Reviewerly/exHarmony huggingface dataset:
Virtual Reality Childbirth Education With 360° Videos

During the pandemic in Finland, most childbirth education (CBE) programs were canceled or transferred online. We aimed to improve the situation by developing a virtual reality (VR) CBE. This article describes the process of developing a VR CBE pilot program and the results from the preliminary user test. To create the VR experience, we used 360° videos as the main content. The program is usable with VR headsets, a computer, tablet, and smartphone. When using the program with a VR headset, the users felt they were in the birthing room; they did not feel motion sickness, nor did they have usability challenges. The users preferred using the program on their own, studying independently with a tablet or mobile device.
Special Issue on Multi-Agent Systems

Multi-agent systems (MAS) are a class of systems in which multiple agents interact with each other and their environment to achieve a common or individual goal [...]
Binary Shape Vectorization by Affine Scale-space

Binary shapes, or silhouettes, are building elements of logos, graphic symbols and fonts， which require various forms of geometric editing without compromising the resolution. In this paper, we present an effective silhouette vectorization algorithm that extracts the outline of a 2D shape from a raster binary image and converts it to a combination of cubic Bézier polygons and perfect circles. Compared to state-of-the-art image vectorization software, this algorithm has demonstrated a superior reduction in the number of control points while maintaining high accuracy.
Quantum Markov chains on the line: matrix orthogonal polynomials, spectral measures and their statistics

Inspired by the classical spectral analysis of birth-death chains using orthogonal polynomials, we study an analogous set of constructions in the context of open quantum dynamics and related walks. In such setting, block tridiagonal matrices and matrix-valued orthogonal polynomials are the natural objects to be considered. We recall the problems of the existence of a matrix of measures or weight matrix together with concrete calculations of basic statistics of the walk, such as site recurrence and first passage time probabilities, with these notions being defined in terms of a quantum trajectories formalism. The discussion concentrates on the models of quantum Markov chains, due to S. Gudder, and on the particular class of open quantum walks, due to S. Attal et al. The folding trick for birth-death chains on the integers is revisited in this setting together with applications of the matrix-valued Stieltjes transform associated with the measures, thus extending recent results on the subject. We also consider the case of non-symmetric weight matrices and explore some examples.
Schema.org for research data managers: a primer

Data managers are currently investigating the value proposition of Schema.org (SDO) with a lightweight implementation that couples the SDO vocabulary with indexing Google Dataset Search. This pathway essentially 'webifies' dataset search and syndication and enhances dataset discovery. The Primer was created to layout the SDO landscape, where it came from, what is driving its uptake in research data management, and how it works in broad strokes. It was designed to introduce individuals with little technical knowledge to the benefits and importance of Schema.org and aimed to be a 'one-step-back' from the numerous guidance documents that are being produced in the RDM community. This document describes the mark-up process in very simple terms, provides current methods of adoption by the research community, describes related technical areas relevant to data managers and lists what organisations they should follow to keep apprised of this work.
Efficient 3D scanning measurement system based on asymmetric trinocular vision and a multi-line laser

The laser scanning measurement system has a pivotal role in precision measurement thanks to the non-contacting and low-cost advantages, but traditional methods and systems are inadequate in terms of accuracy, efficiency, and adaptability. In this study, an efficient 3D scanning measurement system based on asymmetric trinocular vision and a multi-line laser is developed to improve the measurement performance. The system design, working principle, and 3D reconstruction method are explored, as well as the innovation of the developed system. Furthermore, an efficient multi-line laser fringes indexing method is presented based on K-means ++ clustering and hierarchical processing to improve processing speed with guaranteed accuracy, which is the key point of the 3D reconstruction method. Various experiments are conducted to verify the capability of the developed system, and the results show that the developed system fulfills measurement needs in adaptability, accuracy, effectiveness, and robustness. The developed system achieves better results than commercial probes for complex measurement conditions, and measurement precision can be achieved to within 18 µm.
Learning in metaverse: the immersive atelier model of the architecture studio

Purpose This paper aims to describe the immersive atelier model (IAM), a pedagogical model for remote inter-university studios that promotes quality education. The IAM uses multi-user virtual environments (MUVEs) in two atelier types: A predefined MUVE and a student-shaped one. The study questions how the IAM, using MUVEs, meets the needs of remote inter-university studios. The research explores how MUVE types are used and experienced by students. Design/methodology/approach Forty-six students that participated in a remote studio course involving three universities were monitored through observations and a post-course questionnaire, responded to by twenty-five students. Findings Findings provide insights into the learners’ experience and a rich description of the teaching and learning acts that emerged while using the MUVEs types. Student-shaped MUVEs were found particularly supportive of acts associated with indirect learning and conceptualization. The study identifies subtypes of student-shaped MUVEs that support these desired educational acts. Research limitations/implications Findings provide encouraging insights for expanding the traditional atelier beyond its physical constraints and supporting sustainable quality education in remote inter-university studios. Practical implications The IAM can assist tutors in designing future virtual design studios to achieve diverse knowledge and learning progress. Social implications This paper fulfills an identified need to update the atelier pedagogical model to support sustainable quality education in remote inter-university studios. Based on the affordances of MUVEs, the IAM expands the traditional atelier with types of virtual ateliers to support the learners’ sense of belongingness and engagement. Originality/value Innovatively, the IAM simultaneously uses MUVEs as educational and design spaces that enhance learning.
A stance dataset with aspect-based sentiment information from Indonesian COVID-19 vaccination-related tweets

As a platform of social media with high activity, Twitter has seen the discussion of many hot topics related to the COVID-19 pandemic. One such is the COVID-19 vaccination program, which has skeptics in several religious, ethnic, and socioeconomic groups, and Indonesia has one of the largest populations of various ethnicities and religions of countries worldwide. Diverse opinions based on skepticism about the effectiveness of vaccines can increase the number of people who refuse or delay vaccine acceptance. Therefore, it is important to analyze and monitor stances and public opinions on social media, especially on vaccine topics, as part of the long-term solution to the COVID-19 pandemic. This study presents the Indonesian COVID-19 vaccine-related tweets data set that contains stance and aspect-based sentiment information. The data were collected monthly from January to October 2021 using specific keywords. There are nine thousand tweets manually annotated by three independent analysts. We annotated each tweet with three labels of stance and seven predetermined aspects related to Indonesian COVID-19 vaccine-related tweets: services, implementation, apps, costs, participants, vaccine products, and general. The dataset is useful for many research purposes, including stance detection, aspect-based sentiment analysis, topic detection, and public opinion analysis on Twitter, especially on the policies regarding the prevention of pandemics.
Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

Introduction: Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. Materials and Methods: The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. Results: The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. Conclusion: The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..
Prototype-guided multi-scale domain adaptation for Alzheimer's disease detection

Alzheimer's disease (AD) is the most common form of dementia and there is no effective treatment currently. Using artificial intelligence technology to assist the diagnosis and intervention as early as possible is of great significance to delay the development of AD. Structural Magnetic Resonance Imaging (sMRI) has shown great practical values on computer-aided AD diagnosis. Affected by data from different sources or acquisition domains in realistic scenarios, MRI data often suffer from domain shift problem. In this paper, we propose a deep Prototype-Guided Multi-Scale Domain Adaptation (PMDA) framework to handle MRI data with domain shift problem, and realize automatic auxiliary diagnosis of AD, Mild Cognitive Impairment (MCI) and Cognitively Normal (CN). PMDA is composed of three modules: (1) MRI multi-scale feature extraction module combines the advantages of 3D convolution and self-attention to effectively extract multi-scale features in high-dimensional space, (2) Prototype Maximum Density Divergence (Pro-MDD) module adopts prototype learning to constrain the feature outlier samples in a mini-batch when MDD is used to align source domain and target domain, and (3) Adversarial Domain Adaptation module is applied to achieve global feature alignment of the source domain and target domain and co-training two distinctive discriminators to mitigate the over-fitting issue. Experiments have been performed on 3T and 1.5T sMRI with domain shift in ADNI dataset. The experimental results demonstrated that the proposed framework PMDA outperforms supervised learning methods and several state-of-the-art domain adaptation methods and achieves a superior accuracy of 92.11%, 76.01% and 82.37% on AD vs. CN, AD vs. MCI, and MCI vs. CN tasks, respectively.
Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning

Stochasticity is an inherent feature of biological neural activities. We propose a noise-injection scheme to implement a GHz-rate stochastic photonic spiking neuron (S-PSN). The firing-probability encoding is experimentally demonstrated and exploited for Bayesian inference with unsupervised learning. In a breast diagnosis task, the stochastic photonic spiking neural network (S-PSNN) can not only achieve a classification accuracy of 96.6%, but can also evaluate the diagnosis uncertainty with prediction entropies. As a result, the misdiagnosis rate is reduced by 80% compared to that of a conventional deterministic photonic spiking neural network (D-PSNN) for the same task. The GHz-rate S-PSN endows the neuromorphic photonics with high-speed Bayesian inference for reliable information processing in error-critical scenarios.
Phase sensitivity of an SU(1,1) interferometer in photon-loss via photon operations

We study the phase sensitivity of an SU(1,1) interferometer with photon loss by using three different photon operations schemes, i.e., performing photon-addition operation on the input port of the SU(1,1) interferometer (Scheme A), the interior of SU(1,1) interferometer (Scheme B), and both of them (Scheme C). We compare the performance of the three schemes in phase estimation by performing the same times of photon-addition operation to the mode b. The results show that Scheme B improves the phase sensitivity best in ideal case, and Scheme C performs well against internal loss, especially in the case of strong loss. All the three schemes can beat the standard quantum limit in the presence of photon loss, but Scheme B and Scheme C can break through the standard quantum limit in a larger loss range.
Comparison Analysis of AHP-SAW, AHP-WP, AHP-TOPSIS Methods in Private Tutor Selection

Private tutoring was a non-formal education, it was used as an alternative by parents to help support and maximize the learning process that students get at school.Sometimes parents have difficulty in adjusting the desired and needed criteria with available alternatives or teachers.To overcome these obstacles, this research used the MADM approach in providing alternative recommendations, based on the criteria used as the basis for decision making.MADM consists of SAW, WP, TOPSIS, and AHP.The advantages of the SAW, WP, and TOPSIS methods in managing cost and benefit data were used in the ranking process.While the weaknesses of the three methods in the weighting process can be overcome by the AHP method, which was able to provide more objective weighting results.Therefore, this research aimed to analyze the comparison of the combination of AHP-SAW, AHP-WP, and AHP-TOPSIS methods in the selection of private tutors.The combination of these methods was compared based on accuracy, ranking, and preference to get the best combination of MADM methods in determining the selection of private tutors.The criteria used in this research were education, experience, cost, duration, rating, and distance.The comparison of the three combinations of methods showed the AHP-SAW method has an accuracy rate of 88.14%, AHP-WP of 68.64%, and AHP-TOPSIS of 66.95%.The average ranking showed the AHP-SAW method gave results of 91%, AHP-WP of 88%, and AHP-TOPSIS of 89%.In addition, the average preference showed the AHP-SAW method gave a value of 0.771, AHP-WP of 0.073, and AHP-TOPSIS of 0.564.Thus, it showed the AHP-SAW gave better results in the case of private tutor selection than the AHP-WP and AHP-TOPSIS.
Bayesian Collaborative Learning for Whole-Slide Image Classification

Whole-slide image (WSI) classification is fundamental to computational pathology, which is challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc. Multiple instance learning (MIL) provides a promising way towards WSI classification, which nevertheless suffers from the memory bottleneck issue inherently, due to the gigapixel high resolution. To avoid this issue, the overwhelming majority of existing approaches have to decouple the feature encoder and the MIL aggregator in MIL networks, which may largely degrade the performance. Towards this end, this paper presents a Bayesian Collaborative Learning (BCL) framework to address the memory bottleneck issue with WSI classification. Our basic idea is to introduce an auxiliary patch classifier to interact with the target MIL classifier to be learned, so that the feature encoder and the MIL aggregator in the MIL classifier can be learned collaboratively while preventing the memory bottleneck issue. Such a collaborative learning procedure is formulated under a unified Bayesian probabilistic framework and a principled Expectation-Maximization algorithm is developed to infer the optimal model parameters iteratively. As an implementation of the E-step, an effective quality-aware pseudo labeling strategy is also suggested. The proposed BCL is extensively evaluated on three publicly available WSI datasets, i.e., CAMELYON16, TCGA-NSCLC and TCGA-RCC, achieving an AUC of 95.6%, 96.0% and 97.5% respectively, which consistently outperforms all the methods compared. Comprehensive analysis and discussion will also be presented for in-depth understanding of the method. To promote future work, our source code is released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Zero-We/BCL</uri> .
Sign Language Recognition Using Landmark Detection, GRU and LSTM

Speech impairment is a kind of disability, affects individual's ability to communicate with each other. People with this problem use sign language for their communication. Though communication through sign language has been taken care of, there exists communication gap between signed and non-signed people. To overcome this type of complexity researchers are trying to develop systems using deep learning approach. The main objective of this paper is subject to implement a vision-based application that offers translation of sign language to voice message and text to reduce the gap between two kinds of people mentioned above. The proposed model extracts temporal and spatial features after taking video sequences. To extract the spatial features, MediaPipe Holistic has been used that consists of several solutions for the detecting face, had and pose landmarks. Different kind of RNN (Recurrent Neural Network) like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) have been used is to train on temporal features. By using both models and American Signed Language, 99% accuracy has been achieved. The experimental result shows that the recognition method with MediaPipe Holistic followed by GRU or LSTM can achieve a high recognition rate that meets the need of a Sign Language Recognition system that on the real-time basis. Based on the expectation, this analysis will facilitate creation of intelligent- based Sign Language Recognition systems and knowledge accumulation and provide direction to guide to the correct path.
Asymmetric Supervised Fusion-Oriented Hashing for Cross-Modal Retrieval

Hashing technologies have been widely applied for large-scale multimodal retrieval tasks owing to their excellent performance in search and storage tasks. Although some effective hashing methods have been proposed, it is still difficult to handle the intrinsic linkages that exist among different heterogeneous modalities. Moreover, optimizing the discrete constraint problem through a relaxation-based strategy results in a large quantization error and leads to a suboptimal solution. In this article, we present a novel asymmetric supervised fusion-oriented hashing method, named (ASFOH), which investigates three novel schemes to remedy the above issues. Specifically, we first explicitly formulate the problem as matrix decomposition into a common latent representation and a transformation matrix, combined with an adaptive weight scheme and nuclear norm minimization to ensure the information completeness of multimodal data. Then, we associate the common latent representation with the semantic label matrix, thereby increasing the discriminative capability of the model by constructing an asymmetric hash learning framework, thus, making the generated hash codes more compact. Finally, an efficient discrete optimization iterative algorithm based on nuclear norm minimization is proposed to decompose the nonconvex multivariate optimization problem into several subproblems with analytical solutions. Comprehensive experiments on the MIRFlirck, NUS-WIDE, and IARP-TC12 datasets testify that ASFOH outperforms the compared state-of-the-art approaches.
Motivational Readiness of Future Teachers-Philologists to Use the Gamification with Elements of Augmented Reality in Education

The article analyzes the students-philologists’ motivations to use the gamification with elements of augmented reality (AR) in education. The domestic and foreign experience of implementation of gamification with elements of AR into current educational practices is generalized. The aspects of implementation of gamification with elements of AR into the educational process of Mykhailo Stelmakh Faculty of Philology and Journalism are highlighted. In particular, the features of creating the board game “Fantaziarium (Imaginarium)” with elements of AR during the study of “Methodic of teaching literature” and “Methodic of teaching the Ukrainian language” are described. The methodological tools to diagnose the level of future teachers’ motivational readiness for the use of gamification with elements of AR are selected, and their indicators are defined. 47 students of the 4th year study of the specialty 014 “Secondary Education” (Ukrainian language and literature) of Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University took part in the survey. The results of the survey convince of the relevance and effectiveness of organization in studying with the usage of the methods of creative practice-oriented project activities. A certain sequence of steps to increase the motivational readiness of students-philologists in the use of gamification with elements of AR is provided.
High-density integrated delay line using extreme skin-depth subwavelength grating waveguides

Optical delay lines control the flow of light in time, introducing phase and group delays for engineering interferences and ultrashort pulses. Photonic integration of such optical delay lines is essential for chip-scale lightwave signal processing and pulse control. However, typical photonic delay lines based on long spiral waveguides require extensively large chip footprints, ranging from mm2 to cm2 scales. Here we present a scalable, high-density integrated delay line using a skin-depth engineered subwavelength grating waveguide, i.e., an extreme skin-depth (eskid) waveguide. The eskid waveguide suppresses the crosstalk between closely spaced waveguides, significantly saving the chip footprint area. Our eskid-based photonic delay line is easily scalable by increasing the number of turns and should improve the photonic chip integration density.