Reviewerly/exHarmony huggingface dataset:
The Usability of Augmented Reality in Open and Distance Learning Systems: A Qualitative Delphi Study

The main purpose of this research is to determine the usability of augmented reality in open and distance learning environments in accordance with universal design principles, and to make predictions for the future by gathering expert opinions on this subject using the Delphi technique. The Delphi technique was applied to 14 expert participants for 3 rounds as the primary data collection tool with open ended questions based on the theoretical framework. Structured interview questions were used as a secondary data collection tool and were applied during an academic exchange in China. In the Delphi technique used as the primary data collection tool, 92 themes were evaluated by experts and accepted as usability principles by end of this research. Therefore, 92 themes under 21 titles were presented for the use of augmented reality within the framework of universal design principles in open and distance learning. This research may be the first unique study on the usability of augmented reality not just as the convergence of this technology with open and distance learning environments but also incorporating the learning and communication dimensions of this convergence, thereby contributing to the literature of the field. It is imperative to determine the usability of augmented reality in open and distance learning, along with the “how” of this use in application. In this regard, the findings of this study are significant in shedding light to the enrichment, diversification and increased interaction of open and distance learning environments in accordance with universal design principles, bringing a new perspective to how a different technology convergence may be conducted, providing further accessibility.
A liberal arts and sciences education at the Russian higher school: concept, formats, benefits and limitations

О. С. Гилязова, И. И. Замощанский, А
Does Higher Education Promote Firm Innovation in China?

Endogenous growth theories have underpinned the pivotal role of education in innovation. However, our empirical study uncovers a mixed effect of higher education on firm innovation in China. Using Chinese Patent Census Data, a unique dataset, this paper is able to quantify innovation in China by incorporating a quality dimension for the first time. By merging the patent data with the Chinese Industrial Enterprise Database and province-level data on education, we find that the number of higher education institutions has a negative impact on firm-level innovation. However, the quantity of elite higher education institutions at the provincial level exerts a positive impact on firm innovation. In addition, heterogeneity analyses show that the effect of elite higher education on firm innovation is significantly positive for privately owned enterprises, but insignificant for state- and foreign-owned enterprises. Furthermore, the positive effect of elite higher education on innovation in high-tech industries is larger than in other industries.
Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking

In the robust image watermarking framework, watermarks are usually embedded in the direct current (DC) coefficients in discrete cosine transform (DCT) domain, since the DC coefficients have a larger perceptual capacity than any alternating current (AC) coefficients. However, DC coefficients are also excluded from watermark embedding with the consideration of avoiding block artifacts in watermarked images. Studies on human vision suggest that perceptual characteristics can achieve better image fidelity. With this perspective, we propose a novel spatial–perceptual embedding for a color image watermarking algorithm that includes the robust just-noticeable difference (JND) guidance. The logarithmic transform function is used for quantization embedding. Meanwhile, an adaptive quantization step is modeled by incorporating the partial AC coefficients. The novelty and effectiveness of the proposed framework are supported by JND perceptual guidance for spatial pixels. Experiments validate that the proposed watermarking algorithm produces a significantly better performance.
Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures.
Effective Emotion Transplantation in an End-to-End Text-to-Speech System

In this paper, we propose an effective technique to transplant a source speaker’s emotional expression to a new target speaker’s voice within an end-to-end text-to-speech (TTS) framework. We modify an expressive TTS model pre-trained using a source speaker’s emotional speech database to reflect the voice characteristics of a target speaker for which only a neutral speech database is available. We set two adaptation criteria to achieve this. One criterion is to minimize the reconstruction loss between the target speaker’s recorded and synthesized speech, such that the synthesized speech has the target speaker’s voice characteristics. The other criterion is to minimize the emotion loss between the emotion embedding vectors extracted from the reference expressive speech and the target speaker’s synthesized expressive speech, which is essential to preserve expressiveness. Since the two criteria are applied alternately in the adaptation process, we are able to avoid the kind of bias issues frequently encountered in similar tasks. The proposed adaptation technique demonstrates more effective performance compared to conventional approaches in both quantitative and qualitative evaluations.
Decentralised Autonomous Organisations and the Corporate Form

It has been suggested that the development of decentralised autonomous organisations (DAOs) will lead to a paradigm shift in the way we perceive businesses. DAOs ostensibly eliminate agency costs due to the absence of a board of directors, automated governance mechanisms and transparency provided by the blockchain upon which the DAO is launched. This article undertakes a comparative analysis between DAOs and corporations and questions whether DAOs really do improve the corporate form. Using a corporate governance and legal realist lens, this article suggests that a number of the purported benefits of DAOs are overly simplified. Moreover, there are several practical and legal obstacles that technological advancements and improved engineering must overcome before DAOs become a viable, mainstream organisational structure. Balancing the inevitable improvement in technology against these significant obstacles, this article predicts an incremental integration of DAOs into society through a hybrid approach, involving interim legal solutions and varying degrees of automation and decentralisation.
Ship Target Discrimination in SAR Images Based on BOW Model With Multiple Features and Spatial Pyramid Matching

To eliminate the false alarms in the ship target detection effectively for synthetic aperture radar (SAR) images in complex scenes, this article present a novel ship target discrimination algorithm based on bag of words (BOW) model with multiple features and spatial pyramid matching (SPM), which is named MF-SPM-BOW.The proposed discrimination method mainly contains three stages.First, the SAR scale-invariant feature transform (SAR-SIFT) descriptors and gray-level co-occurrence matrix (GLCM) descriptors are extracted as local features to describe the gradient information and texture information of local regions of an image chip.Then, the SPM technique considering its spatial location information-keeping capability is employed to generate global features with excellent discrimination ability.Finally, the support vector machine (SVM) discriminator based on multiple kernel learning is applied to realize feature fusion in image layer and thus identify targets and clutter.Experimental results show that compared with the traditional discrimination methods and the BOW model discrimination methods, the proposed SAR ship target discrimination algorithm achieves better discrimination performance, which can eliminate most of the false alarms in candidate ship target chips effectively.INDEX TERMS SAR images, ship targets discrimination, bag of words (BOW), local features, global features, spatial pyramid matching (SPM).
Contextual Information Retrieval within Recommender System: Case Study "E-learning System"

This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition…) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.
Spatial–temporal graph attention networks for skeleton-based action recognition

Human action recognition based on skeleton currently has attracted a wide range of attention. The structure of skeleton data exists in the form of graph, thus most researchers use graph convolutional networks (GCN) to model skeleton sequences. However, the graph convolution network shares the same weight for all neighbor nodes and relies on the connection of graph edges. We introduce a method, a spatial–temporal graph attention networks (ST-GAT), to overcome the disadvantages of GCN. First, the ST-GAT defines the spatial–temporal neighbor nodes of the root node and the aggregation function through the attention mechanism. The adjacency matrix is only used in GAT to define related nodes, and the calculation of association weight is dependent on the feature expression of nodes. Then ST-GAT network attaches the obtained attention coefficient to each neighbor node to automatically learn the representation of spatiotemporal skeletal features and output the classification results. Extensive experiments on two challenging datasets consistently demonstrate the superiority of our method.
Single-shot face and landmarks detector

Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.
MOVIE RECOMMENDATION SYSTEM USING ITEM BASED COLLABORATIVE FILTERING

In today's digital world where there is an endless variety of content consumed such as books, videos, articles, Films, etc., finding material of one's choice has become an infallible task.Digital content on the other hand Providers want to engage more and more users in their service for maximum time.Where is it the recommender system comes into picture where content providers advise users by content User choice in this paper we have proposed a movie recommendation system .Purpose of movie recommendation system aims to provide users with accurate movie recommendations.Usually basic recommendation system to make recommendations consider one of the following factors; User preference known as content based Filtering or the preference of similar users known as collaborative filtering.To create a stable and accurate recommender system will use of content based filtering.
Analog-Quantum Feature Mapping for Machine-Learning Applications

Quantum information processing is likely to have a far-reaching impact in the field of artificial intelligence. Noisy, intermediate-scale quantum devices provide a platform for exploring the possibility of attaining a quantum advantage through hybrid quantum-classical machine-learning algorithms. One example of such a hybrid algorithm is ``quantum kitchen sinks,'' which builds upon a classical algorithm known as ``random kitchen sinks'' to leverage a gate model quantum computer for machine-learning applications. We propose an alternative algorithm called ``analog-quantum kitchen sinks'' (AQKSs), which employs an analog-quantum computer for mapping data features into new features in a nonlinear manner. The new features can then be used by a classical algorithm to perform machine-learning tasks. We show the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset by simulating the operations of a quantum annealer. We demonstrate that the AQKS algorithm reduces the classification error of a linear classifier from $50\mathrm{%}$ to $0.6\mathrm{%}$ for the synthetic dataset and from $4.4\mathrm{%}$ to $1.6\mathrm{%}$ for the other dataset. Our proposed AQKS algorithm presents the possibility to use current quantum annealers for solving practical machine-learning problems.
Using Augmented Reality (AR) for Enhancing Chinese Vocabulary Learning

This research aims to develop a mobile AR application for enhancing Chinese vocabulary learning to students who learn Chinese as a main subject. The requirement was gathered from Chinese teachers by interview to assess a suitable content and educational technology for students. The result showed that augmented reality is the best choice. Then, 3D model , illustration, sound were drawn based on the contents and the software design and implementation were performed. After finishing this development process, Grade 10th students majoring in Chinese, at School A, Ang Thong provience. tested this application. The evaluation process was performed using pretest and posttest data analyzed by a paired samples t-test at a 95% confidence level. The result of this study shows that the application is an efficient tool to help students improve their learning capabilities.
Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.
An Innovative Multifunctional Buoy Design for Monitoring Continuous Environmental Dynamics at Tianjin Port

The Tianjin port plays a relevant role in driving both ship navigation and the weather and climate of the area. To better understand the underlying peculiarities of this area, several in-service coastal ocean system and facilities have been constructed, at present, the whole coastal ocean systems at Tianjin port have been improved, however, they cannot meet the safety requirements of traffic management and navigational safety. Marine buoys have the unreplaceable advantages of long-term, real-time, reliable capabilities and trivial environmental restrictions to monitor the marine dynamics. Here an innovative multifunctional buoy prototype is proposed to continuously measure meteorological and oceanographic parameters at a high spatial and temporal resolution, which can ensure navigation safety at Tianjin Port. Solar panels and battery module are personalized to ensure electric supply to the buoy system. An independent ARM (Advanced RISC Machine)-based module is affiliated to maintain the stability of the data acquisition and communication module. Buoy platform based on Beidou difference is proved to be an effective approach for the tide measurement. Additionally, a web-designed software for data acquisition is integrated for the visualization purpose. Finally, in-field recording test at Tianjin port will be performed to verify effectiveness of our system on monitoring environment dynamics. It turns out that our buoy prototype functions as a reliable and energy favorable electrical device and shed light on the development of effective measurement instruments working under unpredictable and harsh marine environment.
Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.
Defense against adversarial attacks in traffic sign images identification based on 5G

Abstract In the past decade, artificial intelligence and Internet of things (IoT) technology have been rapid development, gradually began to integrate with each other, especially in coming 5G era. Admittedly, image recognition is the key technology due to a huge number of video cameras integrated in intelligent IoT equipment, such as driverless cars. However, the rapidly growing body of research in adversarial machine learning has demonstrated that the deep learning architectures are vulnerable to adversarial examples. Thus, the raises questions about the security of intelligent Internet of thing (IoT) and trust sensitive areas. This emphasizes the urgent need for practical defense technology that can be deployed to real-time combat attacks at any time. Well-crafted small perturbations lead to the misclassification of legitimate images by neural networks, but not the human visual system. It is worth noting that many attack strategies are designed to disrupt image pixels in a visually imperceptible manner. Therefore, we propose a new defense method and take full advantage of 5G high-speed bandwidth and mobile edge computing (MEC) effectively. We use singular value decomposition (SVD) which is the optimal approximation of matrix in the sense of square loss to eliminate the perturbation. We have conducted extensive and large-scale experiments with German Traffic Sign Recognition Benchmark (GTSRB) datasets and the results show that adversarial attacks, such as Carlini-Wagner’s l 2 , Deepfool, and I-FSGM, can be better eliminated by the method and provide lower latency.
THE EFFECTS OF CALL (COMPUTER ASSISTED LANGUAGE LEARNING) TOWARD THE STUDENTS’ ENGLISH ACHIEVEMENT AND ATTITUDE

The objectives of research this research was to find out whether or not the CALL affects the students’ achievement and to find out the students’ attitude in learning English by using CALL. This research applied quasi-experimental method was used in this research by using non-equivalent control class design. The researcher chose and placed the experimental and control design randomly. The researcher took the first-year students of SMK Negeri 1 Pancarijang Kabupaten Sidrap in academic year 2019-2020 as a research subject. Based on the data found in this research, the researcher concludes that; 1) The use of CALL affected the achievement of the first year students of SMK Negeri 1 Pancarijang Kabupaten Sidrap in academic 2012/2013. It was proved by the mean score of pretest and posttest which was obtained from experimental class was 52.44 and 74.59. While the mean scores of pretest and posttest for the control class were 48.03 and 56.48. After analyzing using SPSS 22.0, the researcher found that (0.00&lt;0.05) where the p-value (0.00) at the level of significance (0.05) and the degree of freedom 66. It indicated that the alternative hypothesis (H1) was accepted and, of course, the null hypothesis (H0) was rejected. 2) The first-year students of SMK Negeri 1 Pancarijang Kabupaten Sidrap have a positive attitude in learning English through CALL. It was approved by the data that none of the student states negative statement to the use of CALL, 21 students (61.8%) was very favorable who get a score in interval 85-100 and 13 students (38.2%) were favorable in interval 69-84. From the data, the highest score of the students of the students is 100 which is categorized as very favorable, and the lowest score is 70 which is categorized favorable.