Reviewerly/exHarmony huggingface dataset:
Non-fungible tokens, decentralized autonomous organizations, Web 3.0, and the metaverse in education: From university to metaversity

The pandemic has accelerated technological advances in higher education. Learning management systems that make use of the persistent platform, a wide range of communication options, and profoundly immersive qualities quickly become the platform of choice for learning management systems. The blockchain enables non-fungible tokens (NFTs) and uses the blockchain architecture to validate college diplomas and transcripts, opening the way for a diverse variety of e-portfolios owned and shared by students and teachers. Another type of blockchain that is relatively new to the education scene is decentralized autonomous organizations (DAOs). Through this, technology courses, certificates, and more can become automated and authenticated on the blockchain. The current state of the web is rapidly evolving into what is known as Web 3.0. It is the emerging evolution of the internet to make it machine-readable, which includes leveraging technologies such as artificial intelligence (AI), the cloud, and distributed ledger technologies such as blockchain. From the perspectives of the university and the metaverse, this paper discusses the shift in education brought about by NFTs, DAOs, Web 3.0, and the metaverse. The powerful mix of Web 3.0 and the metaverse is about to revolutionize the way people learn and teach in the modern world.
A novel Siamese network object tracking algorithm based on tensor space mapping and memory-learning mechanism

The tracker is a core component of the tracking algorithm, but it is difficult to identify the object, which is a challenge to improve the tracking accuracy. This paper proposes a Siamese network-based tracking algorithm based on tensor space mapping and memory-learning mechanisms. Firstly, the source image is mapped to the tensor space to serialize the feature distributions. Then the gating mechanism is used to extract the association information about the adjacent state, which guides the update of the subsequent state, and the interactive information on the objects is used to locate the object. On this basis, a memory-learning module is built to traverse and extract the fine-grained features, which can filter the semantic information of the object learned by the tracker. As a result, the tracking accuracy is enhanced. The experiments show that the proposed algorithm has better performance than that of the comparison methods in the OTB100 data set and the VOT data set.
BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
Hybrid High Dynamic Range Imaging fusing Neuromorphic and Conventional Images

Reconstruction of high dynamic range image from a single low dynamic range image captured by a conventional RGB camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras like event camera and spike camera can record high dynamic range scenes in the form of intensity maps, but with much lower spatial resolution and no color information. In this article, we propose a hybrid imaging system (denoted as NeurImg) that captures and fuses the visual information from a neuromorphic camera and ordinary images from an RGB camera to reconstruct high-quality high dynamic range images and videos. The proposed NeurImg-HDR+ network consists of specially designed modules, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images to reconstruct high-resolution, high dynamic range images and videos. We capture a test dataset of hybrid signals on various HDR scenes using the hybrid camera, and analyze the advantages of the proposed fusing strategy by comparing it to state-of-the-art inverse tone mapping methods and merging two low dynamic range images approaches. Quantitative and qualitative experiments on both synthetic data and real-world scenarios demonstrate the effectiveness of the proposed hybrid high dynamic range imaging system. Code and dataset can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hjynwa/NeurImg-HDR</uri>
Synthesizing Realistic Trajectory Data With Differential Privacy

Vehicle trajectory data is critical for traffic management and location-based services. However, the released trajectories raise serious privacy concerns because they contain sensitive information such as homes and workplaces. Based on differential privacy, this problem can be addressed by generating synthetic trajectories from the original sensitive data while guaranteeing personal privacy. Unfortunately, existing methods focus on synthesizing trajectory datasets that preserve summary-level statistics (e.g., the overall distribution of user movements), making these synthetic trajectories lose individual-level mobility patterns. As shown in our experiment, this results in the low performance of their synthetic datasets in real-world applications. To address these limitations, we propose a novel solution for Synthesizing Private and Realistic Trajectories, namely SPRT, whose key idea is to integrate the public geography structures of the target area into the process of private trajectory synthesis. This enables us to capture more accurate mobility patterns to synthesize realistic trajectories, which can preserve both summary-level statistics and individual-level mobility behaviors. Consequently, the synthetic trajectories generated by SPRT are more similar to real trajectories and therefore more practical. We evaluate the performance of SPRT in real-world applications by applying its synthetic data to a series of trajectory analytic tasks. The results demonstrate that our solution improves data utility by at least 37% over state-of-the-art approaches.
Is social presence (alone) a general predictor for good remote collaboration? comparing video and augmented reality guidance in maintenance procedures

Abstract A common practice in scenarios of remote collaboration is to provide a representation from distributed team members, aiming to positively influence the level of social presence and in turn the work effort. Nevertheless, these stimuli can lead to fractured learning experiences, since collaborators need to split attention among the task, the shared information, and the counterpart representation. This paper explored how the last affects social presence, and other dimensions of collaboration, as well as task resolution in scenarios of remote guidance. A user study was conducted, comparing two distinct conditions: traditional video chat (team members representation always visible) and Augmented Reality (AR) annotations (collaborators representation never available). These were selected due to ongoing research with partners from the industry sector, following the insights of a participatory design process. A real-life use-case was considered, i.e., synchronous maintenance task with 4 completion stages that required a remote expert using a computer to guide 37 on-site participants wielding a handheld device. The results of the study are described and discussed based on data analysis, showing that the majority of participants preferred the AR-based condition, despite the absence of the expert representation.
Skeleton-Based Multifeatures and Multistream Network for Real-Time Action Recognition

Action recognition is a hot topic in the field of computer vision. It has been widely used in human–computer/robot interaction, abnormal behavior monitoring, and medical assistive. Because of the excellent robustness of skeleton data, it has attracted many scholars to research skeleton-based action recognition. Most of the current skeleton-based action recognition methods suffer from the incomplete and poor generalization of the input features, inadequate feature extraction by the network model, and an imbalance between recognition accuracy and model size. We analyze the critical skeleton features for action recognition to solve these problems and propose a multifeatures and multistream network (MM-Net) for real-time action recognition. First, three pairs of features are proposed, which are the joint distance (JD) and JD velocity (JDV), joint angle (JA) and JA velocity (JAV), and fast-action joint position (FJP) and slow-action joint position (SJP). Second, an MM-Net is proposed by using a 1-D convolutional neural network (1DCNN) to reduce the number of parameters of the model and fully extract the three pairs of features. As a result, MM-Net achieves the highest accuracies on both JHMDB (86.5%) and SHREC (96.4% on coarse and 93.3% on fine datasets). In addition, MM-Net is applied to a human–robot interaction (HRI) platform, which proves the practicality of MM-Net.
Asymmetric relationships among financial sector development, corruption, foreign direct investment, and economic growth in sub-Saharan Africa

Prior studies on the relationship between FDI and growth have generally concentrated on mean effects, or average growth benefits. It seems improbable that the majority of sub-Saharan economies will have similar “average” economic growth, hence the emphasis on mean effects in particular falls short. All other drivers can be seen to have an impact based on the uneven growth rates of these economies. The current study brings new evidence about the asymmetric relationship between foreign direct investment (FDI) and economic growth amidst financial sector development (FSD) and corruption covering a sample period of 2002 to 2020 for 48 sub-Saharan economies. For this reason, the instrumental variables panel quantile regression technique is employed to achieve the purpose of the study. The study finds that FDI inflows have a significant positive relationship with economic growth for economies with low growth (less than 50% quantile) but negative at high growth levels (at quantiles 50% and beyond). Also, control of corruption significantly interacts negatively with FDI and GDP per capita irrespective of the GDP levels, whereas FSD significantly positively interacts with FDI to contribute to economic growth at various growth levels. Findings from the study imply that FSD promotes economic growth in sub-Saharan Africa at diverse growth levels. On the other hand, the interacting effect of control of corruption is inimical to FDI-growth nexus at all growth levels. It is pertinent that efforts are made by financial policymakers in sub-Saharan Africa to improve the local financial sector conditions to recuperate the economic advances from FDI.
How Much Presence is Enough? Qualitative Scales for Interpreting the Igroup Presence Questionnaire Score

Presence is often used to evaluate Virtual Reality (VR) applications. However, the raw scores are hard to interpret and need to be compared to other data to be meaningful. This paper leverages a database of 1909 responses to the Igroup Presence Questionnaire (IPQ) in different contexts to put forward a scale that qualitatively interprets raw Presence scores for VR experiences. The qualitative grading encompasses the acceptability dimension and analogous academic grading scales ranging from A to F and the adjective of such scores in a scale from Excellent to Unacceptable. Furthermore, the qualitative grading system encompasses Presence and its subscales Spatial Presence, Involvement, and Experienced Realism as defined by the IPQ. Adopting this grading system, supported by a robust dataset of Presence scores, enables practitioners to evaluate and interpret individual IPQ scores, allowing them to gain insights regarding the evaluated applications’ effectiveness.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era

Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human–computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. However, the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions—aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesized utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology that underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In this overview, we outline ongoing trends and summarize state-of-the-art approaches in an attempt to provide a broad overview of this exciting field.
Increasing the security of transmitted text messages using chaotic key and image key cryptography

It is critical to safeguard confidential data, especially secret and private messages. This study introduces a novel data cryptography approach. The new approach will be capable of encrypting and decrypting any communication size. The suggested approach will use a sophisticated private key with a convoluted structure. The private key will have 5 components with a double data type to prevent guessing or hacking. The confidential data will produce two secret keys, the first of which will be taken from the image key. These keys will be vulnerable to slight changes in private key information. To maximize the approach's efficiency, the suggested method will deal with lengthy messages by splitting them into chunks. On the other hand, the chaotic logistic map model will be used to create the second key. The suggested technique will be implemented, and several sorts of analysis (sensitivity, quality, security, and speed analysis) will be undertaken to demonstrate the benefits of the proposed method. The quality metrics MSE, PSNR, and CC will be computed to validate the suggested method's quality. To illustrate the efficiency of the proposed technique, encryption and decryption times will be measured, and cryptography throughputs will be determined. Various PKs will be tried throughout the decryption process to demonstrate how sensitive the produced outputs are to changes in the private key. The suggested approach will be tested, and the results will be compared to the results of existing methods to demonstrate the improvement offered by the proposed method.
Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.
A hybrid algorithm for workflow scheduling in cloud environment

The advances in cloud computing promote the problem in processing speed. Computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. Efficient workflow scheduling is a challenge in reducing the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimisation problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.
Detection and explanation of anomalies in healthcare data

Abstract The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.
Simple master equations for describing driven systems subject to classical non-Markovian noise

Driven quantum systems subject to non-Markovian noise are typically difficult to model even if the noise is classical. We present a systematic method based on generalized cumulant expansions for deriving a time-local master equation for such systems. This master equation has an intuitive form that directly parallels a standard Lindblad equation, but contains several surprising features: the combination of driving and non-Markovianity results in effective time-dependent dephasing rates that can be negative, and the noise can generate Hamiltonian renormalizations even though it is classical. We analyze in detail the highly relevant case of a Rabi-driven qubit subject to various kinds of non-Markovian noise including <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>f</mml:mi></mml:math> fluctuations, finding an excellent agreement between our master equation and numerically-exact simulations over relevant timescales. The approach outlined here is more accurate than commonly employed phenomenological master equations which ignore the interplay between driving and noise.
Perceived usefulness of, engagement with, and effectiveness of virtual reality environments in learning industrial operations: the moderating role of openness to experience

Abstract The development of virtual reality (VR) in enhancing the effectiveness of the learning process, with its interactive, immersive, and intuitive pedagogical environment, has become a necessity for corporations with increasingly complex operations. However, VR users’ perceptions, openness and learning effectiveness are seldom comprehensively evaluated, particularly in learning complex industrial operations. In this study, grounded in the technology acceptance model, a moderated mediation model of perceived usefulness, ease of use, openness to experience, and engagement in VR-based learning was developed. The model was empirically validated using responses collected from 321 users who were trained on aircraft and cargo terminal operations powered by a novel VR-based learning platform. A survey to measure openness to experience and a pre-training performance test were carried out, followed by a post-training survey of learners’ intrinsic factors, including the influence of perceived usefulness, openness to experience, and attitude towards learning. The study revealed that learners with an open attitude towards experiencing new technology tend to perceive VR technology as a useful platform for training. In addition, the learners with more positive views of VR technology-supported training were more engaged in learning.
An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.