Reviewerly/exHarmony huggingface dataset:
CICHMKG: a large-scale and comprehensive Chinese intangible cultural heritage multimodal knowledge graph

Abstract Intangible Cultural Heritage (ICH) witnesses human creativity and wisdom in long histories, composed of a variety of immaterial manifestations. The rapid development of digital technologies accelerates the record of ICH, generating a sheer number of heterogenous data but in a state of fragmentation. To resolve that, existing studies mainly adopt approaches of knowledge graphs (KGs) which can provide rich knowledge representation. However, most KGs are text-based and text-derived, and incapable to give related images and empower downstream multimodal tasks, which is also unbeneficial for the public to establish the visual perception and comprehend ICH completely especially when they do not have the related ICH knowledge. Hence, aimed at that, we propose to, taking the Chinese nation-level ICH list as an example, construct a large-scale and comprehensive Multimodal Knowledge Graph (CICHMKG) combining text and image entities from multiple data sources and give a practical construction framework. Additionally, in this paper, to select representative images for ICH entities, we propose a method composed of the denoising algorithm (CNIFA) and a series of criteria, utilizing global and local visual features of images and textual features of captions. Extensive empirical experiments demonstrate its effectiveness. Lastly, we construct the CICHMKG, consisting of 1,774,005 triples, and visualize it to facilitate the interactions and help the public dive into ICH deeply.
Copper, Uranium and REE Mineralisation in an Exhumed Oil Reservoir, Southwest Orkney, Scotland

Copper, uranium, and rare earth element (REE) mineralisation occurs in hydrocarbon-bearing Devonian continental sandstones in southwest Orkney, Scotland. The aeolian Yesnaby Sandstone Formation and fluvial Harra Ebb Sandstone Formation were mineralised following oil emplacement. The REE-bearing APS mineral florencite is particularly associated with bituminous nodules, many of which contain brannerite. Subsequently hydrothermal copper and other sulphides, and barite, further mineralised the oil reservoir at a temperature of ~190 °C. Oil was mobilised through mineralised fractures at this stage. Biodegradation of the oil occurred later, following the Carboniferous-Permian uplift. The occurrence confirms that Cu-APS mineralisation is possible in relatively low-temperature regimes in sedimentary basins.
An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.
A machine learning based algorithm for joint improvement of power control, link adaptation, and capacity in beyond 5G communication systems

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
Object Detection of Flexible Objects with Arbitrary Orientation Based on Rotation-Adaptive YOLOv5

It is challenging to accurately detect flexible objects with arbitrary orientation from monitoring images in power grid maintenance and inspection sites. This is because these images exhibit a significant imbalance between the foreground and background, which can lead to low detection accuracy when using a horizontal bounding box (HBB) as the detector in general object detection algorithms. Existing multi-oriented detection algorithms that use irregular polygons as the detector can improve accuracy to some extent, but their accuracy is limited due to boundary problems during the training process. This paper proposes a rotation-adaptive YOLOv5 (R_YOLOv5) with a rotated bounding box (RBB) to detect flexible objects with arbitrary orientation, effectively addressing the above issues and achieving high accuracy. Firstly, a long-side representation method is used to add the degree of freedom (DOF) for bounding boxes, enabling accurate detection of flexible objects with large spans, deformable shapes, and small foreground-to-background ratios. Furthermore, the further boundary problem induced by the proposed bounding box strategy is overcome by using classification discretization and symmetric function mapping methods. Finally, the loss function is optimized to ensure training convergence for the new bounding box. To meet various practical requirements, we propose four models with different scales based on YOLOv5, namely R_YOLOv5s, R_YOLOv5m, R_YOLOv5l, and R_YOLOv5x. Experimental results demonstrate that these four models achieve mean average precision (mAP) values of 0.712, 0.731, 0.736, and 0.745 on the DOTA-v1.5 dataset and 0.579, 0.629, 0.689, and 0.713 on our self-built FO dataset, exhibiting higher recognition accuracy and a stronger generalization ability. Among them, R_YOLOv5x achieves a mAP that is about 6.84% higher than ReDet on the DOTAv-1.5 dataset and at least 2% higher than the original YOLOv5 model on the FO dataset.
Effects of the Interactive Features of Virtual Partner on Individual Exercise Level and Exercise Perception

We designed an exercise system in which the user is accompanied by a virtual partner (VP) and tested bodyweight squat performance with different interactive VP features to explore the comprehensive impact of these VP features on the individual's exercise level (EL) and exercise perception.This experiment used three interactive features of VP, including body movement (BM), eye gaze (EG), and sports performance (SP), as independent variables, and the exercise level (EL), subjective exercise enjoyment, attitude toward the team formed with the VP, and local muscle fatigue degree of the exerciser as observational indicators. We designed a 2 (with or without VP's BM) × 2 (with or without VP's EG) × 2 (with or without VP's SP) within-participants factorial experiment. A total of 40 college students were invited to complete 320 groups of experiments.(1) Regarding EL, the main effects of BM and SP were significant (p < 0.001). The pairwise interaction effects of the three independent variables on EL were all significant (p < 0.05). (2) Regarding exercise perception, the main effects of BM (p < 0.001) and EG (p < 0.001) on subjective exercise enjoyment were significant. The main effect of BM on the attitude toward the sports team formed with the VP was significant (p < 0.001). The interaction effect of BM and SP on the attitude toward the sports team formed with the VP was significant (p < 0.001). (3) Regarding the degree of local muscle fatigue, the main effects of BM, EG, and SP and their interaction effects were not significant (p > 0.05).BM and EG from the VP elevate EL and exercise perception during squat exercises, while the VP with SP inhibited the EL and harmed exercise perception. The conclusions of this study can provide references to guide the interactive design of VP-accompanied exercise systems.
PNANet: Probabilistic Two-Stage Detector Using Pyramid Non-Local Attention

Object detection algorithms require compact structures, reasonable probability interpretability, and strong detection ability for small targets. However, mainstream second-order object detectors lack reasonable probability interpretability, have structural redundancy, and cannot fully utilize information from each branch of the first stage. Non-local attention can improve sensitivity to small targets, but most of them are limited to a single scale. To address these issues, we propose PNANet, a two-stage object detector with a probability interpretable framework. We propose a robust proposal generator as the first stage of the network and use cascade RCNN as the second stage. We also propose a pyramid non-local attention module that breaks the scale constraint and improves overall performance, especially in small target detection. Our algorithm can be used for instance segmentation after adding a simple segmentation head. Testing on COCO and Pascal VOC datasets as well as practical applications demonstrated good results in both object detection and instance segmentation tasks.
Digital teaching competencies and disability. Validation of a questionnaire design using the K coefficient to select experts

Higher education is one of the types of education most influenced by digital technologies. This situation, in educational contexts of quality and equity, presents different advantages but, at the same time, also many challenges. One of them is the use of ICT to support students with disabilities. In this line, the main objective of this study is to evaluate an instrument to measure the level of training and knowledge of university teachers in Spain concerning the application of ICT as support for students with disabilities. For the validation of content, the technique of "expert judgement" was used, applying a process of expert selection called "Expert Competence Coefficient" or "K Coefficient". The instrument's reliability index was obtained through two statistics, Cronbach's alpha and McDonald's Omega. From the results obtained, it is confirmed that the questionnaire under study is an instrument with evidence of validity and reliability that allows us to diagnose, among university teaching staff, sub-dimensions of special relevance to find out the level of training and knowledge they have about ICT and students with disabilities.
Trainable PQC-Based QRAM for Quantum Storage

Quantum Machine Learning (QML) is a new domain of Machine Learning (ML) and Quantum Computing (QC) that uses a trainable Variational Quantum Circuit (VQC) to solve various learning problems.Classical data is transformed to the quantum Hilbert space using an embedding scheme and the VQC performs quantum operations on this transformed quantum data using parametric quantum gates.In this work, we present a new embedding scheme using Quantum Random Access Memory (QRAM) that takes address as input, and gives data as the output similar to a classical Random Access Memory (RAM).We propose a basic circuit-based QRAM architecture and its application in, (a) storage for ML usage and, (b) storage of binary data.For ML, we store images of digits dataset into the QRAM and perform binary classification using a Quantum Neural Network (QNN).We observe that QNN driven by the proposed QRAM converges faster (at 6 th epoch) with 100% classification accuracy compared to QNN with normal embedding and classical Fully Connected Neural Network (FCNN) setups which converge with same accuracy at around 10 th and 15 th epochs, respectively.We obtain ≈ 63% classification accuracy on real hardware from IBM.For storage of binary data, we measure Hamming Distance (HD) and percentage correct predictions for quality evaluation of the proposed QRAM.We observe that the HD and percentage correct predictions worsens as the number of address lines increases.We propose two techniques to improve the QRAM accuracy namely, (i) clustering of raw data and allocating one QRAM per cluster and, (ii) bit splitting to divide wider data into smaller chunks and allocating one QRAM per split.Clustering provides best results by improving the HD for 9-address QRAM by ≈1.95X than pure QRAM and ≈1.74X improvement compared to bit-split (which provides ≈1.11X improvement than pure QRAM).
Application of quantum computing in discrete portfolio optimization

This study proposes a novel and more efficient quantum algorithm for portfolio optimization using quantum combinatorial optimization (QCO) techniques. A recent construction developed in 2021 has sparked the field of financial portfolio optimization through the Quantum Walk Optimization Algorithm (QWOA). In this study, we investigated the complexity and efficiency of quantum optimization algorithms with a special interest in QWOA. The objective is to minimize investment risk by having a good combination of assets in the portfolio. We also focused on reducing the number of iterations while attaining a high-quality resolution through contraction of the solution space to ease computations. The concept of QWOA was extended by constructing a newly outperforming scheme known as the "Quantum Mix Optimization Algorithm (QMOA)." QMOA algorithm codes were provided for the implementation and simulation of numerical results. In addition, the efficiency of QMOA, which is better than the existing QCO algorithms, was discussed. For instance, the least QWOA number of computations required to execute the initial state equation was p > 2, whereas this value was p ≥ 2 in the proposed QMOA.
From 2D to 3D video conferencing: modular RGB-D capture and reconstruction for interactive natural user representations in immersive extended reality (XR) communication

With recent advancements in Virtual Reality (VR) and Augmented Reality (AR) hardware, many new immersive Extended Reality (XR) applications and services arose. One challenge that remains is to solve the social isolation often felt in these extended reality experiences and to enable a natural multi-user communication with high Social Presence. While a multitude of solutions exist to address this issue with computer-generated “artificial” avatars (based on pre-rendered 3D models), this form of user representation might not be sufficient for conveying a sense of co-presence for many use cases. In particular, for personal communication (for example, with family, doctor, or sales representatives) or for applications requiring photorealistic rendering. One alternative solution is to capture users (and objects) with the help of RGBD sensors to allow real-time photorealistic representations of users. In this paper, we present a complete and modular RGBD capture application and outline the different steps needed to utilize RGBD as means of photorealistic 3D user representations. We outline different capture modalities, as well as individual functional processing blocks, with its advantages and disadvantages. We evaluate our approach in two ways, a technical evaluation of the operation of the different modules and two small-scale user evaluations within integrated applications. The integrated applications present the use of the modular RGBD capture in both augmented reality and virtual reality communication application use cases, tested in realistic real-world settings. Our examples show that the proposed modular capture and reconstruction pipeline allows for easy evaluation and extension of each step of the processing pipeline. Furthermore, it allows parallel code execution, keeping performance overhead and delay low. Finally, our proposed methods show that an integration of 3D photorealistic user representations into existing video communication transmission systems is feasible and allows for new immersive extended reality applications.
Extended Reality Vocational Training’s Ability to Improve Soft Skills Development and Increase Equity in the Workforce

With the rise of the metaverse, extended reality (XR), which includes virtual, augmented, and mixed reality, has emerged as a key alternative education medium alongside more traditional online and onsite approaches. The onset of COVID-19 accelerated the efforts of private education companies in this sector to explore enhanced education opportunities in vocational training. Companies that produce immersive learning platforms integrate partners, content creators, and customers, focusing on the future by supporting soft skills. Immersive learning has helped a substantial number of workers to use XR learning methods to acquire knowledge faster, measure and validate their skills, and find upward mobility in the workforce through improved, on-demand accessibility options. This article examines an XR development plan through a diffusion of innovation framework coupled with social capital theory. A literature review reveals organizations’ ability to capitalize on soft skills development while expanding global accessibility options for adult learners to improve learning equity opportunities.
A Hierarchical Clustering Obstacle Detection Method Applied to RGB-D Cameras

Environment perception is a key part of robot self-controlled motion. When using vision to accomplish obstacle detection tasks, it is difficult for deep learning methods to detect all obstacles due to complex environment and vision limitations, and it is difficult for traditional methods to meet real-time requirements when applied to embedded platforms. In this paper, a fast obstacle-detection process applied to RGB-D cameras is proposed. The process has three main steps, feature point extraction, noise removal, and obstacle clustering. Using Canny and Shi–Tomasi algorithms to complete the pre-processing and feature point extraction, filtering noise based on geometry, grouping obstacles with different depths based on the basic principle that the feature points on the same object contour must be continuous or within the same depth in the view of RGB-D camera, and then doing further segmentation from the horizontal direction to complete the obstacle clustering work. The method omits the iterative computation process required by traditional methods and greatly reduces the memory and time overhead. After experimental verification, the proposed method has a comprehensive recognition accuracy of 82.41%, which is 4.13% and 19.34% higher than that of RSC and traditional methods, respectively, and recognition accuracy of 91.72% under normal illumination, with a recognition speed of more than 20 FPS on the embedded platform; at the same time, all detections can be achieved within 1 m under normal illumination, and the detection error is no more than 2 cm within 3 m.
DisguisOR: holistic face anonymization for the operating room

Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow recognition show potential in increasing the quality of patient care, the quantity of video data has surpassed the scale at which images can be manually anonymized. Existing automated 2D anonymization methods under-perform in Operating Rooms (OR), due to occlusions and obstructions. We propose to anonymize multi-view OR recordings using 3D data from multiple camera streams.RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene. We then detect each individual's face in 3D by regressing a parametric human mesh model onto detected 3D human keypoints and aligning the face mesh with the fused 3D point cloud. The mesh model is rendered into every acquired camera view, replacing each individual's face.Our method shows promise in locating faces at a higher rate than existing approaches. DisguisOR produces geometrically consistent anonymizations for each camera view, enabling more realistic anonymization that is less detrimental to downstream tasks.Frequent obstructions and crowding in operating rooms leaves significant room for improvement for off-the-shelf anonymization methods. DisguisOR addresses privacy on a scene level and has the potential to facilitate further research in SDS.
Adaptive Image Enhancement Algorithm Based on Variable Step Fruit Fly Optimization Algorithm and Nonlinear Beta Transform

Due to the traditional use of manual methods for the parameter adjustment of a nonlinear beta transform, which is inefficient and unstable, an adaptive image enhancement algorithm based on a variable step size fruit fly optimization algorithm and a nonlinear beta transform is proposed. Utilizing the intelligent optimization characteristics of the fruit fly algorithm, we automatically optimize the adjustment parameters of a nonlinear beta transform to achieve better image enhancement effects. Firstly, the dynamic step size mechanism is introduced into the fruit fly optimization algorithm (FOA) to obtain a variable step size fruit fly optimization algorithm (VFOA). Then, with the adjustment parameters of the nonlinear beta transform as the optimization object, and the gray variance of the image as the fitness function, an adaptive image enhancement algorithm (VFOA-Beta) is obtained by combining the improved fruit fly optimization algorithm with the nonlinear beta function. Finally, nine sets of photos were used to test the VFOA-Beta algorithm, while seven other algorithms were used for comparative experiments. The test results show that the VFOA-Beta algorithm can significantly enhance images and achieve better visual effects, which has a certain practical application value.