Reviewerly/exHarmony huggingface dataset:
Dispersion broadening of the two-photon quantum interference width due to temporal filtering

Temporal filtering is widely recognized as an effective strategy for improving the visibility of the Hong-Ou-Mandel interferometer, an important tool for observing the phenomenon of two-photon quantum interference. However, such manipulation may lead to additional effects on the measurement. In addition to demonstrating the degradation of photon-detection efficiency, this paper demonstrates that temporal filtering can significantly broaden the interference width, especially in scenarios where photons experience substantial dispersion. This broadening effect is also presented in the nonlocal Franson interferometer. The good agreement between experimental results and theoretical simulations not only reveals the mechanism behind dispersion-induced broadening in two-photon quantum interference but also provides an alternative and convenient method for measuring dispersion without the need for extra equipment.
Entanglement Distillation Based on Polarization and Frequency Hyperentanglement

Entanglement distillation has many applications in quantum information processing and is an important tool for improving the quality and efficiency of quantum communication, cryptography, computing, and simulation. We propose an entanglement distillation scheme using only one pair of polarization-frequency hyperentangled photons, which can be equivalently viewed as containing two pairs of entangled logical qubits: a pair of polarization-entangled qubits and a pair of frequency-entangled qubits. To perform the required CNOT operation between the two qubits we consider the use of a polarization-dependent frequency converter. Compared to past methods of entanglement distillation that relied on polarization and spatial-mode/energy-time degree of freedom, the utilization of frequency-encoded qubits offers an advantage in that it is immune to bit-flip errors when the channel is linear. After distillation, the fidelity of polarization entanglement can be significantly improved by sacrificing the frequency degree of freedom. Through simulation, we show that high fidelity gains, large yield, and high distillation rate can be achieved. Our distillation scheme is simple to implement with current technologies, compatible with existing telecommunication fiber networks, and is a promising approach for achieving efficient quantum communication.
X-RAY STRUCTURAL STUDIES OF SYNTHETIC VALLERIITES USING SYNCHROTRON RADIATION

Valleriite is a rare mineral of the hydroxysulfide group, that structure and physicochemical properties are atypical for sulfides. The first attempts to identify the structure of valleriite led to false conclusion of its purely sulfide nature, while the magnesium and aluminum present in the samples were attributed to the contamination of natural samples with associated minerals (brucite, lizardite and others). Nevertheless, through the number of works of domestic and foreign authors, the structure of natural valleriite was determined, as well as of a number of new minerals of the valleriite group - tochilinite, haapalaite, yushkinite and others.
Cognitive digital modelling for hyperspectral image classification using transfer learning model

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by the homogenous region obtained by segmenting the target image. On this basis, the deep Siamese convolutional neural network is used to reduce the distribution difference between the source domain image and the target domain image to achieve the final result of the target hyperspectral image classification. The experimental results show that the combination of homogenous region and Siamese convolutional network can improve the classification effect of semisupervised transfer learning and better solve cross-regional hyperspectral image classification.
SIKLUS PENDAPATAN DAN PENGELUARAN KAS DI TOKO EMAS E TARGER DALAM SISTEM INFORMASI AKUNTANSI

This study aims to examine the income and expenditure cycle at the E Targer Gold Shop with an accounting information system. The use of accounting information systems helps improve efficiency and accuracy in the cycle process. This study uses a descriptive method by collecting data through observation, interviews, and analysis of related documents. Several problems were found that could be corrected through the implementation of an accounting information system. In the revenue cycle, the information system makes it easier to record sales and financial reports. In the expenditure cycle, the information system facilitates the procurement and recording of expenditures. Implementation of accounting information systems is recommended to increase efficiency, accuracy, and make the right decisions in a gold shop.
Research on the Application of Big Data in Retirement Service Management

In view of the rapid development of China’s socio-economy, the field of science and technology has also ushered in new development opportunities. Various modern information technologies have found extensive application in diverse societal sectors and various aspects of people’s daily lives. The most representative and widely used technology among them is big data. The 21st century presents not only an era dominated by information technology but also a growing challenge of an aging population in China. Consequently, there is a considerable journey ahead in terms of managing services for retirees. It is imperative to firmly establish the right mindset regarding the internet and big data concepts. Actively exploring diverse information technology applications in the service management for retired individuals and fostering innovative work models can lead to significant enhancements in service quality and efficiency. Therefore, this article presents a study on the application of big data Internet technology in the management of retirement services.
The Augmented Welder Profile: Augmenting Craftmanship with Digital and Collaborative Tools

More and more applications of Augmented Reality (AR) in manufacturing industries are introduced every day and while recent research has shown that one of the more popular applications, high volume assembly instructions, might not offer the best setting for this technology, many other applications exist that do. For assembly, remote guidance or training, rare assemblies, low takt time and high mix production, do still show promise. This article introduces the role of the Augmented Welder, a role utilizing AR technology for the programming of a welding robot. An operator support system in the form of a custom application programmed in Unity and visualized with a pair of Hololens2, connected to an ABB robot through RobotStudio. The robot is equipped with a welding gun dummy. The operator can, through the Hololens2, set safety boundaries, introduce work pieces, place targets in a 3D space, simulate the robot path, send the program to the robot, and activate the physical robot, among other things. We performed a modified pluralistic walkthrough to evaluate the operator support system both with respect to our application but also to search valuable insight to the general use of AR in the use of such applications. Results showed that while the subjects were generally positive towards the support system, several issues were identified and raised by various degrees of severity. The primary issues arose around the navigation and interaction with 2D menus and 3D objects in a 3D Mixed Reality (MR) space. The absence of physics confused the subjects as they could not interact with the virtual objects as they would have with physical objects. The lack of physics simply meant that they didn't act the same. Furthermore the interaction with 2D menus in a 3D space was both reported and observed as being very difficult as the 2D representations probably led to problems with depth perception. The general results of the debriefing indicated that using AR for robot programming was challenging although some of this can be attributed to the fact that this was the participants first use of such a system. The users indicated that the menus were appropriate and that the interaction was intuitive while the navigation within the system was not experienced as natural which confirms the above-mentioned issues with menus disappearing from the line of sight and feedback around generated target points being absent.
Application of Fuzzy Logic Control in Iron Box Temperature Regulation

Abstract: This work investigates the use of fuzzy logic control in the context of temperature regulation in an iron box. Instead of the usual Boolean logic employed in computers, fuzzy logic is a computer-based approach of degree determination that gives the truth. Rather than adopting typical methods that put the problem into a black box model, this assists the problem solver in approaching the challenge in a way that they can explain and comprehend. The study explains how fuzzy logic can be used to manage an iron box's heating element based on input variables such as wrinkle severity and water level. To control the heat level of the iron, the system defines membership functions, rules, and other defuzzification mechanisms. This system's output can be used to effectively manage the heating element of the iron, providing a more intelligent and responsive operation. Through modelling and testing, we evaluate the effectiveness of our fuzzy logic control system in achieving accurate and dependable heating level outputs.
Development and Validation of an Automated Tool to Retrieve and Curate Faculty Publications of Academic Departments

Introduction Academic departments need to monitor their faculty's academic productivity for various purposes, such as reporting to the medical school dean, assessing the allocation of non-clinical research time, evaluating for rank promotion, and reporting to the Accreditation Council for Graduate Medical Education (ACGME). Our objective was to develop and validate a simple method that automatically generates query strings to identify and process distinct department faculty publications listed in PubMed and Scopus. Methods We created a macro-enabled Excel workbook (Microsoft, Redmond, WA) to automate the retrieval of faculty publications from the PubMed and Scopus bibliometric databases (available at https://bit.ly/get-pubs). Where the returned reference includes the digital object identifier (doi), a link is provided in the workbook. Duplicate publications are removed automatically, and false attributions are managed. Results At the University of Miami, between 2020 and 2021, there were 143 anesthesiology faculty-authored publications with a PubMed identifier (PMID), 95.8% identified by the query and 4.2% missed. At Vanderbilt University Medical Center, between 2019 and 2021, there were 760 anesthesiology faculty-authored publications with a PMID, 94.3% identified by the query and 5.7% missed. Recall, precision, and the F1 score were all above 93% at both medical centers. Conclusions We developed a highly accurate, simple, transportable, scalable method to identify publications in PubMed and Scopus authored by anesthesiology faculty. Manual checking and faculty feedback are required because not all names can be disambiguated, and some references are missed. This process can greatly reduce the burden of curating a list of faculty publications. The methodology applies to other academic departments that track faculty publications.
PMIndoor: Pose Rectified Network and Multiple Loss Functions for Self-Supervised Monocular Indoor Depth Estimation

Self-supervised monocular depth estimation, which has attained remarkable progress for outdoor scenes in recent years, often faces greater challenges for indoor scenes. These challenges comprise: (i) non-textured regions: indoor scenes often contain large areas of non-textured regions, such as ceilings, walls, floors, etc., which render the widely adopted photometric loss as ambiguous for self-supervised learning; (ii) camera pose: the sensor is mounted on a moving vehicle in outdoor scenes, whereas it is handheld and moves freely in indoor scenes, which results in complex motions that pose challenges for indoor depth estimation. In this paper, we propose a novel self-supervised indoor depth estimation framework-PMIndoor that addresses these two challenges. We use multiple loss functions to constrain the depth estimation for non-textured regions. We introduce a pose rectified network that only estimates the rotation transformation between two adjacent frames of images for the camera pose problem, and improves the pose estimation results with the pose rectified network loss. We also incorporate a multi-head self-attention module in the depth estimation network to enhance the model’s accuracy. Extensive experiments are conducted on the benchmark indoor dataset NYU Depth V2, demonstrating that our method achieves excellent performance and is better than previous state-of-the-art methods.
Multi-Modal Medical Image Matching Based on Multi-Task Learning and Semantic-Enhanced Cross-Modal Retrieval

With the continuous advancement of medical imaging technology, a vast amount of multi-modal medical image data has been extensively utilized for disease diagnosis, treatment, and research. Effective management and utilization of these data becomes a pivotal challenge, particularly when undertaking image matching and retrieval. Although numerous methods for medical image matching and retrieval exist, they primarily rely on traditional image processing techniques, often limited to manual feature extraction and singular modality handling. To address these limitations, this study introduces an algorithm for medical image matching grounded in multi-task learning, further investigating a semantic-enhanced technique for cross-modal medical image retrieval. By deeply exploring complementary semantic information between different modality medical images, these methods offer novel perspectives and tools for the domain of medical image matching and retrieval.
Geochemistry of Some Ion-Adsorption Clay Type REE Deposits from South Perak, Malaysia

Summary Due to their unique chemical, magnetic, and luminescent properties, rare earth elements (REE) are necessary for high-tech enterprises. Ion Adsorbed Deposits (IADs) and Ion Adsorption Clays (IACs) are types of REE deposits formed by chemical weathering of REE-rich parent minerals. This study investigated soil profiles associated with the weathering of Late Triassic granitic bodies in the South Perak region of Peninsular Malaysia in order to advance our knowledge of the behaviour and distribution of rare earth elements during granite weathering. Surface and subsurface samples were collected from eight sites. For laboratory analysis, the samples were dried, pulverised, sieved, and ground to the desired mesh size. In South Perak, there are vast exposures of granitic igneous bodies, and Ion Adsorbed Clays (IACs) contain sufficient concentrations of Rare Earth Elements (REEs) associated with this form of weathered rare earth deposits. At numerous locations, the total REEs content (TREE) exceeds the minimum threshold value of 300 ppm.
Digital Twins, Metaverse, and Learning. Review and Proposal of Conceptual Framework

Digital twins (DT) technology has advanced and is gaining momentum of applicability in different contexts. The metaverse enables immersive experiences that integrate the real world with the digital. Given the importance of DT in the metaverse, this article seeks to review the literature (between 2018–2022) on the articulation of DT, metaverse, and learning, and propose a conceptual framework towards Education 5.0. The review indicates few works in this regard and specifically in Latin American countries the research and application in educational contexts is insufficient. There is an interest in defining frameworks and articulating with other extended reality technologies. The possibilities offered by DT and the metaverse are wide, and that is why it is necessary to reduce the gap in this regard, with more research, projects, dissemination, and agreements. This study contributes to the investigation and projection of the DT, metaverse in learning scenarios, as well as to continue delving into frameworks or models under the Industry/Society 5.0 approach.
A Study on AI: Customer Feedback and Personalized Marketing Comparison between India and Nigeria

In the world of business, using Artificial Intelligence (AI) has really changed how companies connect with customers. This research looks at how AI can understand what customers think and how it helps make marketing more personal. It explores how AI tools can learn important things from customer feedback, like what people like and how they feel. Then, comes how companies can uses these things to make marketing that feels special to each customer. Further, this shows the customer perspective and loyalty along with comparative study of India's AI personalized marketing with Nigeria's AI personalized marketing, by looking at real examples from different businesses. This helps companies build strong relationships with customers and be better than their competitors in today's world that uses a lot of data. The research findings help us see how AI and understand customer feedback are super important for making customers happy and loyal to a brand. The findings underscore the importance of AI in improving client engagement and pride, ultimately fostering loyalty to manufacturers. However, it is miles vital to notice that the effectiveness of AI-powered personalized advertising strategies can vary based totally on cultural, economic, and regulatory elements.
Automating the Generation of Competency Questions for Ontologies with AgOCQs

Competency Questions (CQs) are natural language questions drawn from a chosen subject domain and are intended for use in ontology engineering processes. Authoring good quality and answerable CQs has been shown to be difficult and time-consuming, due to, among others, manual authoring, relevance, answerability, and re-usability. As a result, few ontologies are accompanied by few CQs and their uptake among ontology developers remains low. We aim to address the challenges with manual CQ authoring through automating CQ generation. This novel process, called AgOCQs, leverages a combination of Natural Language Processing (NLP) techniques, corpus and transfer learning methods, and an existing controlled natural language for CQs. AgOCQs was applied to CQ generation from a corpus of Covid-19 research articles, and a selection of the generated questions was evaluated in a survey. 70% of the CQs were judged as being grammatically correct by at least 70% of the participants. For 12 of the 20 evaluated CQs, the ontology expert participants deemed the CQs to be answerable by an ontology at a range of 50 $$\%$$ -85 $$\%$$ across the CQs, with the remainder uncertain. This same group of ontology experts found the CQs to be relevant between 70 $$\%$$ -93 $$\%$$ across the 12 CQs. Finally, 73 $$\%$$ of the users group and 69 $$\%$$ of the ontology experts judged all the CQs to provide clear domain coverage. These findings are promising for the automation of CQs authoring, which should reduce development time for ontology developers.
Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods.
Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic (S<sup>2</sup>) method from the given problem. The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models, respectively. The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations. The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities. The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.