Reviewerly/exHarmony huggingface dataset:
Digitalisation of Schools from the Perspective of Teachers’ Opinions and Experiences: The Frequency of ICT Use in Education, Attitudes towards New Media, and Support from Management

The digitalisation of education has become an irreversible process, and Poland is no exception. However, the issue of ICT usage in education raises many concerns and controversies, posing numerous methodological challenges at the same time. In the interpretation of our research, one of the most frequently used and validated models in empirical research—the Unified Theory of Technology Acceptance and Use (UTAUT) model—was used. The aim of the research was to show the frequency of use of popular hardware, software and websites among teachers from Poland (in the Silesia Province) and finding answers to the questions pertaining to the main determinants of digital teaching aids. The frequency of ICT use in education was juxtaposed with the attitudes towards new media in the school environment as well as with the support of managers in that area. Quantitative research was conducted on a sample of N = 258 in 2020. A questionnaire in the form of a digital diagnostic survey was used to collect data. Based on the data collected, it was noted that: (1) Teachers use virtual systems (eRegisters) and interactive whiteboards most often, while educational podcasts and software for learning foreign languages are used least often; (2) approximately 40% of teachers use ICT often or very often in their school teaching; (3) in public schools, digital teaching aids are used slightly more often than in non-public institutions; (4) teachers are consistent in their use of digital teaching aids, so the individuals experimenting with and implementing ICT at school do so regardless of the type of software and hardware; (5) active support from school head teachers strengthens the frequency of ICT use in education; and (6) in schools where the use of smartphones by students is prohibited, the frequency of ICT use in education is at a lower level.
A novel multiple kernel fuzzy topic modeling technique for biomedical data

Abstract Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction

Joint relational triple extraction from unstructured text is an important task in information extraction.However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.To address the issues, we introduce a new blank filling paradigm for the task, and propose a relation-first blank filling network (RFBFN).Specifically, we first detect potential relations maintained in the text to aid the following entity pair extraction.Then, we transform relations into relation templates with blanks which contain the fine-grained semantic representation of the relations.Finally, corresponding subjects and objects are extracted simultaneously by filling the blanks.We evaluate the proposed model on public benchmark datasets.Experimental results show our model outperforms current state-of-the-art methods.The source code of our work is available at: https: //github.com/lizhe2016/RFBFN.
Efficient Classification of Long Documents Using Transformers

Several methods have been proposed for classifying long textual documents using Transformers.However, there is a lack of consensus on a benchmark to enable a fair comparison among different approaches.In this paper, we provide a comprehensive evaluation of the relative efficacy measured against various baselines and diverse datasets -both in terms of accuracy as well as time and space overheads.Our datasets cover binary, multi-class, and multilabel classification tasks and represent various ways information is organized in a long text (e.g.information that is critical to making the classification decision is at the beginning or toward the end of the document).Our results show that more complex models often fail to outperform simple baselines and yield inconsistent performance across datasets.These findings emphasize the need for future studies to consider comprehensive baselines and datasets that better represent the task of long document classification to develop robust models.
Exploration of Material Mechanics Teaching in Vocational Colleges Based on Engineering Case Introduction

Material Mechanics is an important course for students of science and engineering in higher vocational colleges.However, the unfavourable teaching quality of the course has been a problem for a long time.One key factor affecting the teaching effect is the students' lack of interest in learning.To solve this problem, this paper analyzes the problems existing in the teaching of Material Mechanics, and proposes a method of introducing engineering cases to enhance students' interest in learning.The teaching practice has achieved good results.Students' learning enthusiasm has been strengthened, and the interaction between students and teacher in the classroom has also been improved.The teaching method expounded in this paper should be applied to more courses of vocational education.
An Eye State Recognition System Using Transfer Learning: AlexNet-Based Deep Convolutional Neural Network

Abstract For eye state recognition (closed or open), a mechanism based on deep convolutional neural network (DCNN) using the Zhejiang University (ZJU) and Closed Eyes in the Wild (CEW) dataset, has been proposed in this paper. In instances where blinking is consequential, eye state recognition plays a critical part for the development of human–machine interaction (HMI) solutions. To accomplish this objective, pre-trained CNN architectures on ImageNet were first trained on the both dataset, which included both open and closed-eye states, and then they were tested, and their performance was quantified. The AlexNet design has proven to be more successful owing to these assessments. The ZJU and CEW datasets were leveraged to train the DCNN architecture, which was constructed employing AlexNet modifications for performance enhancement. On the both datasets, the suggested DCNN architecture was tested for performance. The achieved DCNN design was found to have 97.32% accuracy, 95.37% sensitivity, 97.97% specificity, 93.99% precision, 94.67% F 1 score, and 99.37% AUC values in the ZJU dataset, while it was found to have 97.93% accuracy, 98.74% sensitivity, 97.15% specificity, 97.11% precision, 97.92% F 1 score, and 99.69% AUC values in the CEW dataset. Accordingly, when compared to CNN architectures, it scored the maximum performance. At the same time, the DCNN architecture proposed on the ZJU and CEW datasets has been confirmed to be an acceptable and productive solution for eye state recognition depending on the outcomes compared to the studies in the literature. This method may contribute to the development of HMI systems by adding to the literature on eye state recognition.
OutdoorSent

Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures, and one specifically designed for sentiment analysis. We also evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification and cross-dataset generalization performance. The evaluation explores a novel dataset, namely OutdoorSent, and other datasets publicly available. We observe that the incorporation of knowledge about semantic attributes improves the accuracy of all ConvNet architectures studied. Besides, we found that exploring only images related to the context of the study, outdoor in our case, is recommended, i.e., indoor images were not significantly helpful. Furthermore, we demonstrated the applicability of our results in the city of Chicago, USA, showing that they can help to improve the knowledge of subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment, which are also correlated with median income, opening up opportunities in different fields.
Atmospheric Factors Affecting Global Solar and Photosynthetically Active Radiation Relationship in a Mediterranean Forest Site

Light availability and its composition in components affecting plant growth as photosynthetically active radiation (PAR), are of critical importance in agricultural and environmental research. In this work, radiation data for the period 2009–2014 in a forest site in Greece were analyzed to identify the effect of meteorological variables on the formation of the photosynthetically active to global solar radiation ratio. The temporal changes of the ratio are also discussed. Results showed that the ratio values are higher in summer (0.462) and lower in autumn (0.432), resulting in an annual average of 0.446. In addition, for the investigated site, which was characterized by relatively high water content in the atmosphere, the atmospheric water content and clearness were found to be the most influential factors in the composition of the global solar radiation in the wavelengths of PAR. On the contrary, temperature and related meteorological attributes (including relative humidity, vapor pressure deficit and saturation vapor pressure) were found to have minor effect.
Statistical Analysis and Distribution of Global Solar Radiation and Temperature Over Southern Nigeria

The intensity of solar energy that is received by a particular location is affected by most meteorological conditions including, the solar irradiance received by the location, precipitation, extreme heat as a result of the surface or ambient temperature, etc. We obtain the monthly global solar irradiation and ambient temperature for the three (3) eco-climatic zones in the south of Nigeria (17 locations) for 12 years (2005 - 2016) from the Photovoltaic Geographical Information System (PVGIS) Satellite. The goal of this study is to understand how regional meteorological conditions affect radiation and temperature reception. Monthly and annual trends were plotted and compared for both variables in each region to show the similarity or dichotomy in their trends. The Mann-Kendall (M-K) trend test has been adopted to reveal that the changes in the variations on an annual basis, and results showed that the trend were not significant for both variables. Box plots have been used to give a better description of the data, and compared to show similarities and differences. Finally, we adopted the Gaussian (normal) distribution to show, understand and compare the data distribution. Linear regression plots for each zone shows that the relationship between the solar irradiation and temperature is high. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneityof data or results for locations in the same zones may be attributed to local meteorological conditions. The results obtained here will prove vital in decision making relating to the adoption of solar energy technologies in the region. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneity of data or results for locations in the same zones may be attributed to local meteorological conditions.
Comparison of the effect of 360° versus two-dimensional virtual reality video on history taking and physical examination skills learning among undergraduate medical students: a randomized controlled trial

Abstract Before caring for patients, video instruction is commonly used for undergraduate medical students, and 360° virtual reality (VR) videos have gained increasing interest in clinical medical education. Therefore, the effect of immersive 360° VR video learning compared with two-dimensional (2D) VR video learning in clinical skills acquisition should be evaluated. This randomized, intervention-controlled clinical trial was aimed to assess whether immersive 360° VR video improves undergraduate medical students' learning effectiveness and reduces the cognitive load in history taking and physical examination (H&amp;P) training. From May 1 2018 to October 30 2018, 64 senior undergraduate medical students in a tertiary academic hospital were randomized to receive a 10-min immersive 360° (360° VR video group; n = 32) or 2D VR instructional video (2D VR video group; n = 32), including essential knowledge and competency of H&amp;P. The demographic characteristics of the two groups were comparable for age, sex, and cognitive style. The total procedure skill score, physical examination score, learner’s satisfaction score, and total cognitive load in the 360° VR video group were significantly higher than those in the 2D VR video group (effect sizes [95% confidence interval]: 0.72 [0.21–1.22], 0.63 [0.12–1.13], 0.56 [0.06–1.06], and 0.53 [0.03–1.03], respectively). This study suggested that a10-minute 360° VR video instruction helped undergraduate medical students perform fundamental H&amp;P skills as effectively as 2D VR video. Furthermore, the 360° VR video might result in significantly better procedural metrics of physical examinations with higher learner satisfaction despite the higher cognitive load.
Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization

In this article, a new path planning algorithm is proposed. The algorithm is developed on the basis of the algorithm for finding the best value using multi-objective evolutionary particle swarm optimization, known as the MOEPSO. The proposed algorithm is used for the path planning of autonomous mobile robots in both static and dynamic environments. The paths must follow the determined criteria, namely, the shortest path, the smoothest path, and the safest path. In addition, the algorithm considers the degree of mutation, crossover, and selection to improve the efficiency of each particle. Furthermore, a weight adjustment method is proposed for the movement of particles in each iteration to increase the chance of finding the best fit solution. In addition, a method to manage feasible waypoints within the radius of obstacles or blocked by obstacles is proposed using a simple random method. The main contribution of this article is the development of a new path planning algorithm for autonomous mobile robots. This algorithm can build the shortest, smoothest, and safest paths for robots. It also offers an evolutionary operator to prevent falling into a local optimum. The proposed algorithm uses path finding simulation in a static environment and dynamic environment in conjunction with comparing performance to path planning algorithms in previous studies. In the static environment (4 obstacles), the shortest path obtained from the proposed algorithm is 14.3222 m. In the static environment (5 obstacles), the shortest path obtained from the proposed algorithm is 14.5989 m. In the static environment (6 obstacles), the shortest path obtained from the proposed algorithm is 14.4743 m. In the dynamic environment the shortest path is 12.2381 m. The results show that the proposed algorithm can determine the paths from the starting point to the destination with the shortest distances that require the shortest processing time.
A Case Study on Implementing Agile Techniques and Practices: Rationale, Benefits, Barriers and Business Implications for Hardware Development

Agile methodologies, along with the corresponding tools and practices, are claimed to facilitate teams in managing their work more effectively and conducting their work more efficiently while fostering the highest quality product within the constraints of the budget. Therefore, the rate of awareness and adoption of Agile frameworks both within and outside the software industry has increased significantly. Yet, the latest studies show that the adoption of Agile techniques and practices are not one-size-fits-all, and highlight the challenges, risks, and limitations regarding numerous domains. In this regard, the state-of-the-art literature provides comprehensive reading. However, in the case of hardware manufacturing, it seems to be sparse and fragmented. To fill this gap, the goal of this study is to analyze and present an in-depth account of the implementation of mix agile-oriented tools and practices. To tackle this goal, a single industry case study was undertaken, based on the primary data obtained through the interview protocol and the secondary data extracted from the project’s documentation. The findings concern three areas. First, the rationale behind the implementation of agile for hardware development is explained. Second, the implemented agile techniques and practices are identified, as well as the supporting tools through which their adoption was successfully undertaken. Third, the areas positively impacted by their application are highlighted with the corresponding evaluation measures deployed; moreover, the barriers to adopting Agile practices encountered, and the benefits gained from particular techniques, are further discussed. The presented findings might be of great importance for both researchers and practitioners who are searching for empirical evidence regarding Agile-oriented implementations. Finally, in terms of both benefits and barriers, business implications for hardware development are formulated. Alongside this, numerous open issues and questions present interesting research avenues that concern, in particular, the effectiveness of collaboration and areas of communication through the lens of agile techniques and practices.
Virtual Mouse using Machine Learning and GUI Automation

With the rapid advancement of computer technology, it is becoming increasingly important for us to develop new ways of interacting with computers. From CLI (Command Line Interface) to GUI (Graphical User Interface) the growth has been astounding. The future is going to be NUI (Natural User Interface) where the user makes use of one&#x0027;s natural environment to provide input to machine. Imagine playing Video games likes Temple Run, Subway Surfers or even play a musical instrument on your computer without touching, this way also helps in improving the digital well-being of humans. NUI/NUX will also be a lot helpful during the time of pandemic where we can reduce touch in many public places. An input module, such as a virtual mouse that uses Object Detection, Object Tracking and Gestures to assist us in communicating, could be a viable replacement to the traditional touch screen and hardware mouse. The system design proposed is a TensorFlow-based mouse controlling system, which uses hand gestures that are captured through a webcam using an RGB color scheme. This solution permits users to control the system cursor using their hand, that the computer webcam tracks and perform mouse operations like Left click, Right click, Scroll, Drag, Move using different hand gestures. Python, TensorFlow and OpenCV libraries are used for real time computer vision to implement the system.
A Variational Bayes Association-based Multi-object Tracker under the Non-homogeneous Poisson Measurement Process

The non-homogeneous Poisson process (NHPP) has been widely used to model extended object measurements where one object can generate zero or several measurements; it also provides an elegant solution to the computationally demanding data association problem in multiple object tracking. This paper presents an association-based NHPP system, based on which we propose a variational Bayes association-based NHPP (VB-AbNHPP) tracker that can estimate online the object kinematics and the association variables in parallel. In particular, the VB-AbNHPP tracker can be easily extended to include online static parameter learning (e.g., measurement rates) based on a general coordinate ascent variational filtering framework developed here. The results show that the proposed VB-AbNHPP tracker is superior to other competing methods in terms of implementation efficiency and tracking accuracy.
Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications

Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.