Reviewerly/exHarmony huggingface dataset:
A Theoretical Examination of the Composite Index or Measuring a Variable Using Likert Scale

The assumption whilst using Likert scales is that the intensity or strength of character and personality traits such as attitudes can be measured on a linear continuum from strongly agree to strongly disagree. This paper discusses Likert scale items vis-a-vis Likert type items and highlights the difference between the two. Likert scale used interrelated items that can be converted into a composite score during data analysis. As such appropriate inferential statistical techniques can be applied. Conversely Likert items are individual items based on an ordinal scale and data analysis is specific to the item and only non-parametric statistical methods are appropriate.
A multi-case study on the development and implementation of convergence curriculum for pre-service teachers: Focusing on the three instructors’ experiences

Objectives The purpose of this study is to examine the development, implementation, and evaluation process of convergence curricular for pre-service teachers, and to analyze the instructors’ internal and external factors that influenced their practice of the curricula along with the related difficulties.&#x0D; Methods In this study, three cases of developing and implementing convergence curriculum for pre-service teachers were explored. First, individual interviews were conducted with the instructor in charge of each case, and various literature data were collected for a comprehensive understanding and explanation of each case. The collected data were analyzed in inductive methods and procedures focusing on the development-implementation- evaluation process and related factors of the convergence curriculum for pre-service teachers.&#x0D; Results The convergence curricula for pre-service teachers were practiced in various ways in the form of interdisciplinary content integration(webbed, integrated, shared), learner needs survey, evaluation method(criteria- based evaluation, relative evaluation, alleviated relative evaluation), collaboration between instructors, connection with various sites including school sites, and evaluation-and-feedback. Next, the instructors' perceptions of teacher education and convergence education, the form of subjects(liberal arts, major subjects), and human and material support emerged as factors influencing the practice of the convergence curriculum. Finally, communication and collaboration between instructors were identified as one of the biggest difficulties in the practice of convergence curriculum for pre-service teachers, which was closely related to the fragmented culture and system in the current teacher preparation programs.&#x0D; Conclusions For further development and implementation of convergence curriculum for pre-service teachers, first, in the development stage of the convergence curriculum, teacher educators need to select academic (curricular) integration models, and actively reflect the learner's needs in designing their instruction contents and methods. Second, it is necessary to expand the criteria-based evaluation system to facilitate the practice of the convergence curriculum (i.e., increasing the preference of pre-service teachers for convergence education subjects and the flexibility of teaching and learning). Third, in order to invigorate the convergence curriculum for pre-service teachers, it is necessary to promote cooperation between instructors from various academic backgrounds, expand opportunities to apply what they learned to the context of school and classroom, and provide opportunities to get involved in diverse field experiences.
Design of an Educational Application for Conservation and Ornamental Fish Capture Areas Based on Android

This research aims to design an educational application about conservation and ornamental fish capture areas in Pangkajene Islands Regency. The application is developed based on the Android operating system to facilitate access and usage for the community. In the testing process, the application will undergo unit testing and compatibility testing to ensure that every part of the application works correctly and can be used smoothly on various devices and operating systems. The application will provide information about the conservation and ornamental fish capture areas in Pangkajene Islands Regency, including the types of ornamental fish and how to conserve the aquatic ecosystem in the area. With the presence of this application, it is expected to provide useful information for the community and increase awareness about the importance of preserving the aquatic ecosystem and understanding the concept of conservation and ornamental fish capture areas. Through unit and compatibility testing, it is expected that the application will work correctly and can be used smoothly on various devices and operating systems.
Building Web-Based Subject-Specific Corpora on the Desktop: Evaluation of Search Metrics

Building subject-specific or domain corpora from Web data is well-researched. However, most approaches start by using seed articles as inputs to Web crawlers and take document similarity algorithms for selection. We take a different lean resource approach by applying traditional search metrics with a relatively large (more than 100 search terms) ‘bag of domain words’ approach on the colossal clean crawled corpus. This approach enables one to build rich domain corpora of text documents quickly in a resource-poor environment (e.g., a few CPU cores). This paper tests several metrics using three different subject domains—language, Colossal Clean Crawled Corpus basic mathematics, and information science—and finds that there are significant performance differences between the various metrics. Surprisingly, a naïve, simple metric, outperforms TD-IDF and performs almost as well as our top ranked algorithm, Okapi BM25. This demonstrates that the performance of search metrics using a relatively larger number of search key words (> 100) is different than when a small set of search key words is used. We also demonstrate how to optimize the free parameters for Okapi BM25.
On the Relationship Between Universal Adversarial Attacks and Sparse Representations

The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in image input. In this work, we aim at explaining this vulnerability through the framework of sparsity. We show the connection between adversarial attacks and sparse representations, with a focus on explaining the universality and transferability of adversarial examples in neural networks. To this end, we show that sparse coding algorithms, and the neural network-based learned iterative shrinkage thresholding algorithm (LISTA) among them, suffer from this sensitivity, and that common attacks on neural networks can be expressed as attacks on the sparse representation of the input image. The phenomenon that we observe holds true also when the network is agnostic to the sparse representation and dictionary, and thus can provide a possible explanation for the universality and transferability of adversarial attacks.
Marker-less Motion Capture Technology Based on Binocular Stereo Vision and Deep Learning

Among the current human pose estimation methods, the more mature one is the two-dimensional (2D) human pose estimation method. However, the 2D information is difficult to reflect the real posture of the human body in space, and the three-dimensional (3D) estimation algorithm is imperfect and has poor accuracy. In this paper, based on the fusion of binocular stereo vision and convolutional neural network (CNN), a new method of 3D human pose estimation was carried out. In order to verify the accuracy of this method, we used the method in this paper and Microsoft Kinect V2 to capture action images to reconstruct the 3D pose of the human body. The results showed that within the working distance of 4450-4700mm, the minimum root mean square error (RMSE), the minimum mean absolute error (MAE) and the minimum mean absolute percentage error (MAPE) of the action performed by the testers could reach 20.469, 16.408 and 4.508. The length accuracy of human joints restored by this method was higher than that of Kinect V2, and the restoration of human actions in 3D space was higher. It can be concluded from the experimental results that the proposed method might have the ability to reduce the cost and limitation of current human motion capture, and may be applied in the fields of stroke patient rehabilitation and community rehabilitation in the future.
Special Section on Resiliency techniques in cloud computing infrastructures and applications

The Internet of Things (IoT) consists of a collection of inter-connected devices that are used to transmit data. Secure transactions that guarantee user anonymity and privacy are necessary for the data transmission process. One of the major constraints of IoT Healthcare is the issue of maintaining security. These problems are addressed in this study by the Contingent Anonymity-Preserving Privacy Method (CAP2M), which takes into account the various security needs. The CAP2M method uses the session deployment and user requirements to preserve user privacy. The initial and final privacy settings are modified in the session deployment depending on the session interval and the service provider's recommendation. Federated learning is employed for training privacy settings toward the session's final interval. The suggested approach maintains privacy on various sharing intervals with minimum computation complexity. In the privacy-preserving process, two-factor authentication is used for concealing the sharing and shared ends. The performance is validated using the metrics failure, complexity, and latency.
Investigation of the Peculiarities of the Metrorhythmic Organization of Onegin's Vocal Part in P.I. Tchaikovsky's Opera "Eugene Onegin"

The main attention of the author of the work focuses on the study of temporal structures in the solo episodes of Eugene Onegin. In this work, based on the research of theorists and musicologists, a detailed analysis of metric systems and rhythm formulas in the musical material of the protagonist is carried out. Special attention is paid to the study of the hidden meaning of leit rhythms as means of artistic expression, independent of the melodic-harmonic component of the musical fabric. The author examines in detail such aspects of the topic as accentuation and "broad three-lobe", polyrhythm and polymetry, elements of polyfurcation and fractal processes of shaping, shift of the clock line and higher-order bars. Special attention is paid to the influence of the metrorhythmic side on the figurative and artistic component of the process of creating and developing the plot of an opera performance. The main conclusion of the study is the need to study the regularities of metrorhythmic constructions in vocal parts. This will allow performers to more deeply and comprehensively penetrate deep into the semantic musical structures, get additional opportunities to control the performing apparatus while working on creating an artistic image. This approach is relevant and quite new in terms of the originality of the research perspective, since so far this topic has not been presented in a similar way in the scientific literature. The presence of musical examples, quotations from scientific literature, tables and diagrams turns the work into a visual and easy-to-study scientific material.
Service Robots and AI in the Frontline1

The Encyclopedia of Services is a ground-breaking resource that offers a unique overview of what constitutes the main source of wealth and employment in our contemporary economies, namely services. This title contains one or more Open Access chapters.
An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)

In the past decade, technology has grown exponentially, especially the speed of the Internet and mobile technology have reached its peak it seems. This technology advancement also gives its impact to all the areas especially in the education sector. Researchers have to be interested in investigating how these technologies can be exploited for educational purposes aiming to enhance learning experiences. Subsequently, this has prompt an exploration slant which is ordinarily alluded to as Mobile Learning (M-Learning) in which specialists endeavors have meant to disseminate fitting learning encounters to learners considering their own flexibility needs, the universal usage of portable advances and the accessibility of data whenever anyplace. By and by, m-learning is still in its start and extraordinary endeavors should be done as such as to explore the potential outcomes of educational outlook change from the conventional on-estimate fits-all illuminating ways to deal with a versatile and customized discovering that can be circulated by means of portable creations. This paper, presents the suitability and need of mobile learning faciltity in Shinas College of Technology(SHCT) and also presents the framework for implemeting m-learning in SHCT.
Individualized causal discovery with latent trajectory embedded Bayesian networks

Abstract Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However, such assumption can be seriously violated, causing significant biases when the underlying population is inherently heterogeneous. To this end, we propose a novel causal Bayesian network model, termed BN-LTE, that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. This new framework allows for more precise network inference by improving the estimation resolution from the population level to the observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational, cross-sectional data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the proposed BN-LTE is uniquely identifiable under relatively mild assumptions. Through extensive experiments, we demonstrate the superior performance of BN-LTE in causal structure learning as well as inferring observation-specific gene regulatory networks from observational data.
Impact of ICT-Enabled Teaching–Learning Processes in Physical Sciences in Indian Higher Education in Light of COVID-19: A Comprehensive Overview

The COVID-19 (coronavirus disease) pandemic has caused a lot of damage to the social, economic and educational infrastructure of the world. The rapid change in the online learning environment calls for the development of effective learning strategies to support student learning. Principally information communication technology (ICT) emerged as a new hope in teaching-learning in streams like science and technology. Especially in the teaching-learning of a challenging subject like physics and its various streams, e.g. mechanics, wave, optics, etc., the use of ICT has increased exponentially due to its unique properties. However, some of its side effects have also been visible during this period. This study presents the feedback, experiences and recommendations of physics teachers regarding the application of ICT in the teaching-learning of physics. This article offers a comprehensive view of the impact of information and communication technology-enabled teaching-learning processes in physical sciences. To accomplish this study, an 18-question questionnaire was circulated among physics teachers across the nation and more than 100 physics teachers participated and recorded their responses. The outcomes of these responses were analysed, and corresponding conclusions with recommendations are presented. This study may be helpful for students, teachers, researchers and policymakers working in this field of ICT-enabled physics education.
Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

There has been a meteoric rise in the total amount of digital texts as a direct result of the proliferation of internet access. As a direct result of this, document clustering has evolved into a crucial method that must be used in order to successfully extract relevant information from big document collections. When employing the document clustering approach, documents are automatically sorted into groups whose members have a high degree of similarity to one another. These groups are created by applying the document clustering technique. Because they do not take into account the semantic linkages that exist between the texts, traditional clustering approaches are unable to provide an acceptable description of a collection of texts. This is because traditional clustering techniques. Document clusters, in which texts are ordered according to their meaning rather than their use of keywords, have been extensively utilized as a means of overcoming these challenges as a result of the incorporation of semantic information. This has been possible as a result of the fact that document clusters can group together related texts. In this investigation, we looked at a total of 27 distinct papers that were published over the previous five years and categorized the documents based on the semantic similarities that existed between the various pieces. A detailed literature evaluation is included to each and every one of the publications that were selected for further consideration. Comparative research is carried out on a wide variety of evaluation strategies, including as algorithms, similarity metrics, instruments, and processes. Following that, there is a drawn-out discussion that analyzes the similarities and differences between the activities.&#x0D; &#x0D;
Efficient DNN Backdoor Detection Guided by Static Weight Analysis

Despite the great progress of deep neural networks (DNNs), they are vulnerable to backdoor attacks. To detect and provide concrete proof for the existence of backdoors, existing techniques generally adopt the reverse engineering approach. However, most of them suffer from high computational complexity and weak scalability. In this paper, we make a key observation that the weights connected to the backdoor target labels in trojaned DNNs tend to have abnormal distributions, including dissimilarity to other labels and anomalously large magnitude. Based on this observation, we propose an efficient and scalable backdoor detection framework guided by static weight analysis. Our approach first detects the outlier existing in weight distributions and identifies suspicious backdoor target/victim label pairs. Then we conduct reverse engineering to recover the triggers, including a newly designed reverse engineering approach for global transformation attacks and one existing approach for local patch attacks. Finally, we analyze the characteristics of the recovered triggers to suppress false positives. Experimental results show that our approach has state-of-the-art performance on MNIST, CIFAR-10, ImageNet, and TrojAI. In particular, it outperforms NC, ABS, and K-Arm by 31%, 8.7%, and 5% on the public detection benchmark TrojAI in terms of detection accuracy while maintaining the highest efficiency.
Theories of Real Time Stream Processing Architecture Using Task Forking Model

Present study reports the theoretical aspects of big data stream processing architecture and it's performance metric identification, performance optimization and related experiments.The proposed architecture is considered as a complex directed graph model with various real time computational elements as nodes and big data tuples as edges, forming a real time topology.The notions of hard time deadline bound computation on streaming big data tuple along with minimum performance guarantee of processing every tuple have been introduced in the present research.Time bound computation issues in the real time stream computing architecture have been improved by optimizing time deadline management through task forking models.An algorithm for optimization of throughput has been reported and the performance metrics of the proposed system has also been identified for proper analysis on the basis of queuing theories by expressing it through appropriate Kendalls notation.Experimental results, at the end, report considerable improvement of performance of the architecture by applying optimization algorithms on a standard dataset.
The Risks of Remote Voting

Abstract: This paper discusses the risk of remote voting. Furthermore, it investigates claims that "voting via the Internet" or "voting on the blockchain" will enhance ballot safety and finds them insufficient and dishonest. While current election techniques are extremely reliable, Digital and blockchain based voting could dramatically increase the likelihood of undiscovered electoral mishaps
Density field measurement deflectometry for supersonic wind tunnels

A method for the quantitative measurement of two-dimensional density field distributions based on deflectometry is proposed. With this method, from the point of view of the inverse Hartmann test, the light rays emitted from the camera reach the screen after being disturbed by the shock-wave flow field. After the coordinates of the point source are obtained by using the phase information, the deflection angle of the light ray can be calculated, and then the distribution of the density field can be determined. The principle of density field measurement deflectometry (DFMD) is described in detail. In the experiment, the density fields in wedge-shaped models with three different wedge angles are measured in supersonic wind tunnels, the experimental results from the proposed method are compared with the theoretical results, and the measurement error is found to be around 2.76 × 10-3kg/m3. This method has the advantages of fast measurement, a simple device, and low cost. It provides a new approach, to the best of our knowledge, to measuring the density field of a shock-wave flow field.
Seismic Signal Denoising with Attention U-Net

Seismic stations record superpositions of the seismic signals generated by all kinds of seismic sources. In earthquake seismology, seismic noise sources can be natural events such as wind or anthropogenic events such as cars. In this study, we developed a machine learning (ML) based algorithm to remove the noise from earthquake data. This is important since the information related with the features of the seismic event may be overlapped by the noise. The presence of noise in the recordings can affect the performance of the seismic network, lowering its sensibility and increasing the magnitude of completeness of the seismic catalogue. To train ML model, 10000 thousand earthquake records with relatively low signal to noise ratio (SNR) are selected and contaminated by 25 noise records that are magnified up to 50% of peak amplitude of the earthquake signal and frequency content of those signals are stored as three component traces. The architecture used consists of an Attention U-Net, i.e. an encoder-decoder model using an attention gate within the skip connections: the encoder maps samples from input space (the waveform STFTs) to a latent space while the decoder maps the latent space to the output space (the signal-noise mask). Skip connections are introduced to recover, from previous layers, fine details lost in the encoding-decoding process. Attention gates identify salient regions and prune inputs to preserve only the ones relevant to the specific task. The use of attention gates in skip connections allows to pass "fine-detailed" information to high levels of the decoder that the model itself considers useful to the waveform segmentation. Trained model is tested with a new set of data to understand its capabilities. It is found that trained model can significantly improve the SNR of noise signals with respect to standard filtering methods. Hence, it can be considered as a strong candidate for seismic data filtering method.&amp;#160;