
Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.
An Evaluation of Training Strategies in QuGAN

Recently, researchers have attempted to enhance Generative Adversarial Network (GAN) for content generation using hybrid quantum-classical approaches. However, quantum GAN (QuGAN) is a novel field of study, and it remains unclear to understand the behaviors due to the presence of two competing modules, namely generator and discriminator. In this study, through empirical evaluation, we show that the choice of training strategies for generator and discriminator strongly affects the performance of QuGAN.
Graphic design and new technologies

Stress and anxiety problems are common in our society. This work aims to offer a solution through video game design. Over time, different classes of video games have been developed, some allow you to relax, such as Stardew Valley (Barone, 2016), while others create stress and anxiety, such as VVVVVV or Super Hexagon (Cavanagh, 2012). Despite this, no specific video games have been found focused on teaching users to manage their emotions. Taking advantage of elements of graphic design, such as interactivity, graphical interface and character development, we have generated an immersive, pleasant and educational experience. The result is an art bible that collects the graphics created, including sketches of the character, enemies, scenes and objects of the game, as well as interface designs; and a Game Design Document (GDD) where we will reflect everything necessary for the development of this video game.
Non-Gaussian Dynamics of Quantum Fluctuations and Mean-Field Limit in Open Quantum Central Spin Systems

Central spin systems, in which a central spin is singled out and interacts nonlocally with several bath spins, are paradigmatic models for nitrogen-vacancy centers and quantum dots. They show complex emergent dynamics and stationary phenomena which, despite the collective nature of their interaction, are still largely not understood. Here, we derive exact results on the emergent behavior of open quantum central spin systems. The latter crucially depends on the scaling of the interaction strength with the bath size. For scalings with the inverse square root of the bath size (typical of one-to-many interactions), the system behaves, in the thermodynamic limit, as an open quantum Jaynes-Cummings model, whose bosonic mode encodes the quantum fluctuations of the bath spins. In this case, non-Gaussian correlations are dynamically generated and persist at stationarity. For scalings with the inverse bath size, the emergent dynamics is instead of mean-field type. Our Letter provides a fundamental understanding of the different dynamical regimes of central spin systems and a simple theory for efficiently exploring their nonequilibrium behavior. Our findings may become relevant for developing fully quantum descriptions of many-body solid-state devices and their applications.
Evaluation and Improvement of Segment Anything Model for Interactive Histopathology Image Segmentation

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM’s performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM’s limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM’s decoder. The experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. The code is available at https://github.com/hvcl/SAM_Interactive_Histopathology
TOUCH, SPEAK, LEARN: MULTIMODAL INTERFACES IN THE CLASSROOM

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 8316-8325ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2129Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
Evolutionary Game Analysis of Technology Finance Model in Underdeveloped Areas Based on Blockchain

The traditional model of technology finance, especially in underdeveloped areas, the model of technology finance has some limitations, such as high risk, imperfect system and lack of government intervention. This paper proposes blockchain technology to solve this problem and verify its effectiveness
Mitigating Decentralized Finance Liquidations with Reversible Call Options

Liquidations in Decentralized Finance (DeFi) are both a blessing and a curse—whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.
Development of learning media for the Google site web-based on character

This research aims to develop learning media based on the Google site web-based on character values, which will help elementary school teachers and students learn, especially on social studies content on ethnic and cultural diversity material in Indonesia. This study uses the ADDIE R&amp;D model with five stages. The results of the feasibility validation from design experts show a percentage of 88.5% with excellent and valid criteria. Material experts perform a presentation of 86.6% with excellent and valid criteria. Linguists with a presentation of 80% with excellent and valid criteria. While aspects of application and student attractiveness with a presentation of 85% and teachers with a presentation of 96%. From the aspect of effectiveness, with a presentation of 98.4%, and from the aspect of student PPK assessment, with a presentation of 94.2%. These results indicate that media can be applied to process learning in schools, especially in grade IV SD.
CoSiReT: Innovation of ReT (Refutation Texts) to Reduce Students' Misconceptions Concerning Transverse Waves

The research goal was to develop innovative learning media on refutation texts (ReT), scilicet integrating Computer Simulations on Refutation Texts (CoSiReT). CoSiReT's role is to reduce students' misconceptions concerning the basic concept of transverse waves. This research design used 4D (Define, Design, Develop, and Disseminate). Participants include 64 students aged 16-18 years in the Tuban region, East Java, Indonesia (37 male were called Mas, and 27 female were called Mbak). The research instrument used a media validation sheet (15 assessment indicators). The pre-post test instrument used the Multi-representation of a Four-tier Instrument on Transverse Wave (MOFI-OTW). Analysis of data validation results was using a rater test assisted by Rasch's Mini-Facets software. According to the seven validators, the results of CoSiReT fulfill aspects of instructions for use, language, content presentation, and appearance. The percentage of students' misconception reduction was analyzed using the Reduction Misconceptions (RM) adapted from Hake's n-gain equation. Overall, the reduction of misconceptions is in the high category. Thus, it can be concluded that CoSiReT can be developed and used a role in reducing student misconceptions. Researchers or practitioners are expected to implement CoSiReT as a learning innovation that aims to reduce basic misconceptions of transverse waves.
Specializations in Software Engineering Education

This paper describes the origins and evolution of software engineering education as it has developed independently of computer science and electrical engineering programs. The rapid growth of software technology and development processes has led to the emergence of subdisciplines in software engineering, to the extent that it is no longer feasible for software engineers to remain knowledgeable about all of the relevant topics. As a result, it seems likely that software engineering education will follow the path taken in other fields, such as law and medicine, where students receive foundational education in software engineering, followed by additional education and practice in one or more specialized areas.
Development of Islamic and ethnic educational videos using Android-based inspiring suite software

Modifications of education and learning must be made because of the industrial revolution that continues to advance and develop so a teacher needs to prepare himself with science and teaching skills in the 21st century without losing the principles and character of traditional Islamic education. Likewise, with the use of media in learning, teachers are required to be able to innovate by utilizing existing technology so that students can get knowledge, expertise, or behavior from the results of the learning carried out. This research was conducted to develop educational videos using iSpring suite software that can be used in the face-to-face learning process, online learning, or independent learning, as well as to see its validity and practicality. The type of research used is research and development by applying 4D models (define, design, develop, and disseminate). The results showed that the educational videos that have been developed are needed by teachers and students and are declared very valid and very practical so that learning is more varied, interesting, and efficient and can be used independently or in groups anytime and anywhere without using the internet because it is in the form of an APK. For face-to-face learning, the learning media that has been designed is published in HTML while for self-study it into video form so that it can be opened on each student's Android.
Scientific journals: guidelines for creating a data collection

This article aims to suggest actions for the opening of scientific data, deposit, use and dissemination, so that scientific journals, linked to Higher Education Institutions, create open data collections. It is an applied social research, with a qualitative, exploratory, documentary approach and with action-research dimensions. A survey of the scientific data openness policies of the journals that use the Dataverse repository of Harvard University was carried out, based on the requirements of the quality seal of reliable digital repositories Core Trust Seal and the Transparency and Openness Promotion Guidelines. The information obtained in this survey supported the implementation of action-research steps, through which guidelines were formulated to meet the proposed objective. With such results, other journals will be able to join the movement of open data adjacent to the articles they publish, expanding the scientific practices related to the movement for open science and the democratization of scientific knowledge.
Comparing Photorealistic and Animated Embodied Conversational Agents in Serious Games: An Empirical Study on User Experience

Embodied conversational agents (ECAs) are paradigms of conversational user interfaces in the form of embodied characters. While ECAs offer various manipulable features, this paper focuses on a study conducted to explore two distinct levels of presentation realism. The two agent versions are photorealistic and animated. The study aims to provide insights and design suggestions for speech-enabled ECAs within serious game environments. A within-subjects 2 × 2 factorial design was employed for this research with a cohort of 36 participants balanced for gender. The results showed that both the photorealistic and the animated versions were perceived as highly usable with overall mean scores of 5.76 and 5.71 respectively. However, 69.4% of participants stated they preferred the photorealistic version; 25% of participants stated they preferred the animated version; and 5.6% of participants had no stated preference. The photorealistic agents were perceived as more realistic and human-like while the animated characters made the task feel more like a game. Even though the agents’ realism had no significant effect on usability, it positively influenced participants’ perceptions of the agent. This research aims to lay the groundwork for future studies on the impact of ECA realism in serious games across diverse contexts.
Moving Object Detection and Tracking in Video Frame Based on OpenCV

Local occlusion may be developed during the target motion, such that it is urgent to solve the problem of video tracking loss caused by moving target occlusion. In this paper, the computer vision library OpenCV is used to preprocess the motion video frame. Two algorithms are combined to solve the problem of tracking loss due to target-background similarity and occlusion: one is the Camshift algorithm (which is used to track the moving target); the other is the Kalman filter (which is used to predict the target position). Comparing with Meanshift algorithms in the same experimental environment, the results show that the proposed Camshift-based method can effectively filter out the background interference in the tracking process based on accurate detection and tracking of moving targets, and make the motion information of occluded targets more prominent. Good performance has been achieved in the interference and occlusion environment, and it has certain real-time performance and effectiveness.
Bell-State-Exchange-Parity-Based Protocol for Efficient Autocompensation of Quantum Key Distribution Encoded in Polarization or Spatial Modes

We analyze autocompensation possibilities in entanglement-based QKD protocols. In particular, we study the seminal BBM92 protocol and find that an autocompensating technique is possible, although with severe limitations. This prompts the introduction of a different, more practical protocol based on Bell state exchange parity (BSEP), which allows for intrinsic autocompensation of optical fiber perturbations in various two-dimensional fiber-optic encodings while retaining advantageous MDI-QKD characteristics. We present the BSEP protocol in detail, describing both the quantum light propagation and the optical hardware requirements. Finally, we analyze its security, computing its expected performance through the key rate.
Knowledge Graph-Based Approach to Unstructured Multi-Source Data Fusion

The current unstructured multi-source data fusion has insufficient application of data attributes, which leads to unsatisfactory data fusion results. To this end, an unstructured multi-source data fusion method based on knowledge graph is proposed. Building a data tracking model to integrate unstructured multi-source datasets. Processing unstructured multi-source datasets. Associating data based on knowledge graphs. Alignment and complementary processing of data attributes to correlate unstructured multi-source data attribute fusion. The experiments show that the average fusion of the method reaches 0.92152. The fusion of unstructured multi-source data is substantially improved and has high practical application value.
Efficient model selection for predictive pattern mining model by safe pattern pruning

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering unified inconsistent notation sub-structures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the safe pattern pruning method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences.
ANALISIS PENTINGNYA KEMAMPUAN MENULIS KARYA ILMIAH PADA SISWA SMA

The aim of this study was to test the significance of scientific writing abilities among secondary school students. The research methodology used involved a literature review, specifically using library research, and adopted a descriptive qualitative approach. The analysis methodology includes a systematic exploration of diverse sources, including books, journals, and research, to understand, evaluate, and synthesize information related to the assessment of scientific writing skills in secondary educational institutions. According to the results of this research, an important component in the Indonesian language curriculum is learning to write scientific papers, which is taught during the secondary education phase. The process of writing a scientific paper is different from writing a regular composition. Achieving accuracy in writing requires the application of certain writing methods and techniques. Improving students' abilities in scientific writing includes several dimensions, including the preparation of the structure of scientific work, the ability to analyze problems and content, proficiency in grammar and spelling, proficiency in writing quotations and bibliography, as well as maintaining accuracy in scientific writing. Mastering writing skills, especially in the context of scientific work, is a complex endeavor with great challenges. Students face challenges in various aspects of scientific writing, including formulating titles, background information, problem statements, discussions, quotations and bibliography, as well as writing formats. Teachers are expected to provide better guidance to students in developing background information, formulating problem statements, building theoretical foundations, solving problems, and creating accurate and appropriate bibliography.
IMPLEMENTASI METODE PEMBELAJARAN ROLE PLAYING UNTUK MENINGKATKAN HASIL BELAJAR BAHASA INGGRIS DI SMAK SANTO PETRUS ENDE

Learning using the Role Playing learning method aims to improve the English learning outcomes of Class The research method used is classroom action research (PTK). Data collection techniques in this research used test techniques and observation techniques. Data analysis in this research uses test data analysis techniques and observation data analysis techniques. The research results show that there is an increase in English learning outcomes through the Role Playing learning method. This is indicated by the average learning outcomes and classical completeness of students in the first cycle of 61.89 with classical completeness of 17.24%; cycle II was 76.20 with classical completeness of 68.96%; and in cycle III it was 80.17 with classical completeness of 89.65%. Apart from learning outcomes, student activity also increased, where in cycle I the average activity of students who were actively involved was 24.13%, students who were passively involved was 34.48%, and students who were not involved was 41.37%. In cycle II the average activity of students who were actively involved was 65.51%, students who were passively involved was 27.58%, and students who were not involved were 6.89%. And in cycle III the average activity of students who were actively involved was 89.65%, students who were passively involved was 6.89%, and students who were not involved were 3.44%.
An Efficient Method for Bangla Handwritten Digit Recognition Using Convolutional Neural Network

Handwritten digit recognition is a fundamental problem in the field of computer vision and pattern recognition. This paper presents a Convolutional Neural Network (CNN) approach for recognizing handwritten Bangla digits. The proposed method utilizes a dataset of handwritten Bangla digit images and trains a CNN model to classify these digits accurately. The dataset is preprocessed to enhance the quality of the images and make them suitable for training the CNN model. The trained model is then tested on a separate test dataset to evaluate its performance in terms of accuracy. With the Ekush: Bangla Handwritten Data - Numerals dataset, we tested our CNN implementation to determine the precision of handwritten characters. According to the test results, 25% of the images using a training set of more than 150,000 images from Ekush dataset had an accuracy of 98.3%.