Reviewerly/exHarmony huggingface dataset:
Real-time conversion of speech to sign language and hand gesture recognition

The hearing-impaired community exclusively depends on sign language as a medium of communication. However, learning and understanding sign language may be difficult for some and none of this software is available for free or at a low cost. This paper presents a method of communication for hearing- impaired people by providing a speech (English Language) to sign language conversion and hand gesture recognition system. The system works in real-time by analysing the speech that wants to be conveyed and converts it into text using Azure cognitive API, which upon matching is then used to fetch the American sign language (ASL) GIF’s from the hand-speak API. It lists a collection of GIF’s which are later concatenated to form a video. It also recognises the hand signs and converts them into speech by making use of Dense-Net, Back Propagation technique and artificial neural networks. Through this system, the aim is to fill the communication gap between the deaf and non-deaf communities.
Improved Raft Algorithm for Optimizing Authorized Nodes Based on Random Forest

The traditional Raft algorithm has such an issue as "vote snatching" among candidates that results in abnormal elections in the absence of a majority. To address this issue, a random forest-based method is introduced for the identification of authorized nodes. First, the attributes of authorization are introduced to the original Raft algorithm to be the label of authorized nodes, i.e., only authorized nodes are eligible for candidacy. Second, the random forest algorithm is introduced to classify the authorized nodes according to their term, broadcasting duration, timeout duration, and other characteristics to divide all nodes into preferred nodes and ordinary nodes. In the end, the main consensus group makes up of preferred nodes adopted integral accumulation to top out the preferred node with the highest integral as the authorized nodes. According to the results, the optimized Raft algorithm avoids abnormal elections and outperforms the original algorithm in election efficiency. The method offers an orderly mechanism of node operation for the Consortium Blockchain.
Research Gaps and Future Trends in Educational Media and Educational Theater Research: Analytical Study in Scopus and Web of Science Databases

With the growing digitalization, there has been increasing demand in companies for a skilled workforce to sustain in a competitive business environment.Although the traditional education system serves as a good source of knowledge, they have been consistently proven to lack emphasis on the overall development of students.Therefore, in recent years, the integration of art in education has been promoted.Education media and education theater are interactive education methodologies that improve the learning experience of students and develop their cognitive skills.This study assesses the research gaps in the field of educational media and educational theater through a comparison of critical studies published in Scopus and the Web of Science database.The areas of educational media prevalence, usage of theater in education, educational media role, and benefits of educational theater have been explored.The analysis identifies the main problems and is based on the current education sector needs.Accordingly, suggestions for improvement for the Egyptian education ministry are provided.
The Transformation of RGB Images to Munsell Soil-Color Charts

[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.
Smart Meter Data Anomaly Detection Using Variational Recurrent Autoencoders with Attention

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with “dirty” data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.
Exploring Capturing Approaches in Shared Fabrication Workshops: Current Practice and Opportunities

Capturing content of fabrication activities is the first step for producing knowledge resources and an integral part of maker culture. As a secondary task, it conflicts though with the fabrication activity, and thus it is often forgotten and knowledge resources end up incomplete. In this article, we investigate different dimensions of content capture for knowledge resources in fabrication workshops. Based on past work in this area, we first propose a framework through which we identify two research directions to investigate. From these, we derive three dimensions to explore in more depth: The number of capturing devices, their feature variety and the degree of automation of each feature. We then explore the design space resulting from these three dimensions with the help of a design concept and an online survey study (N=66). Results show (1) a variety of needs and preferences justifying feature variety and multiplicity, (2) challenges in defining the right degree of manual and automatic control, and (3) the socio-technical impact of cameras in a shared space regarding privacy and ethics. We conclude with discussions on the benefits and vulnerabilities of equipping fabrication workshop with distributed camera-based capturing devices and offer opportunities for design.
Pyramidal dense attention networks for single image super‐resolution

Recently, residual and dense networks have effectively promoted the development of image super-resolution (SR). However, most dense networks based SR methods do not make full use of dense feature information. To solve this problem, a pyramidal dense attention network for single image super-resolution is proposed in this paper. In this method, the proposed pyramidal dense learning can gradually increase the width of the densely connected layer inside a pyramidal dense block to extract deep features efficiently. Meanwhile, the adaptive group convolution that the number of groups grows linearly with dense convolutional layers is introduced to relieve the parameter explosion. Besides, a novel joint attention to capture cross-dimension interaction between the spatial dimensions and channel dimension in an efficient way for providing rich discriminative feature representations is also proposed. Extensive experimental results show that the method achieves comparable performance in comparison with the state-of-the-art SR methods.
A Systematic Literature Review of Routine Activity Theory’s Applicability in Cybercrimes

Cybercrimes are increasing at an alarming rate and cause detrimental effects to the victims. Routine Activity Theory (RAT) is commonly used to understand the factors influencing cybercrime victimization. However, there have been inconsistent findings on the applicability of RAT theory. This study performs a Systematic Literature Review analysis to consolidate and provide a coherent analysis of the related studies employing RAT theory for cybercrime victimization. The articles were also differentiated based on the cybercrimes topologies being investigated; (a) cybercrime dependent (hacking and malware) and (b) cybercrime enabled (phishing, fraud and identity theft). The findings suggest that a refined specification and operationalization of RAT’S construct tailoring to the types of cybercrimes can arguably yield more accurate application and interpretation of RAT Theory in cybercrimes. Consequently, this will address the inaccurate measurement issues of some of the RATS’s constructs, leading to inconclusive effects on cybercrime victimization. In addition, there is a need for more longitudinal studies to disentangle the effect of RAT’s construct during pre and post cybercrimes. Security advocates can apply the findings of this research to formulate relevant cybercrime awareness programs. The findings also shed some insights into which groups should be targeted for different cybercrime educational and awareness programs. This study can increase the awareness among citizens in terms of their online activities, their attributes and the types of protection from becoming cybercrime victims.
METHODOLOGICAL INSTRUMENTS FOR THE ORGANIZATION OF PROFESSIONAL TRAINING IN HIGHER EDUCATION INSTITUTIONS

The article deals with the selection of methodological tools for organizing professional training of specialists. Considered are the modern requirements for the organization of professional training of specialists, as well as the possibilities of existing methodological approaches for their full implementation&#x0D; The possibilities of the competence-based approach in the organization of vocational training in connection with the modern trend to define it as the main one for the development of educational standards are revealed.&#x0D; Opportunities for correcting certain shortcomings in the organization of professional training of specialists, the solution of which are not provided by the tools of the competence-based approach, have been determined. The article describes the need to use the position of the systematic approach in order to give the system of professional training the properties of a flexible adaptive system, which has the property of being open for external and internal correction.&#x0D; The necessity of using the provisions of the technological approach in the process of determining effective combinations of methods and forms of organizing the process of vocational training is revealed. The mechanism of combining the named methodological approaches for organizing professional training based on the use of content-procedural integration of methodological approaches has been determined.
Enhancing Performance of Solar Photovoltaic Drip Irrigation System Through Binary Particle Swarm Optimization

This paper presents a multisector drip irrigation system (DIS) powered by solar photovoltaic (PV).A binary particle swarm optimization (BPSO) method possibly determines irrigation in multisector, depending on the power availability of the solar PV.The power from solar PV in this study was optimized through maximum power point tracking (MPPT) using an incremental conductance (INC) method.A prototype was built using a PV emulator in 10 controlled irrigation sectors.Three operating modes applied include control of all sectors with BPSO, partly sectors with a combination of linear programming, and all sectors with bypassed without BPSO.Simulations and tests showed very similar results, indicating that the BPSO method provides an optimal and accurate irrigation system with 0.7 psi of an average irrigation operating pressure (IOP) error, 4.01 % of water volume error, and 96.55 % of the uniformity coefficient of water volume.
Focus Group Findings to Support the Preliminary Development of the Augmented Reality Education Experience (AREduX)

Dementia is considered a global health priority with projections of the disease set to increase dramatically across the world. Current support for persons living with dementia (PLWD) relies on long-term care and local service centers to provide education and support. Augmented reality-based programs continue to gain momentum across health sectors, becoming an innovative approach that provides an opportunity to have a visceral experience, which can deepen understanding and provide an embodied perspective of other groups within a relatively short time frame. There is increasing interest in developing approaches to aid patient care outcomes for PLWD and their caregivers. Hence, healthcare providers (HCPs) who are appropriately trained and equipped to provide quality care to PLWD are essential and of international concern. The purpose of this research program is to develop an augmented reality (AR) education experience (AREduX), a proof of concept prototype in the form of a digital resource that uses AR to simulate the physical and cognitive symptoms that PLWD experience. The findings from a stakeholder focus group will allow for the preliminary development of the AREduX.
A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Abstract The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.
Autoencoder and Partially Impossible Reconstruction Losses

The generally unsupervised nature of autoencoder models implies that the main training metric is formulated as the error between input images and their corresponding reconstructions. Different reconstruction loss variations and latent space regularizations have been shown to improve model performances depending on the tasks to solve and to induce new desirable properties such as disentanglement. Nevertheless, measuring the success in, or enforcing properties by, the input pixel space is a challenging endeavour. In this work, we want to make use of the available data more efficiently and provide design choices to be considered in the recording or generation of future datasets to implicitly induce desirable properties during training. To this end, we propose a new sampling technique which matches semantically important parts of the image while randomizing the other parts, leading to salient feature extraction and a neglection of unimportant details. The proposed method can be combined with any existing reconstruction loss and the performance gain is superior to the triplet loss. We analyse the resulting properties on various datasets and show improvements on several computer vision tasks: illumination and unwanted features can be normalized or smoothed out and shadows are removed such that classification or other tasks work more reliably; a better invariances with respect to unwanted features is induced; the generalization capacities from synthetic to real images is improved, such that more of the semantics are preserved; uncertainty estimation is superior to Monte Carlo Dropout and an ensemble of models, particularly for datasets of higher visual complexity. Finally, classification accuracy by means of simple linear classifiers in the latent space is improved compared to the triplet loss. For each task, the improvements are highlighted on several datasets commonly used by the research community, as well as in automotive applications.
Improved Reliability of Manufacturing Process Data Using FOMs(smart-Factory Operation Management) Solution

Many small- and medium-sized enterprises in Korea are introducing smart factories to maintain competitiveness. The smart factory collects and monitors production process and equipment data in real time. When an abnormality occurs, the smart factory can immediately identify the problem, minimizing equipment downtime and product defect and increasing corporate profits. In this study, 4M (human, machine, material, method) data, a key element of manufacturing, was collected and analyzed to build a smart factory, and the reliability of the collected data was very high. The reliability of the data was verified using the factory operation management solution and improved through on-site customized training. The data for the manufacturing site were collected, applied, and verified from small and medium-sized enterprises that manufacture automobile parts.
Emoji-based Fine-Grained Attention Network for Sentiment Analysis in the Microblog Comments

Microblogs have become a social platform for people to express their emotions in real-time, and it is a trend to analyze user emotional tendencies from the information on Microblogs. The dynamic features of emojis can affect the sentiment polarity of microblog texts. Since existing models seldom consider the diversity of emoji sentiment polarity, the paper propose a microblog sentiment classification model based on ALBERT-FAET. We obtain text embedding via ALBERT pretraining model and learn the inter-emoji embedding with an attention-based LSTM network. In addition, a fine-grained attention mechanism is proposed to capture the word-level interactions between plain text and emoji. Finally, we concatenate these features and feed them into a CNN classifier to predict the sentiment labels of the microblogs. To verify the effectiveness of the model and the fine-grained attention network, we conduct comparison experiments and ablation experiments. The comparison experiments show that the model outperforms previous methods in three evaluation indicators (accuracy, precision, and recall) and the model can significantly improve sentiment classification. The ablation experiments show that compared with ALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicating that the fine-grained attention network can understand the diversified information of emoticons.
Manta ray optimized deep contextualized bi-directional long short-term memory based adaptive galactic swarm optimization for complex question answering

Complex question answering (CQA) is used for human knowledge answering and community questions answering. CQA system is essential to overcome the complexities present in the question answering system. The existing techniques ignores the queries structure and resulting a significant number of noisy queries. The complex queries, distributed knowledge, composite approaches, templates, and ambiguity are the common challenges faced by the CQA. To solve these issues, this paper presents a new manta ray foraging optimized deep contextualized bidirectional long-short term memory based adaptive galactic swarm optimization (MDCBiLSTMAGSO) for CQA. At first, the given input question is preprocessed and the similarity assessment is performed to eliminate the misclassification. Afterwards, the attained keywords are mapped into applicant results to improve the answer selection. Next, a new similarity approach named InfoSelectivity is introduced for semantic similarity evaluation based on the closeness among elements. Then, the relevant answers are classified through the MDCBiLSTM and optimized by a new manta ray foraging optimization (MRFO). Finally, adaptive galactic swarm optimization (AGSO) resultant is the best output. The proposed scheme is implemented on the JAVA platform and the outputs of designed approach achieved the better results when compared with the existing approaches in average accuracy (98.2%).
Cost-Asymmetric Memory Hard Password Hashing

In the past decade billions of user passwords have been exposed to the dangerous threat of offline password cracking attacks. An offline attacker who has stolen the cryptographic hash of a user’s password can check as many password guesses as s/he likes limited only by the resources that s/he is willing to invest to crack the password. Pepper and key-stretching are two techniques that have been proposed to deter an offline attacker by increasing guessing costs. Pepper ensures that the cost of rejecting an incorrect password guess is higher than the (expected) cost of verifying a correct password guess. This is useful because most of the offline attacker’s guesses will be incorrect. Unfortunately, as we observe the traditional peppering defense seems to be incompatible with modern memory hard key-stretching algorithms such as Argon2 or Scrypt. We introduce an alternative to pepper which we call Cost-Asymmetric Memory Hard Password Authentication which benefits from the same cost-asymmetry as the classical peppering defense i.e., the cost of rejecting an incorrect password guess is larger than the expected cost to authenticate a correct password guess. When configured properly we prove that our mechanism can only reduce the percentage of user passwords that are cracked by a rational offline attacker whose goal is to maximize (expected) profit i.e., the total value of cracked passwords minus the total guessing costs. We evaluate the effectiveness of our mechanism on empirical password datasets against a rational offline attacker. Our empirical analysis shows that our mechanism can significantly reduce the percentage of user passwords that are cracked by a rational attacker by up to $$10\%$$ .