Reviewerly/exHarmony huggingface dataset:
Novel Approach for Gesture Recognition Using mmWave FMCW RADAR

Hand gesture recognition driven by RADAR technology has attracted significant attention in recent years. Among various RADAR types, frequency-modulated continuous-wave (FMCW) RADAR is used in this work due to its very high range and velocity resolution. However, data collected by RADAR are disturbed by static background and static clutter. Therefore, a novel data preprocessing approach is proposed to remove the static background and clutter in the acquired data. A convolutional neural network is used to extract the features of the acquired data set. To the best of our knowledge, this is the first time that range, velocity and angle features are combined in one map, forming the input signal of a convolutional neural network. Classifiers are applied to recognize gestures. Experimental results show that the proposed method using the XGBoost classifier can achieve a high recognition accuracy of 98.93% on the test set. In contrast, the proposed method with the random forest classifier can achieve a recognition rate of 100% on the same test set with six dynamic hand gestures. This approach could be useful in aspects such as in-car entertainment systems and smart homes.
STSIIML: Study on token shuffling under incomplete information based on machine learning

Transaction data on the public chain is open and transparent to all participants, which poses a potential threat to the privacy of participants. Some privacy-conscious token holders want to employ obfuscation methods to protect the origin and destination of their tokens, and the need for token shuffling services (TSS) arose at a historical moment. The prevailing token shuffling policies rely too heavily on blacklists in the token shuffling process, which is contrary to the idea of decentralization. Therefore, weakening or even eliminating the usage of blacklisting mechanisms in TSSs, which is an urgent issue to be addressed. In this paper, we adopt the idea of machine learning and propose a general framework for TSSs, which replaces the natural role under incomplete information with machine learning, so as to achieve the goal of eliminating the blacklist mechanism in TSSs. Then, the token shuffling process is constructed as an extended game under incomplete information on the basis of different token shuffling policies, and this incomplete information game is analyzed on the basis of poison policy, haircut policy, and suicide policy respectively. Finally, the sequential equilibrium under different games is investigated through simulations. The simulation results show that in the incomplete information game based on the poison policy, the participation of two players in the TSS is a sequential equilibrium, while in the incomplete information game based on the haircut policy, the players do not participate in the TSS is an sequential equilibrium.
Two is better than one—Examining biology-specific TPACK and its T-dimensions from two angles

Until now, TPACK has mostly been measured by subject-unspecific self-report measures. In contrast, this study introduces and analyzes an objective and biology-specific performance assessment of the four T-dimensions of TPACK. This instrument was used in combination with a biology-specific self-report measure to examine 206 biology teacher students at an Austrian university. Using two test instruments to measure the same construct allows direct comparison and increases validity. Structural equation models were calculated to analyze and compare the internal structure of the four T-dimensions within the two instruments. Although different test formats were used, for which the dimensions showed moderate correlations, the data for both test formats showed a similar internal structure and support a transformative view due to lack of direct influence of technological knowledge (TK) on TPACK.
Design and Research of Computer-Aided English Teaching Methods

Design of intelligent research systems is considered as one of the most prominent developments in multi-modal information domains in our day-to-day life. While significant growth in computer-aided English teaching methods (CAETMs) has made a progression over the past few years using techniques such as computational intelligence, biological computing aspects within the artificial intelligence domain. All the research in English teaching structures has been automated through online cloud-based applications and progressing at a rapid rate. But there are still a number of subjects that need to be explored in terms of its design, implementation, deployment of intelligent methods, and multi-agent systems in a real-world environment. However establishing teaching research subjects with novel techniques and methodologies utilized in computer vision in support of deep learning, semantic models in healthcare, organizations, and education sector are in need of further research with innovative and creative ideas. This paper will provide the emerging CAETM that solved the global needs of people in the educational domain. We will also discuss the improvements that need to be done in English teaching methods with digital computing solutions.
Exploring Nonlocal Group Sparsity Under Transform Learning for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising has been regarded as an effective and economical preprocessing step in data subsequent applications. Recent nonlocal low-rank approximation on each full band patch group has demonstrated their superiority for HSI denoising. These methods, however, directly design the low-rank regularization to the grouped patch image itself (i.e., original domain), which ignores the spatial information of the grouped patch image and cannot explores the potential structure. To address these issues, this paper proposes a nonlocal group sparsifying transform learning method (dubbed TLNLGS) for HSI denoising. Motivated by the global spectral correlation in the HSI, we firstly impose a certain low-dimensional subspace hypothesis over the HSI to prevent the heavy computation burden with the spectral band increases, and then explore a discriminatively intrinsic nonlocal group sparse prior of the reduced image by transform model. The learned group sparse prior can not only excavate the nonlocal self-similarity as recent nonlocal low-rank approximation methods but also preserve the local spatial smooth structure of the image. Moreover, compared with the fixed transform domain (e.g., gradient and discrete cosine transformation domains), the transform learning scheme can improve the sparse representation ability. An efficient block coordinate descent (BCD) algorithm is developed to solve the proposed model. Extensive experiments, including simulated and real HSI datasets, indicate the superiority of the proposed TLNLGS method over the state-of-the-art HSI denoising approaches.
Auxiliary signal-guided knowledge encoder-decoder for medical report generation

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists' working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.
CAT-MNER: Multimodal Named Entity Recognition with Knowledge-Refined Cross-Modal Attention

Multimodal named entity recognition (MNER) aims to detect and classify named entities in multimodal scenarios. It requires bridging the gap between natural language and visual context, which presents two-fold challenges: the cross-modal alignment is diversified, and the cross-modal interaction is sometimes implicit. Existing MNER methods are vulnerable to some implicit interactions and are prone to overlook the involved significant features. To tackle this problem, we novelly propose to refine the cross-modal attention by identifying and highlighting some task-salient features. The saliency of each feature is measured according to its correlation with the expanded entity label words derived from external knowledge bases. We further propose an end-to-end Transformer-based MNER framework, which holds neater architecture yet achieves better performance than previous methods. Extensive experiments are conducted to validate the merits of our method. Moreover, our method reveals a significant advantage in data efficiency and generalization ability.
Human vs robot baristas during the COVID-19 pandemic: effects of masks and vaccines on perceived safety and visit intention

Purpose This study aims to compare the effect of barista type (human vs robot) on perceived safety and examine the role of two moderators (mask-wearing and coronavirus vaccination) on the effects of barista type on perceived safety and visit intention. Design/methodology/approach The research design consists of three studies. Three experiments were sequentially designed and conducted to address research questions. Findings Study 1 found that perceived safety mediates the effect of barista type on customers’ visit intention. Study 2 revealed that the mask-wearing of human and robot baristas differently influences perceived safety. Study 3 showed that customers, especially where robot baristas are used, perceive the effect of mask-wearing differently depending on their coronavirus vaccination status. Research limitations/implications Given that the levels of restrictions vary worldwide, together with the extent of countries’ vaccination rollouts, caution is required when generalising the research findings. Practical implications The findings have practical implications for the hospitality industry, where the roles of face masks and coronavirus vaccines in shaping consumer psychology and behaviour have been underexplored. Originality/value Coronavirus vaccination is considered one of the most important driving forces for the recovery of hospitality businesses. As a heuristic-systematic model postulated, this study identified that vaccination status (fully vaccinated vs not vaccinated) changes the level of involvement when customers assess the level of risk in service environments. By pinpointing the function of service robots in safeguarding customers from the potential spread of the disease, this study broadens the scope of human–robot interaction research in hospitality.
Secure Big Data Processing in Multihoming Networks with AI-Enabled IoT

The constant investigation, as well as dispensation of data among various processing, has been influenced by computerized strategies enabled by artificial neural network associated with Internet of Things, as well as cloud-dependent organizations. Multihoming is also a category of an organization that brings together several categories of organizations in its atmosphere during the dealing with various information. These days, the massive amounts of information being handled also observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of ANN-associated frameworks among multihoming massive information using IoT as well as AI-associated frameworks could be beneficial in a variety of ways. The constant investigation and dispensation of information among various processing are influenced by computerized strategies enabled by ANN associated with IoT, as well as cloud-associated organizations. Multihoming is also a kind of organization that brings together several kinds of organizations into a unique situation while dealing with a large volume of information. When multihoming enormous amounts of data with IoT and AI-integrated systems, there are various advantages to employing AI-based solutions. Despite the fact that multihoming security difficulties and their evaluation have been carefully explored by various scientists and researchers, big data security analysis in multihoming, particularly when using automated methodologies and systems, receives little attention. These days, the massive amounts of information being handled and observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of AI-based frameworks in multihoming massive information with IoT associated with AI frameworks could be beneficial in a variety of ways. Even though multihoming security challenges and their analysis have been widely focused on by many researchers and specialists, little attention has been paid to them.
Obi-Wan

We consider the problem of integrating heterogeneous data (relational, JSON, key-values, graphs etc.) and querying it efficiently. Traditional data integration systems fall into two classes: data warehousing , where all data source content is materialized in a single repository, and mediation , where data remains in their original stores and all data can be queried through a mediator. We propose to demonstrate Obi-Wan, a novel mediator following the Ontology-Based Data access (OBDA) paradigm. Obi-Wan integrates data sources of many data models under an interface based on RDF graphs and ontologies (classes, properties, and relations between them). The novelty of Obi-Wan is to combine maximum integration power (GLAV mappings, see below) with the highest query answering power supported by an RDF mediator: RDF queries not only over the data but also over the integration ontologies. This makes it more flexible and powerful than comparable systems.
Ensemble feature selection for multi‐label text classification: An intelligent order statistics approach

Because of the overgrowth of data, especially in text format, the value and importance of multi-label text classification have increased. Aside from this, preprocessing and particularly intelligent feature selection (FS) are the most important step in classification. Each FS finds the best features based on its approach, but we try to use a multi-strategy approach to find more useful features. Evaluating and comparing features’ importance and relevance makes using multiple strategy and methods more suitable than conventional approaches because each feature is measured based on several perspectives. Nevertheless, the ensemble FS merges the final performance results of various methods to take advantage of different methods’ strengths and better classify. In this article, we have proposed an ensemble FS method for multi-label text data (MLTD) for the first time using the order statistics (EMFS) approach. We have utilized four multi-label FS (MLFS) algorithms with various particular performances to achieve a good result. In this method, as one of the most important statistics methods, Order Statistics was used to aggregate the ranks of different algorithms, which is robust against noise, redundant and inessential features. In the end, the performance of EMFS, executing six MLTDs, was evaluated according to six performance criteria (ranking-based and classification-based). Surprisingly, the proposed method was more accurate than others among all used MLTDs. The proposed method has improved by 1.5% compared to other methods. This value is based on the results obtained based on six evaluation criteria and all tested data sets.
Deep Learning-Based Object Tracking in Satellite Videos: A comprehensive survey with a new dataset

As a fundamental task for research in satellite videos (SVs), object tracking is used to track the target of interest in traffic evaluation, military security, and so forth. The current satellite technology in the remote sensing field makes it possible to track moving targets with a relatively high frame rate and image resolution. However, objects under this special view are often small and blurry, making it hard to extract deep features effectively. As a result, quite a few deep learning (DL) methods were proposed for object tracking in SVs. In addition, evaluation criteria for daily life videos (DLVs) are not fully applicable to SVs, which always get low precision evaluation results for tiny objects. In this article, we make three contributions to the research on SVs. First, a new single object tracking (SOT) dataset, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SV248S</i> , is proposed, including 248 sequences with high-precision manual annotation, and 10 kinds of attribute tags are designed to completely represent the difficulties during tracking. Second, two high-precision evaluation methods are proposed, especially for small object tracking. Finally, 28 DL-based state-of-the-art (SOTA) tracking methods, from 2017 to 2021, covering popular frameworks, are evaluated and compared on the proposed dataset. Furthermore, some guidelines for effectively adopting DL-based methods are summarized based on comprehensive experimental results.
Developing a digital competence scale for teachers: validity and reliability study

Teachers' digital competence is very significant in terms of integrating digital technologies into the education process. This study aims to develop an up-to-date scale that can determine the digital competencies required for teachers to acquire new skills that arise with the change and development of technology and use them in educational environments. A total of 695 teachers participated in the study. Exploratory and confirmatory factor analyses were used to examine the construct validity of the scale. To assess the discrimination index of the items, the lower 27% and upper 27% groups were determined, and the differences between the groups were examined. Internal consistency coefficients were calculated for the reliability analysis. According to the results of the analysis, the developed scale consists of six factors and 46 items, and the Cronbach Alpha coefficient of the entire scale is 0.975. The factors were identified as "Safety," "Data Literacy," "Problem Solving," "Digital Content Creation," "Communication and Collaboration," and "Ethics," respectively, according to the content of the items. When compared with the DigComp 2.1 framework developed by the European Union, it was determined that the ethical factor emerged differently in this study. As a result, it can be said that the Digital Competency Scale for Teachers is a valid and reliable scale that can be used to measure teachers' digital competencies.
The potentials and trends of holography in education: A scoping review

The advancement of digital hologram technology has conceded exciting possibilities in the educational sector pushing the temporal and spatial boundaries with enhanced virtual presence and interactions. The application and adaptation of the technology have started off in the classrooms mainly in medical education, science, and engineering education in the higher education sites. With this, the studies on the use of holograms and their educational effects have been also on an increase. This scoping review examines the literature in the past decade and provides a comprehensive overview of the research on using holography in educational sites. The results show that although the number of studies on the use of holography is on the rise in recent five years, studies on educational effects are limited to small age groups, subjects, and the effectiveness constructs that measure learning outcomes are still scattered. This review contributes to the bibliometric and thematic mapping of literature and the identification of gaps for future research.
Sentiment interaction and multi-graph perception with graph convolutional networks for aspect-based sentiment analysis

Graph convolutional networks have been successfully applied to aspect-based sentiment analysis, due to their ability to flexibly capture syntactic information and word dependencies. However, most existing graph network-based models only consider the syntactic dependencies between specific aspects and contexts. These cannot capture the internal semantic correlations within aspect-specific phrases and ignore the sentiment interaction relations between different aspects of a sentence. In this paper, we propose a novel graph convolutional network with sentiment interaction and multi-graph perception for aspect-based sentiment analysis. The proposed model considers the complementarity of semantic dependencies and sentiment interactions simultaneously. Specifically, we generate four types of adjacency graphs by integrating the internal semantic correlations between aspect phrases and linking the sentiment interaction relations among different aspects. Adjacency graphs are used to construct graph convolutional neural networks to enrich aspect-centric dependencies and enhance the capability of context-awareness. In addition, we construct a multi-graph perception mechanism to capture the specific dependency information that cannot be captured between different graphs and hence reduce the amount of overlapping information. Experimental results on five publicly-available datasets demonstrate that our proposed model outperforms state-of-the-art methods and achieves the best performance in terms of accuracy and macro-F1 score.
Virtual reality for the enhancement of cultural tangible and intangible heritage: The case study of the Castle of Corsano

This paper concerns the development of an immersive VR application for the enhancement of an inaccessible old Castle in Corsano, a small village in Salento - Italy. Starting from the 3D reconstruction of the building, the project allowed the development of an interaction system aimed at providing the user with the historical information about the Castle. These contents come from the analysis of the tangible cultural heritage (such as the architectural elements, the furniture, the decorative motifs of the castle, and their evolution through the centuries), but also from the collection of intangible heritage, such as reminiscences on folk customs and traditions related to the context of the castle. Moreover, in order to evaluate the user experience of the developed application, some tests were carried out on a heterogeneous sample of users, obtaining positive feedback on the degree of immersiveness and sense of presence of the application.