Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies: Business Students’ Awareness and Universities’ Adoption Readiness and Compatibility of Use Considering the Mediation of Attitudes

The study aims to determine the effect of awareness of business students towards cryptocurrencies and readiness of universities and colleges to accept cryptocurrencies as a medium of exchange considering the mediation of business students’ attitude. The research used partial least squares structural equation modeling (PLS-SEM) and deployed a questionnaire attuned to the awareness and attitudes of business students towards cryptocurrencies and readiness and compatibility of use to universities and colleges in Ilocos Norte. The output of the investigation revealed that awareness of business students are not correlated to readiness of universities and colleges which means that even though students understand cryptocurrencies, it is not an assurance that universities and colleges are ready to accept it as medium of exchange. The study proposes that trainings and seminars for business students and professionals should be conducted to expand understanding and acceptance of cryptocurrencies.&#x0D; &#x0D; &#x0D;
Blockchain Interoperability: a Feature-based Classification Framework and Challenges Ahead

Blockchain is one of the most recent disrupting technologies. Since Bitcoin emerged as its initial application, many blockchain projects arose offering different features for general and domain specific scenarios. In turn, Blockchain platforms work as isolated environments and they do not support interaction (i.e. interoperability) between each other.During the last years, some academic and industry efforts were performed in order to achieve interoperability between blockchain platforms. However, these efforts solve specific use cases and do not provide a general interoperability solution. In order to organize existing work in the area, this paper provides a feature-based classification framework based on 16 key interoperability features, that enabled the classification of 65 academic papers in three categories and six subcategories.The results show that nowadays research is focused on public interoperability, while private and public-to-private interoperability are still on their first steps. Based on these results, this work also discusses nowadays blockchain interoperability landscape, identifies challenges, and suggests future research directions.
An extended version of average Markov decision processes on discrete spaces under fuzzy environment

The article presents an extension of the theory of standard Markov decision processes on discrete spaces and with the average cost as the objective function which permits to take into account a fuzzy average cost of a trapezoidal type.In this context, the fuzzy optimal control problem is considered with respect to two cases: the max-order of the fuzzy numbers and the average ranking order of the trapezoidal fuzzy numbers.Each of these cases extends the standard optimal control problem, and for each of them the optimal solution is related to a suitable standard optimal control problem, and it is obtained that (i) the optimal policy coincides with the optimal policy of this suitable standard control problem, and (ii) the fuzzy optimal value function is of a trapezoidal shape.Two models: a queueing system and a machine replacement problem are provided in order to examplify the theory given.
RTGEN++: A Relative Temporal Graph GENerator

Graph management systems have become popular for storing and querying graph-oriented data, and they are often evaluated with benchmarks based on large-scale graphs. However, obtaining such graphs is difficult due to their limited public availability. Several graph generators have been developed to address this challenge, producing synthetic graphs with characteristics similar to real-world graphs, such as degree distribution, community structure, and diameter. Generating synthetic graphs with constantly changing topology has received less attention despite its importance in developing useful benchmarks for temporal graph systems. In this paper, we present RTGEN++, a temporal graph generator that supports two evolution models. The first model generates temporal graphs by controlling the evolution of their degree distributions, using optimal transport methods to minimize the transformation effort. We also extend our method in order to consider the community structure of the generated graphs. The second model allows one to control the number of added and removed graph entities, thus enabling the modeling of the evolution of real-world graphs in many use cases. Our generator also includes a decorator that adds types and time-varying attributes to nodes and edges, enhancing the generated graphs and aligning with data platforms that use the property graph model. We validate our approach with experiments that demonstrate the reliability of the generated graphs in approximating ground-truth parameters.
Embracing Business Sustainability Through Innovation and Productivity in the Automotive Sector

The purpose of the study reported on in this chapter was to explore and attempt to answer the primary research question: To what extent can embracing business sustainability through digital transformation, implemented via unified communication and collaboration (UC&amp;C) technologies, impact innovation and productivity in the global automotive sector? This will be presented against the background of embracing business sustainability through innovation and creativity in the service sector. Apart from creativity, collaboration, and generating savings featured in the subtitle of the chapter, changing work practices were also identified as a factor impacting innovation.
3D Object Detection for Self-Driving Cars Using Video and LiDAR: An Ablation Study

Methods based on 64-beam LiDAR can provide very precise 3D object detection. However, highly accurate LiDAR sensors are extremely costly: a 64-beam model can cost approximately USD 75,000. We previously proposed SLS-Fusion (sparse LiDAR and stereo fusion) to fuse low-cost four-beam LiDAR with stereo cameras that outperform most advanced stereo-LiDAR fusion methods. In this paper, and according to the number of LiDAR beams used, we analyzed how the stereo and LiDAR sensors contributed to the performance of the SLS-Fusion model for 3D object detection. Data coming from the stereo camera play a significant role in the fusion model. However, it is necessary to quantify this contribution and identify the variations in such a contribution with respect to the number of LiDAR beams used inside the model. Thus, to evaluate the roles of the parts of the SLS-Fusion network that represent LiDAR and stereo camera architectures, we propose dividing the model into two independent decoder networks. The results of this study show that-starting from four beams-increasing the number of LiDAR beams has no significant impact on the SLS-Fusion performance. The presented results can guide the design decisions by practitioners.
Changes in the market structure and risk management of Bitcoin and its forked coins

Inconsistency of consensus results in blockchain forks, which create a new financial risk. After filtering out Bitcoin’s linear, nonlinear, and lag impacts on forked coins, this study employs a bottom-up hierarchical clustering algorithm to examine the logarithmic return series for Bitcoin and its 14 forked coins from 2018 to 2021. The results indicate that the market for forked coins can be divided into three clusters: SegWit-supported forked coins, mature forked coins, and the latest forked coins. Bitcoin and the mature forked coins form a cluster, and its performance is superior to others. Although Bitcoin’s return significantly affects that of its forked coins, it does not affect the market structure. Furthermore, this study provides references for risk aversion among investors in forked coins and presents macro-level information for cryptocurrency market authorities.
Robust sparse Bayesian learning based on the Bernoulli-Gaussian model of impulsive noise

Traditional sparse reconstruction methods suffer from performance degradation in the presence of impulsive noise. In this paper, we adopt the Bernoulli-Gaussian distribution to model the impulsive noise and introduce the hidden state variables to indicate the state of the noise. Based on the new hierarchical Bayesian model, a robust sparse reconstruction algorithm called Bernoulli-Gaussian robust sparse Bayesian learning (BG-RSBL) is developed through the variational Bayesian inference framework. Different from existing robust reconstruction methods, BG-RSBL does not make sparse assumptions about the impulses and outliers. The employment of the Bernoulli-Gaussian model enables BG-RSBL to adapt to environments with a wider range of impulse densities. Comparative analysis shows that the reconstruction performance can be improved by including the outliers in posterior inference with appropriate weightings. Simulations performed under both Bernoulli-Gaussian and symmetric α-stable distributed impulsive noise demonstrate the robust and outstanding reconstruction performance of the proposed algorithm.
Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target's Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a "Multi-frame Moving Object Detection System (MMODS)", to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.
MBR membrane fouling diagnosis based on improved residual neural network

High nonlinearity and dispersion in response to the numerous influencing elements of membrane pollution, lead to challenges in diagnosing and other issues. To increase the accuracy of membrane fouling diagnosis, we suggest a method in this research that uses a residual neural network with an attention mechanism. First, the stacking properties of residual blocks are employed to extract the fault information step by step while avoiding the gradient dispersion problem once the fault data has been extracted by the convolutional neural network. Secondly, at each bottleneck in the residual block, the convolutional and coordinated attention mechanism combination is introduced to extract features from the multi-dimensional refinement and boost the diagnostic precision. Finally, the research object for the experimental examination of fault identification is listed as the membrane fouling data. The results of the experiments demonstrate that the proposed diagnostic method can extract useful features in a wide data range with an average accuracy of 99.42% in model accuracy comparison experiments and 96.67∼97.96% in variable noise experiments, which are higher than other methods, and has the ability to reduce power consumption and maintenance costs, providing a theoretical research basis for practical production.
Hospitality Industry 5.0

The hospitality sector 5.0 aspires to integrate technology into service marketing, human resource management, and hotel operations in order to give visitors a seamless and useful customer experience. Artificial intelligence, the internet of things, blockchain technology, collaborative robots, chatbots and sensors, contactless automation, mobile technology, augmented reality, virtual reality, big data, cloud computing, 6 G and beyond, as well as machine-to-machine communication, are all part of the hospitality industry 5.0. When automated technologies, intelligent devices, and intelligent systems converge with human intelligence, hospitality 5.0 will be born. It aims to provide additional contributions to the field of technological use in hospitality and tourism and thereby offers a strong knowledge base. Therefore, all people involved in the study's implementation might benefit from its findings in order to better understand and meet the requirements and expectations of both customers and employees through the use of technology.
Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

Abstract Air writing and gesture recognition has been one of the most comprehensive and intensively researched areas due to their wide variety of applications in several domains of Human-Computer Interaction (HCI),such as gaming, medicines, and automobiles. This article presents ahighly accurate point-based algorithm for identifying dynamic air writing and free hand gesture recognition for Devanagari Hindi characters.Usually, the written gestures are limited in nature. In order to create a training data set, we are using Leap Motion Controller (LMC) that generates twenty-four sample points from the gesture drawn. In the pre-processing stage, a trimesh is generated using these sample points; the developed trimesh is then fed as input to PointNet. The algorithm developed measures the similarity of the drawn gesture with the training samples to identify and recognize the gesture drawn using Mahalanobis distance. The validation test indicates that the approach is quite accurate, giving a recognition rate of more than 97%. Comparative studies also show that the methodology performs better when trimesh is used as input for the PointNet and more training samples are used.
Kesulitan Guru Bahasa Inggris dalam Menggunakan E- Learning Selama Pandemi Covid-19

The purpose of this study was to determine the difficulties of English teachers in using e-learning during the Covid-19 pandemic. This research uses qualitative research. The participants were 10 English teachers from different high schools in Tolitoli Regency, Central Sulawesi. The instruments used to collect data were questionnaires and interviews according to the topic. These findings reveal that English teachers experience three obstacles during online learning, including knowledge about the use of e-learning, quality of internet connection, and physical condition. &#x0D; Keywords: Teacher, e-learning, Covid-19 Pandemic
Efektivitas Penerapan Metode Field Trip terhadap Kemampuan Menulis Karangan Deskripsi Siswa

This study aims to determine the effectiveness of applying the field trip method on the ability to write descriptive essays of class VIII students of SMP Negeri H Wukirsari. The research design used an experimental approach with a quasi-experimental research type. The results showed that t0 was 5.21, tt (1%) was 2.75 and tt (5%) was 2.04. In conclusion, the application of the field trip method is practical on the ability to write descriptive essays of class VIII students of SMP Negeri H Wukirsari.&#x0D; Keywords: Description, Field Trip, Writing
CPAE: Contrastive predictive autoencoder for unsupervised pre-training in health status prediction

Fully-supervised learning approaches have shown promising results in some health status prediction tasks using Electronic Health Records (EHRs). These traditional approaches rely on sufficient labeled data to learn from. However, in practice, acquiring large-scaled labeled medical data for various prediction tasks is often not feasible. Thus, it is of great interest to utilize contrastive pre-training to leverage the unlabeled information.In this work, we propose a novel data-efficient framework, contrastive predictive autoencoder (CPAE), to first learn without labels from the EHR data in the pre-training process, and then fine-tune on the downstream tasks. Our framework comprises of two parts: (i) a contrastive learning process, inherited from contrastive predictive coding (CPC), which aims to extract global slow-varying features, and (ii) a reconstruction process, which forces the encoder to capture local features. We also introduce the attention mechanism in one variant of our framework to balance the above two processes.Experiments on real-world EHR dataset verify the effectiveness of our proposed framework on two downstream tasks (i.e., in-hospital mortality prediction and length-of-stay prediction), compared to their supervised counterparts, the CPC model, and other baseline models.By comprising of both contrastive learning components and reconstruction components, CPAE aims to extract both global slow-varying information and local transient information. The best results on two downstream tasks are all achieved by CPAE. The variant AtCPAE is particularly superior when fine-tuned on very small training data. Further work may incorporate techniques of multi-task learning to optimize the pre-training process of CPAEs. Moreover, this work is based on the benchmark MIMIC-III dataset which only includes 17 variables. Future work may extend to a larger number of variables.
Multi Task Learning: A Survey and Future Directions

Multi-task learning (MTL) is a problem that must be applied in modern recommendation systems and is just as difficult. In the recent e-commerce advertising market, it is necessary to be able to predict not only the probability of users clicking, but also the probability of conversion and purchase. By predicting multi-task, it is possible to increase the accuracy of each task and optimize advertisements for various goals of advertisers. Traditional conversion rate (CVR) prediction models have difficulty learning because the number of conversions is too small compared to the total number of impressions. This problem is called a data sparsity (DS) problem. Another problem is that CVR models trained with samples of clicked impressions infer on samples of all impressions. This problem is called a sample selection bias (SSB) problem. This paper is a summary of the various solutions and current limitations and further directions about solving sample selection bias problem and data sparsity problem.
Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

Abnormal event detection is one of the most challenging tasks in computer vision. Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. It is assumed that the reconstruction error gap between frames of normal and abnormal scores is high for abnormal events during the testing phase. Yet, this assumption may not always hold due to superior capacity and generalization of deep neural networks. In this paper, we design a generalized framework (rpNet) for proposing a series of deep models by fusing several options of a reconstruction network (rNet) and a prediction network (pNet) to detect anomaly in videos efficiently. In the rNet, either a convolutional autoencoder (ConvAE) or a skip connected ConvAE (AEc) can be used, whereas in the pNet, either a traditional U-Net, a non-local block U-Net, or an attention block U-Net (aUnet) can be applied. The fusion of both rNet and pNet increases the error gap. Our deep models have distinct degree of feature extraction capabilities. One of our models (AEcaUnet) consists of an AEc with our proposed aUnet has capability to confirm better error gap and to extract high quality of features needed for video anomaly detection. Experimental results on UCSD-Ped1, UCSD-Ped2, CUHK-Avenue, ShanghaiTech-Campus, and UMN datasets with rigorous statistical analysis show the effectiveness of our models.
An Introduction to the Special Issue on Digital and Collaborative Higher Education: The Case of the Erasmus+ OpenU Project

This Special Issue casts a scholarly lens on collaboration among higher education institutions (HEIs), promoted by digital transformation and “digitalization” [...]
An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps

Multi-label stream classification is the task of classifying instances in two or more classes simultaneously, with instances flowing continuously in high speed. This task imposes difficult challenges, such as the detection of concept drifts, where the distributions of the instances in the stream change with time, and infinitely delayed labels, when the ground truth labels of the instances are never available to help updating the classifiers. To solve such task, the methods from the literature use the problem transformation approach, which divides the multi-label problem into different sub-problems, associating one classification model for each class. In this paper, we propose a method based on self-organizing maps that, different from the literature, uses only one model to deal with all classes simultaneously. By using the algorithm adaptation approach, our proposal better considers label dependencies, improving the results over its counterparts. Experiments using different synthetic and real-world datasets showed that our proposal obtained the overall best performance when compared to different methods from the literature.
The impact of social media marketing on purchase intention: The mediating role of brand trust and image

This research examines how social media marketing affects brand image, brand trust, and purchase intention in the context of beauty centers in Jordan. The study presents a model that outlines the influence of social media marketing strategies on customer behavior. To support the proposed model, a quantitative method was employed to collect data through online surveys. The sample consisted of 374 respondents selected through non-probability convenience sampling techniques. The data was analyzed using PLS-SEM methodologies by Smart PLS, which is appropriate for exploratory research. The findings indicate that social media marketing has a noteworthy effect on brand image and brand trust. In addition, the study found that brand trust and brand image have a substantial influence on purchase intention. The relationship between brand image and brand trust accounts for 70% of the variance in purchase intention. Furthermore, social media marketing explains 45% of the variance in brand image, while it explains 55% of the variance in brand trust.