Reviewerly/exHarmony huggingface dataset:
Using Heuristic Evaluation in Immersive Virtual Reality Evaluation

Previous works show that virtual reality itself can be used as a medium in which to stage an experimental evaluation. However, it is still unclear whether conventional usability evaluation methods can directly be applied to virtual reality evaluations and whether they will lead to similar insights when compared to equivalent real-world lab studies. Therefore, we conducted a user study with nine participants, comparing Heuristic Evaluation (HE) for the evaluations of a novel smart artefact. We asked participants to evaluate the physical prototype and their virtual counterparts in the real-world and the virtual environment, respectively. Results show the HE have similar performance when evaluating artefacts usability in VR and real-world in terms of identified usability problems. The VR implementation has an impact on the immersive VR evaluation result.
Towards the Inclusion of Executive Coaching Concerns for the Improvement of Training in Service Development Companies

Economic expansion and globalization have greatly increased the value associated to teamwork, thus promoting personal development and learning as distinguishing skills, especially in service development companies. This encourages workers to gain adaptability to changes due to a higher need of reaction capacity, particularly among supervisors and managers who demand more training according to their characteristics. In this regard, conducting in-company training is globally agreed as a possible solution. Such training activities are developed in a flexible way, varying both in the content and the level of the participants. Advanced training techniques such as mentoring or coaching (or its professional counterpart: executive coaching) have permitted the application of practices and processes that boost learning, improving the performance of the training. Although coaching has an individual focus, it also enhances the development of interpersonal skills such as leadership, assertiveness or teamwork, among others. This paper presents a statistical analysis of the evaluations conducted on a total of eight groups incorporating an in-company training activity performed in 2011. The work has focused on comparing assessments of participants in training for a large multinational company working in the field of service development. The main goal of the technical training was the improvement of service-software development processes incorporated in the company throughout the completion of various activities. These training activities were based on the application of executive coaching techniques so as to progressively improve the fulfilment of the expectations of the technical course. After the courses we analysed several evaluation questionnaires fulfilled by each of the participants in each group. The result of such analysis exposed a vast improvement in meeting the expectations of the training and even an overall improvement of the course.
Modeling and Simulation of UAV Autonomous Obstacle Avoidance Based on DQN

During the obstacle avoidance process of UAV, the route planning and obstacle avoidance decision-making is dynamic and sequential due to the change of obstacles in real time, so it is difficult to build a dynamic and accurate route planning model. This paper breaks through the traditional research thinking of route modeling and optimization solution for UAV obstacle avoidance, and applies deep reinforcement learning to UAV autonomous obstacle avoidance decision-making; through designing the environment model, autonomous decision-making model and DQN algorithm model for UAV obstacle avoidance, four simulation experimental environments for UAV obstacle avoidance are constructed to verify the superiority and effectiveness of deep reinforcement learning in solving the decision-making problems related to dynamic model and timing sequence, and provide a new solution for future UAV route planning.
Socio-philosophical cognition of the phenomenon of uncertainty and forecasting in education

The principle of uncertainty operates in the perspective of discussing the modern Russian education modernization, it arises in the circumstances of unclear public priorities, goals, values. At the educational practice viewpoint, the holistic methodology absence for the philosophical understanding of certainty and uncertainty of ontological, epistemological, epistemological, axiological research programs, is reflected in both satisfying human cognitive needs and forecasting in education. Developing a methodological formalization of concepts «certainty», «uncertainty», «ontological uncertainty» (non-articulation of properties and behavior of the system or its individual element), «epistemological uncertainty» (in techniques of cognition, forming a worldview, implementing innovations, as well as interpreting the quality of education from the standpoint market laws), «epistemological uncertainty» (parametric, random, ambiguous and uncertainty of interaction) are associated with a critical discussion of the essential signs of uncertainty in society and education, which are becoming the research subject of the education philosophy. A survey of philosophers’ opinions regarding the education models of the future pointed to the critically-oriented nature of already existing prototypes and the development of their scenarios. The need to determine the theoretical status of «uncertainty in education» is closely related to understanding a person's place in the world, agreeing in the public consciousness of a consistent understanding of the axiological categories: «value of education» and «education as a value» and their new interpretation based on the articulation of «axiological certainty» as new ecosystems of values and principles of their functioning in the education of the future. Understanding the changes in the ontological, epistemological and axiological foundations of Russian education is private explications of socio-philosophical subjectivism to a certain extent. Modern concepts discussing the axiological reversal in educational practices concentrate around the problem of axiological certainty, namely: the preservation of civilization and the hierarchical nature of educational goals and values as an important component of effective education management.
Multi-subspace RBFNN driven by features correlation learning

For the radial basis function neural network (RBFNN), the centers of the kernels and the network weight are critical to the network performance. The expectation maximization (EM) algorithm can learn the network parameters in RBFNN adaptively. However, high-dimensional samples often have complex distributions in the feature space. In this case, the kernels learned by the EM algorithm may be inaccurate or the algorithm cannot converge. To address these problems, this paper proposes a multi-subspace RBFNN (MS-RBFNN) based on features correlation learning. Inspired by the two-dimensional convolutional neural network sliding window operation to extract local information, this paper uses the correlated features to construct feature subsets with local characteristics. In each subspace, a multi-layer RBFNN is used to extract the local distribution response characteristics of samples. Finally, the distributed response features in multi subspaces are combined to perform classification tasks. This method can use more potential local characteristics of vector features to learn more information about the data. At the same time, The constructed low-dimensional subspaces make the network easier to train and use. Experiment results show that compared with some existing algorithms, the model proposed in this paper has a higher classification accuracy than the state-of-the-art method.
Tsunami Potential Prediction using Seismic Features and Artificial Neural Network for Tsunami Early Warning System

Tsunamis are categorized as geophysical disasters because tectonic earthquakes triggered most of their occurrences.The high number of deaths due to tsunami catastrophe has made many countries develop a tsunami early warning system (TEWS), especially countries prone to tectonic earthquakes.One of the crucial subsystems in a TEWS is the tsunami potential prediction subsystem.To provide an early warning of tsunami, the prediction must be carried out based on early observation of the seismic event before the tsunami.In this short time of computation, the calculation of seismic parameters can only produce some roughly estimated features.Hence, a proper inference method that can decide accurate predictions upon the features is urgently needed for the TEWS.Some existing TEWSs are using rule-based inference to decide the prediction and often overestimate the prediction of tsunami potential.This study tries to develop a tsunami-potential prediction system using the machine learning approach as its inference method.Seismic features extracted from P-wave seismic signals are used as input data for learning and classification using a backpropagation artificial neural network (ANN).The accuracy result is then validated by K-fold cross-validation.Our simulation results show that the utilization of backpropagation ANN has given better accuracy in tsunami prediction compared to one of the existing TEWS that does not use machine learning for its prediction.At least for some seismic events that occurred during 2010-2017, the proposed system results in fewer overestimated predictions than the existing TEWS referred.
Compositional Process

Most composers have a fairly specific process they use to compose. It’s important to realize that, like any marketable skill, composing takes time and effort to master. How composers spend the time they have will drastically affect the outcome. Technology can be a helpful tool if used correctly, but many would argue that nothing can replace pencil and paper. There isn’t a “correct” way to compose, but one needs to commit to discovering what works best on an individual basis, honing a useful skill set, and finding their own voice and style.
The Positive Impact of Red Songs on College Students’ Anxiety

In this paper, in order to investigate whether red songs have a positive and effective effect on anxiety in college student groups, the Positive Psychological Quality Scale and the Self-Assessment of Anxiety Scale (SAS) for Chinese College Students were used as experimental measurement tools, and this experimental design was conducted on 123 college student subjects, and 82 were randomly selected and divided into a control group and an experimental group, with 41 students in each group.The experimental results showed that (1) gender, age and whether the college students were only children were not correlated with the measured positive psychological qualities of Chinese college students and college students' anxiety self-ratings.(2) The anxiety self-ratings of college students with no music background were significantly higher than the anxiety self-ratings of college students after listening to red songs.(3) The positive psychological quality scores of college students without music were significantly lower than those of college students after listening to red songs.
Twitter And Instagram Sentiment Analysis of Covid

This paper tries to examine the technology utilized in Social Media Sentiment Analysis. Sentiment evaluation, additionally referred to as opinion mining, is a technique that identifies the emotional tone in the back of a textual content. It facilitates businesses to acquire insights from unorganized and unstructured textual content that comes from online assets consisting of emails and social media channels. Sentiment evaluation gear may be utilized by businesses for a number of programs, consisting of figuring out emblem awareness, gathering client comments from social media, websites or online forms. Aspect-based sentiment analysis is the task of identifying opinion polarity towards a specific aspect in a sentence can be divided into two subtasks: aspect-term sentiment analysis (ATSA) and aspect-category sentiment analysis (ACSA). In the past, an extra normally used approach turned into undertaking the time serial set of rules consisting of Long Short-Term Memory (LSTM) or RNN, which commonly wishes extra schooling time and has complicated structures. We will construct a version primarily based totally on gating mechanisms, mixed with convolutional neural networks (CNN) and self-interest mechanisms.
Dynamic Hand Gesture Recognition of the Days of a Week in Indian Sign Language Using Low-Cost Depth Device

We develop a dynamic hand gesture recognition system on the gestures of seven days of the week in Indian Sign Language. We use Kinect V2 sensor, which is a low-cost RGB-D camera, to collect videos of gestures of two subjects and then perform key frame extraction, cropping of static regions, background subtraction, feature extraction and classification. On the resultant dataset, tenfold cross-validation by random forest classifier gives us an accuracy of around 74.29%.
Diagnosis of Future Demand for the Design of University Courses

This study develops a method to determine future needs and competencies required for the design of future professional careers, this aligned with the demands: country, institutional and business. The article is based on a study of primary and secondary sources: current state of supply, working companies, dynamics with parents, student surveys nationwide. Finally, findings are made regarding future demand careers: national needs, labor demand for skills, educational supply, educational needs. And it concludes with a study plan of future needs for adapt.
[Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree].

Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络。本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差。然后,基于最优的变量组合建立人工神经网络(ANN)生物量模型,并采用留一交叉验证法对模型进行检验,比较两种生物量估测方法的精度。结果表明: 仅基于胸径一个变量的生物量模型已经能较准确地估测生物量,引入树高和冠幅因子能进一步提高模型精度,最优模型体系为AMS4。通过两种方法建立的生物量模型都能较准确地进行估测,各组分生物量的决定系数(R2)均高于0.87。相比AMS4,人工神经网络模型系统中,树叶生物量模型的R2高了约0.05,其余各器官也高了0.01左右。此外,均方根误差(RMSE)等指标明显更小,树干和地上生物量的RMSE分别减小了2.135和3.908 kg,模型的检验指标如平均相对误差(MRE)等也表现更优。总体来看,人工神经网络(ANN)是一种灵活可靠的生物量估计方法,估测林木地上生物量或单独某器官生物量时,ANN模型是值得考虑的替代方法。.
Virtual programming environments: programming the cloud (keynote)

Cloud-based applications are ubiquitous and essential. We expect them to be simultaneously scalable and available and simple to build and deploy. Virtual Programming Environments are what make these applications possible. Virtual Programming Environments are themselves complex distributed systems, built using the entire spectrum of System and Runtime Virtualization technology that is the subject of VEE. In the first part of the talk, I will focus on the purest form of Serverless Computing: Functions as a Service (FaaS) as embodied in Virtual Programming Environments such as AWS Lambda or Apache OpenWhisk. I will describe the programming abstractions they provide to the developer and how these abstractions are realized using virtualization technology. In the second part of the talk, I will outline the research challenges in moving beyond FaaS to build Virtual Programming Environments that can productively support building complex stateful applications on the cloud.
Asymmetry quantification in cross modal retrieval using copulas

Copulas are used to describe and explain the asymmetry between image-text and text-image retrieval observed in different values of the mean average precision (MAP). We use empirical copulas to quantify the asymmetry in a general framework of cross-modal retrieval via suitable asymmetry measures. Several experiments are done on real world dataset feautures to prove the relevance of our analysis.
Statistical Parameter Estimation Theory

The principles of statistical parameter estimation are presented. Models, parameters and estimators are explained. The notion of Fisher information is introduced and the Cramér–Rao bound is established. The roles of maximum a posteriori probability and hidden Markov modelling are described.
Sentiment analysis of political discussion on Twitter in Nigeria's 2019 presidential election

Social media have helped in political communication by reinforcing social change and commitments from governments. This study analyses political discussion on Twitter during the 2019 Nigeria general election. The aim is to identify popular issues discussed and opinion of citizens on popular presidential candidates' social media network and correlate the online discussion with real world events during the 2019 election. Five most popular presidential candidates were selected for the investigation. Latent Dirichlet allocation (LDA) model was used to identify frequent terms and topics discussed. Sentiment of the tweets was analysed using National Research Council Canada (NRC) Emotion Lexicon. Findings revealed that Twitter is mainly used for political discussion that cantered on personal representation, political party's promotion and elections matters. The behaviour of users shows that Twitter platform is not being used to share original political ideologies, instead the platform was used for re-tweeting of tweets and political campaigns for candidates.
Experimental and theoretical advances push study of metastable cortical neural networks forward

New observations show promise for better understanding a range of neural processes in several species, from stimulus coding to decision making.
DEEP NEURAL NETWORK ALGORITHMS AND THEIR ROLE: A SYSTEMATIC REVIEW

DNNs (Deep Neural Networks) are widely employed in advanced applications including image and audio processing. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DNNs that have been popular for industrial applications in recent years. RNNs are well-suited to time variation problems due to their recursive structure. RNNs are ideally suited for temporal variation concerns due to their recursive structure, whereas CNNs are often employed in computer vision applications such as object recognition. Despite the fact that CNNs and RNNs are both DNNs, their implementation differs significantly. Recurrent Neural Networks (RNN) can be used to solve the sequence to sequence problem when both the input and output have sequential structures. There are always some unseen links between the structures. The traditional RNN technique, on the other hand, has trouble examining the links between the sequences appropriately. This survey introduces some attention-based RNN models, applications, types, and DNN algorithms that can focus on different features of the input for each output item in order to investigate and exploit the implicit links between the input and output items.
Comparing Machine Learning Models for Earthquake Detection

A new study evaluated the performance of emerging deep learning models for earthquake detection, phase identification, and phase picking.
Critical healthcare for the elderly against COVID-19 : a multidisciplinary approach

Objective: Describe critical health care for the elderly against COVID-19 in a multidisciplinary approach. Method: This is a qualitative study, with an exploratory documentary and detailed descriptive approach, with a bibliometric emphasis. Where several studies will be gathered for the characterization of each element present in their results. Results and discussion: Several studies were analyzed and discussed about the topics related to this study, they are: critical health care, health of the elderly, Covid-19 and multi disciplinary care. Scientific articles of national and international origin contributed effectively to the discussion of these results. Conclusion: This study achieved its objective, as it described critical health care for the elderly against COVID-19 in a multi disciplinary approach, it contributed satisfactorily to the entire academic community world wide, enabling the creation of new studies on this topic.
Research on the Application Ability of Mathematical Modeling and Software Simulation under Big Data Technology

The thesis first collects data through questionnaire surveys, and conducts statistical analysis of the reasons why students have insufficient application ability in mathematical modeling. The purpose is to understand students’ modeling ability and the root cause of their problems, to correct the problem, prescribe the right remedy and propose solutions for the next step. The method prepares the theoretical basis. Secondly, the article designs and implements a Spark-based mathematical modeling analysis modeling system for college students. The system realizes the adaptive collection and offline and online analysis and processing of structured, semi-structured and unstructured data through five modules: data acquisition, data storage, data analysis, data management and data application. The research results found that the proposed system can achieve effective and accurate clustering of specific scene data of college students’ mathematical modeling, and can meet the speed and accuracy of big data processing.
Building a Platform for Data-Driven Pandemic Prediction from Data Modeling to Visualization – The CovidLP Project

"Building a Platform for Data-Driven Pandemic Prediction from Data Modeling to Visualization – The CovidLP Project." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–4
Reducing the noise in power quality disturbance with VMD

In this paper, an improved variational modal decomposition (VMD) denoising algorithm is proposed. Firstly, the noisy signal is decomposed into several intrinsic modes (IMF); Then the correlation coefficient algorithm is used to determine the optimal number of decomposition layers; The relevant modes and useful components are extracted and reconstructed to obtain the denoised signal. Practice shows that this method has better denoising effect than traditional empirical mode decomposition and wavelet decomposition.