Reviewerly/exHarmony huggingface dataset:
An outlier-based analysis for behaviour and anomaly identification on IoT sensors

The pervasive nature of WSN-based IoT devices provides benefits for the industry, healthcare, and other environments. Because of that, a secure network that identifies sensor measurement errors is essential. However, the sensors are sensitive to changes according to the environment in which they are installed. Therefore, this work proposes an outlier-based analysis for behaviour and anomaly identification on IoT sensors which is twofold: first, cluster the sensors based on the variance of the devices' sensors; next, identify anomalies applying Mahalanobis distance within the clusters to identify anomalous devices. We use different datasets in the experimental process to validate our proposal. As a result, we demonstrate anomalous sensors identification without relying only on the neighbour measurements (which could lead to incorrect identification of anomalies), and by segregating the sensors' behaviour based on daytime.
Code smell severity detection using machine learning

Code smells are indications of design issues in software. The importance of early detection of these indicators helps in improving the maintenance process and raising the efficiency of software work. Besides detecting the severity of code smells the big problems of refactoring can be avoided as well. Machine learning algorithms are an effective way to detect code smells. This paper explicitly examines three different algorithms for SMO, ANN, and J48, among the four, most frequently detected code smells such as data class, god class, feature envy, and along method. The results showed that the SMO algorithm got better accuracy for data class, god class, and feature envy. Apart from this, ANNE with SMO algorithms got identical higher accuracy results in the long method.
Improvement of power quality in grid-connected hybrid system with power monitoring and control based on internet of things approach

Purpose. This article proposes a new control monitoring grid connected hybrid system. The proposed system, improvement of power quality is achieved with internet of things power monitoring approach in solar photovoltaic grid system network. The novelty of the proposed work consists in presenting solar power monitoring and power control based internet of things algorithm, to generate DC voltage and maintain the constant voltage for grid connected hybrid system. Methods. The proposed algorithm which provides sophisticated and cost-effective solution for measuring the fault and as maximum power point tracking assures controlled output and supports the extraction of complete power from the photovoltaic panel. The objective of the work is to monitor and control the grid statistics for reliable and efficient delivery of power to a hybrid power generation system. Internet of things is regarded as a network comprising of electronic embedded devices, physical objects, network connections, and sensors enabling the sensing, analysis, and exchange of data. The proposed control technique strategy is validated using MATLAB/Simulink software and real time implementation to analysis the working performances. Results. The results obtained show that the power quality issue, the proposed system to overcome through monitoring of fault solar panel and improving of power quality. The obtained output from the hybrid system is fed to the grid through a 3ϕ voltage source inverter is more reliable and maintained power quality. The power obtained from the entire hybrid setup is measured by the sensor present in the internet of things-based module. In addition to that, the photovoltaic voltage is improved by a boost converter and optimum reliability is obtained with the adoption of the perturb &amp; observe approach. The challenges in the integration of internet of things – smart grid must be overcome for the network to function efficiently. Originality. Compensation of power quality issues, grid stability and harmonic reduction in distribution network by using photovoltaic based internet of things approach is utilized along with sensor controller. Practical value. The work concerns a network comprising of electronic embedded devices, physical objects, network connections, and sensors enabling the sensing, analysis, and exchange of data. In this paper, internet of things sensors are installed in various stages of the smart grid in a hybrid photovoltaic –wind system. It tracks and manages network statistics for safe and efficient power delivery. The study is validated by the simulation results based on MATLAB/Simulink software and real time implementation.
The problem with printing pitch: challenges in designing 3D printed claves

Purpose Although music education has been proven to benefit students, music programs are often cut when public school funding is reduced. The cost of instruments is a significant financial burden on music programs, which restricts student access to high-quality instruments that would benefit their learning. Therefore, this paper aims to develop additively manufactured, polylactic acid (PLA) claves that could reproduce the sound of wooden claves and be printed by students in schools at a reduced cost to promote equal access to music education regardless of a school’s financial status. Design/methodology/approach This study developed a dual approach for analyzing clave vibration using mathematical analysis in MATLAB and SolidWorks finite element frequency simulation to predict the natural frequencies of 15 claves with varying geometries. To evaluate the performance of both models, the authors obtained natural frequencies experimentally by recording the claves and analyzing the resulting frequency spectra. The authors considered the possible effects of damping and determined the effective damping required in both models through comparison with experimental results. Findings Results indicate that PLA claves cannot easily be made to replicate the high pitch of rosewood claves while maintaining typical clave dimensions. However, PLA claves could still be suitable in instances where matching pitch is not a primary concern or improved durability is desired. The SolidWorks simulation approach could accurately predict clave pitch for all varieties of clave, whereas the mathematical approach was only accurate in predicting pitch for the filled claves. Originality/value In this work, the authors attempted to create an analytical process for simple percussion instrument design, which is typically done with trial-and-error methods. Instead, the authors developed a two-pronged approach in which experimental results were compared to results obtained both mathematically and from a finite element simulation. Additionally, we limited the materials and equipment used to those that would be available in a school setting so that the clave prototypes could be reproduced by public school students, the population this research is intended to serve.
Discussion on the Construction of Enterprise Internal Audit Informatization

With the popularization and application of enterprise, internal audit has gradually emerged. Therefore, enterprises need to strengthen information technology in enterprise audit, the development of information technology in information construction, and the integration of audit information, so as to accelerate the growth of enterprises. This article mainly uses the internal audit information of insurance company A as an example to analyze the existing problems in the current audit information structure and propose relevant solutions.
Study on the Impact of Chinese Comedy International Communication on the Health of Older People under Cultural Ecological Environment

There are many kinds of works in Chinese comedy culture, which are deeply loved by audiences at home and abroad. In order to explore the impact of the international spread of Chinese comedy culture in the ecological environment on the health of the elderly public, this study studied these elderly comedy audiences from the aspects of age, monthly income, physical health, mental health, and public stability through statistical methods. The results show that the international spread of Chinese comedy culture is conducive to the physical and mental health of the elderly public, relieves the pressure of life, and ultimately contributes to social harmony and stability.
Violence Detection Using Computer Vision Approaches

Violent crime has always been a major social problem. The rise of violent behavior in public areas can be attributed to a variety of factors. Greed, frustration, and hostility among individuals, as well as social and economic anxieties, are the primary causes of increased violence. It is critical to protect our possessions, as well as our lives, from threats such as robbery or homicide. It is impossible to prevent crime and violent acts unless brain signals are studied and a certain pattern deduced from criminal ideas is detected in real-time. Due to its technological viability, it has yet to be realized. However, We can identify violent activity in public spaces by using the concepts of computer vision (a subfield of deep learning) technology. The goal of this project is to build a real-time violent activity monitoring system that will be capable of detecting violence very quickly and efficiently. The public of any city can benefit from it, as it will allow the people of the law enforcement department to take necessary actions to prevent violent activities. When the system is implemented, it will be able to detect the speed of the movements of people and their distances from other people walking in public places by using cameras. The system will mainly detect the speed of hand and leg movements of a person who will be very close to another person. If anyone is identified as a violent maker, the server-side of the system will notify the people who will be responsible for preventing violence in a very short time. The system was built using the concepts of computer vision and neural networks. The system has been developed and tested initially on the personal computing devices of the system developers. This system is very easy to design and develop, making it very easy to use for any kind of public area surveillance. At the same time, the system gives its desired output due to its high accuracy.
A Novel Discrete Grey Wolf Optimizer for Scientific Workflow Scheduling in Heterogeneous Cloud Computing Platforms

There are several scientific workflow applications which need vast amount of processing so the cloud offerings give the sense of economic. Workflow scheduling has drastic impact on gaining desired quality of service (QoS). The main objective of workflow scheduling is to minimize the makespan. The workflow scheduling is formulated to a discrete optimization problem which is NP-Hard. This paper presents a novel discrete grey wolf optimizer (D-GWO) for scientific workflow scheduling problems in heterogeneous cloud computing platforms in the aim of minimizing makespan. Although the original GWO had great achievements in continuous optimization problems, it seems clear gap in utilizing GWO for combinatorial discrete optimization problems. It revolves around the fact that the continuous changes in search space during the course of discrete optimization lead inefficient or meaningless solutions. To this end, the proposed algorithm is customized in such a way to optimize discrete workflow scheduling problem by leveraging some new binary operators and Walking Around approaches to balance between exploration and exploitation in discrete search space. Scientific unstructured workflows were investigated in different circumstances to prove effectiveness of proposed novel meta-heuristic algorithm. The simulation results witnessed the superiority of proposed D-GWO against other state-of-the-arts in terms of scheduling metrics.
A Text Classification Model Based on GCN and BiGRU Fusion

A text classification model with the fusion of graph convolutional neural network (GCN) and bi-directional gated recurrent unit (BiGRU) is designed to address the lack of ability of simple neural networks to capture the contextual semantics of text, extract spatial feature information of text and nonlinear complex semantic relations. First, the text is preprocessed and text vectorization is performed by Word2Vec; then, the graph convolutional neural network and bi-directional gated recurrent unit are fused to form a hybrid model so that it can extract complex semantic relations and spatial feature information of the text; finally, the classification is performed by a softmax classifier. Experiments are conducted on a publicly available dataset, and the results demonstrate that the model can effectively improve the performance of text classification.
Performance evaluation of Shannon and non-Shannon fuzzy 2-partition entropies for image segmentation using teaching-learning-based optimisation

Thresholding has been used extensively by different researchers for the segmentation of images due to its accuracy and precision. Fuzzy 2-partition entropy with various evolutionary algorithms including teaching-learning-based optimisation (TLBO) has been used widely to determine optimal threshold value for image segmentation. Fuzzy 2-partition Shannon entropy is generally applied for thresholding. In this paper, Havrda-Charvat fuzzy 2-partition entropy and Renyi fuzzy 2-partion entropy-based TLBO techniques have been proposed for selecting optimal threshold value. The performance of fuzzy 2-partition Shannon and non-Shannon measures of entropy using TLBO has been compared with other nature-based evolutionary algorithms namely genetic algorithm, biogeography-based optimisation and with a recursive approach, which is a non-evolutionary approach. From the results, it has been observed that TLBO-based Havrda-Charvat fuzzy 2-partition entropy gives better performance than all other approaches in terms of quality of the segmented image as well as taking less computational time.
Real-Time Masked Face Recognition Using FaceNet and Supervised Machine Learning

The coronavirus pandemic has led to the implementation of health protocols such as the use of masks worldwide. Without exception, work activities also require the wearing of masks. This condition makes it difficult to recognize an individual's identity because the mask covers half of the face, especially when the employee is present. The attendance system recognizes a face without a mask more accurately, in contrast, a masked face makes identity recognition inaccurate. Therefore, this study proposes a combination of facial feature extraction using FaceNet and several classification methods. The three supervised machine learning methods were evaluated, namely multiclass Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest. Furthermore, the masked face recognition system was evaluated using real-time video data to assess the accuracy and processing time of the video frame. The accuracy result on real-time video data using a combination of FaceNet with K-NN, multiclass SVM, or Random Forest of 96.03%, 96.15%, and 54.04% are obtained respectively and in processing time per frame of 0.056 s, 0.055 s, and 0.061 are obtained respectively. The results show that the combination of the FaceNet feature extraction method with multiclass SVM produces the best accuracy and data processing speed. In other words, this combination can reach 18 fps at real-time video processing. Based on these results, the proposed combined method is suitable for real-time masked face recognition. This study provides an overview of the masked face recognition method so that it can be a reference for the contactless attendance system in this pandemic era.
Improving Emotional Intelligence in the New Normal Using Metaverse Applications for Digital Native

This research provides a study on analyzing emotional intelligence in the New Normal using Metaverse application for digital native generation. Coronavirus 2019 (COVID-19) outbreak has created new behavior and characterized human life to be more aware of the new digital applications. Significantly, research results show that the digital native generation, whose internet environment was typical from their birth, is optimistic in New Normal life amid COVID 19. In 2021, Metaverse set off a new generation of online interactive user behavior concepts. According to some research, the developing concept of Metaverse fits precisely in line with the future living of the digital native generation. However, some psychological side consequences should be revealed concurrently with the positive impact conceived. People's emotional intelligence argues for reduction due to the dissatisfaction of rapid adaptation to the New Normal condition. Thus, this research demonstrates an analysis to mitigate the psychological side effects of emotional intelligence when people are bound to the New Normal life. The improvement of the Web-based Extended Reality (WebXR), a critical development field of Metaverse, is proposed together to assist the immersive experience demands in addressing the problem-solution methods.
Adaptive Grey wolf Optimization Algorithm with Gaussian Mutation

Grey wolf optimizer is a well-known optimization algorithm and is still being investigated by the researcher to improve its performance for applying in complex optimization problems. This paper introduced an adaptive grey wolf optimization algorithm (AGWO) by providing adequate exploration to the grey wolves during hunting for the prey. The exploration procedure of the original grey wolf algorithm (GWO) dominates up to half of the iterations after the remaining part of iterations dedicated to the exploitation process. This restriction in exploration leads to a lack of population diversity in the GWO. To overcome this problem, the proposed method adaptively switched to the Gaussian mutation stage with a certain probability. To assess performance, the performance of the proposed algorithm is tested with a set of 23 benchmark functions defined in the CEC2005 data suit and compared with other standard optimization algorithms along with GWO. The results reveal that the proposed algorithm exceeds the well-known optimization algorithms, the GWO.
Deep Learning in Healthcare: Applications, Challenges, and Opportunities

Obtaining information and real-time insights from complicated, high-dimensional, and diverse biological data is a major problem in healthcare transformation. In current biomedical research, numerous types of data have emerged, such as imaging, electronic health records, text, and sensor data, all of which are complicated, diverse, inadequately annotated, and usually unstructured. Statistical learning and traditional data mining techniques often need feature engineering to generate useful and resilient features from data, followed by the construction of clustering or prediction models on top of them. In the case of complex data and a lack of subject knowledge, both processes provide numerous obstacles. The most recent breakthroughs in deep learning technology offer an operative paradigms for obtaining learning models from large and complex data. Deep learning algorithms could be used to translate large amounts of biomedical data into enhanced human health. The challenges and opportunities for enhanced technique and applications, particularly in terms of easiness for domain experts and citizen researchers, are highlighted in this chapter. This chapter also discusses how to create comprehensive and relevant easily understandable frameworks to integrate human understandability and deep learning models.
Towards a Deep Learning Approach for Automatic GUI Layout Generation

Various studies have been carried out over the past few years to solve the problem of automatically converting image models into source code. In 2018, pix2code has inspired and promoted the work in this domain. This paper presents a new model architecture to improve the framework of pix2code. We design a framework that can automatically generate a specific platform code for a given graphic user interface screenshot as an input. Although bilingual evaluation understudy (BLEU) is natural language processing metric, it has been adopted for source code evaluation. To overcome the limitation of BLEU in domain-specific language (DSL) tokens evaluation, we introduce a modified BLEU score (MBLEU). Our results show our proposed frameworks outperform the state-of-the-art methods in BLEU and MBLEU. We show MBLEU is suitable for DSL similarity evaluation, but further work is necessary to establish this new metric.
Practical Aspects of Zero-Shot Learning

Zero-shot learning is applied, for instance, when properly labeled training data is not available. A number of zero-shot algorithms have been proposed. However, since none of them seems to be an “overall winner”, development of a meta-classifier(s) combining “best aspects” of individual classifiers can be attempted. In this context, state-of-the-art zero-shot learning methods are compared for standard benchmark datasets. Next, multiple meta-classifiers are applied to the same datasets.
Real-Time Field Segmentation and Depth Map Using Stereo, Color and Ball Pattern

We present a real-time field segmentation in recognizing the object ball, field lines, and goalpost that have the same color. The same color on these objects make it difficult to be segmented. Then the system we build is using stereo cameras to vision sensors and a neural network for the object classification process. With the stereo camera and neural network, the object ball, field lines, and goalposts can be recognized easily. This is because the results of the value of the stereo camera and the neural network are unique. This is evident from the 10 times trials in recognizing all the white objects, gaining an error of 10%. The entire system runs an average time-consuming process for 0.08523 s.
The Automated Temporal Analysis of Gaze Following in a Visual Tracking Task

The attention assessment of an individual in following the motion of a target object provides valuable insights into understanding one’s behavioural patterns in cognitive disorders including Autism Spectrum Disorder (ASD). Existing frameworks often require dedicated devices for gaze capture, focus on stationary target objects, or fails to conduct a temporal analysis of the participant’s response. Thus, in order to address the persisting research gap in the analysis of video capture of a visual tracking task, this paper proposes a novel framework to analyse the temporal relationship between the 3D head pose angles and object displacement, and demonstrates its validity via application on the EYEDIAP video dataset. The conducted multivariate time-series analysis is two-fold; the statistical correlation computes the similarity between the time series as an overall measure of attention; and the Dynamic Time Warping (DTW) algorithm aligns the two sequences, and computes relevant temporal metrics. The temporal features of latency and maximum time of focus retention enabled an intragroup comparison between the performance of the participants. Further analysis disclosed valuable insights into the behavioural response of participants, including the superior response to horizontal motion of the target and the improvement in retention of focus on the vertical motion over time, implying that following a vertical target initially proved a challenging task.