Reviewerly/exHarmony huggingface dataset:
Application of Digital Media Technology for Teaching in Higher Vocational Colleges Using Big Data

The utilization and accessibility of big data and digital media technology by common people have decreased technological prices. Professional education has been pushed to modernize curricula and boost talent development efficiency. The vocational education curriculum system must take into account the usage of new technologies in a comprehensive manner to ensure the availability of knowledge and ability structure. To accomplish the goal, the application techniques and approaches of teaching and practice utilizing big data and digital media technologies in vocational education are explored and researched. To improve the teaching quality of higher vocational education, with the help of big data, this paper aims to conduct an in-depth study on the application of digital media technology in higher vocational education. Firstly, the teaching management system of higher vocational education is constructed. Secondly, the structure, efficient units, architecture, and database of the teaching management system are pronounced. Thirdly, fuzziness mining and teaching data scheduling are used to optimize and regulate the storage structure. According to the multiparameter optimization method, the teaching feature screening and resource scheduling are completed improving the capability of parameter recognition and online identification related to teaching resources. The simulation results show that the method proposed in this paper can make the teaching methods of higher vocational education more diverse. It is anticipated that it can help the reform process and growth of vocational education, as well as the development of innovative and entrepreneurial skills. As the data receiving rate of teaching resources is high, therefore, the ability of teaching resource scheduling is also strong along with a high application value.
A Smart and Intelligent Alcohol Detection System for Corporate Organization

In today's world, we find that life is becoming increasingly busy and hectic with each passing day, and as a result, employees of any corporate organization work extremely hard to meet project deadlines. Furthermore, as evidenced by numerous cases from the corporate sector, some of them used to drink after work and before work. So the issue is how to keep track of these activities in the office. To address such flaws in the system, a proposal has been made to detect people who come to work while inebriated. There will be no additional setup required in the office, according to the proposal. At each entry gate where each employee must punch before entering, a small alcohol sensor is all that is required. The alcohol sensor will detect each person's alcohol sensitivity and send the data to the server storage, where the database developers will perform the ETL process on the data and save it in the form of OLAP cubes, which will help in the future in generating reports with multidimensional data from which the admin and HR will get the record of each employee through application. In this way, the company can keep a hold on the employee, which will improve the company's rating and market growth.
Saliency Detection Using a Bio-inspired Spiking Neural Network Driven by Local and Global Saliency

The detection of the most salient parts of images as objects in salient object detection tasks mimics human behavior, which is useful for a variety of computer vision applications. In this paper, the Local and Global Saliency Driven Dual-Channel Pulse Coupled Neural Network (LGSD-DCPCNN) model is used to provide a novel strategy for saliency detection. To achieve visually homogeneous sections and save computation costs, the input image is first subjected to superpixel segmentation. The global and local saliency maps are then created using the segmented image’s position, color, and textural properties. The LGSD-DCPCNN network is activated using these saliency maps to extract visually consistent features from the input maps to provide the final saliency map. An extensive qualitative and quantitative performance study is undertaken to assess the efficacy of the proposed method. When compared to state-of-the-art approaches, the experimental results show a considerable improvement in the detection of salient regions. Quantitative analysis of the proposed method reveals a significant improvement in the area under the ROC curve (AUC) score, F-measure score, and mean absolute error (MAE) score. The qualitative analysis describes the proposed algorithm’s ability to detect multiple salient objects accurately while maintaining significant border preservation.
Organization of internal expertise of academic disciplines work programs

The article discusses the expertise of work programs of academic disciplines of the main professional educational programs as a condition for improving the quality of training of graduates of an educational institution of higher education. The organization of internal expertise of work programs of academic disciplines by members of the expert community of the university’s department teachers is considered. The phased content of the activities of such a community, as well as the leading forms of teacher’s work are disclosed.
Predicting Breast Cancer Using Changing Parameters of Machine Learning Model

Breast cancer is the most common type of fatal ailment seen in females in the world. There are many types of cancer, one of which is breast cancer. Several types of breast cancer are found in women that affecting their lives across the world. Several types include “lobular carcinoma in situ (LCIS), ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC).” How many people are dying due to cancer today, and one of the main reasons is not known in time. Generally, breast cancer may be a malignant neoplasm that begins within the cell of the breast and eventually spreads to the encompassing tissue. Due to breast cancer, a lot of death is happening. The death rate can be reduced by using machine learning techniques. Mammography is a good and effective modality that is used in the detection of breast cancer in today’s time. In this paper, we used different machine learning algorithms like Naïve Bayes, k-nearest neighbors, logistic regression, support vector machine, decision tree, and convolution neural network. After changing the unique hyperparameter of each model, find the better accuracy within the model and also do the comparison between models. The performance of convolution neural network is found maximum accuracy with minimum loss. The accuracy achieved by convolution neural network is 99.05%.
Multiview Actionable Knowledge Graph Generation From Wikipedia Biographies

<i>Actionable knowledge graph</i> (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization&#x2013;based frameworks for AKG generation from three different perspectives: <i>subject</i>, <i>context</i> and <i>functionality</i> of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs.
Cyber Security Event Sentence Detection From News Articles Based on Trigger and Argument

Events are critical for comprehending the things that occur in the actual world. The term “events” is frequently used to describe the numerous relationships between people, places, activities, and things. Events-centered modelling entails the representation of several facets of an event in addition to the semantic representation of event facts. Detecting cybersecurity occurrences is important to keep us aware of the rapidly increasing number of such incidents reported via text. The authors focus on cyber security event detection task in this study, specifically on identifying event trigger words and arguments in the cybersecurity area. For this study, they use the CASIE dataset. They propose a system that involves the events identification, event triggers identification, and event arguments extraction. In this section, they divide the cyber security event sentence classification model into two steps: event trigger and argument identification, and cyber security event sentence classification using the training corpus.
Generative Adversarial Network-Based Satellite Image Enhancement

In recent times, satellite images are gaining popularity in the monitoring of agricultural fields, forest ranges, surveillance, and public commute convenience. In many places, it is difficult to implant cameras; thus in that scenario, satellite images establish to be very helpful. However, the raw image taken by the satellite needs to be preprocessed to remove cloud cover, improve contrast, perform color correction, and improve inter-image contrast. In this paper, a method is devised to remove cloud cover and improve inter-image contrast. Since deep learning is gaining immense popularity, a deep learning approach utilizing generative adversarial networks (GANs) has been used for the enhancement of satellite images. To improve inter-image contrast, histogram matching technique has been used. The technique proposed in this paper has been investigated by considering the satellite images collected from SKYMAP Global.
Cascaded DNNs for detecting the position and orientation of Left Ventricle from 3D CT Scans

Accurate and automated localization of the left ventricle (LV) of heart and identification of its’ orientation are crucial for effective diagnosis of various heart diseases. This work presents a fully automated deep learning based pipeline for generating a short-axis transformation of transversal 3D CT scans. The proposed novel solution consists of three independent convolutional neural networks (CNN) in cascaded sequential phases. In phase one, a binary classification network is used to determine a suitable 2D slice that clearly shows the left ventricle from the 3D image stack. In phase two, a different CNN is used to predict the location of 3 landmarks on the suitable 2D slice. From these landmarks a general orientation of the left ventricle is computed in the xy plane. In the last phase, a third CNN is used to predict the location of 3 landmarks on the orthogonal image. With all 6 points, a 3D orientation of the heart can be calculated. The proposed method performs as good as human accuracy, considering inter-observer variability, and performed equally well in terms of End-Diastolic Volume (EDV) and Left Ventricular Mass (LVM) calculation.
A High-Efficiency Knowledge Distillation Image Caption Technology

Abstract Image caption is wildly considered in the application of machine learning. Its purpose is describing one given picture into text accurately. Currently, it uses the Encoder-Decoder architecture from deep learning. To further increase the semantic transmitted after distillation by feature representation, this paper proposes a knowledge distillation framework to increase the results of the teacher section, extracting features by different semantic levels from different fields of view, and the loss function adopts the method of label normalization. Handle unmatched image-sentence pairs. In order to achieve the purpose of a more efficient process. Experimental results prove that this knowledge distillation architecture can strengthen the semantic information transmitted after distillation in the feature representation, achieve a more efficient training model on less data, and obtain a higher accuracy rate.
Regulation of Assessment of General Education Quality in the Regulatory Activities of Governing Bodies

The author shows that the use of adaptive and heuristic methods for assessing the quality of general education affects the planning aspect of the strategic activities of government. This assessment will make it possible to adjust plans and strategies for the activities of government bodies, and will increase the importance of the general education system.
Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM

The goal of Chinese fine-grained emotion analysis is to identify the target words corresponding to fine-grained elements from sentences and determine the corresponding emotional polarity for the target words. Aiming at the problem that the current Sina Microblog user emotion analysis methods have low accuracy and are difficult to effectively predict and manage, a Sina Microblog user emotion analysis method based on the Bidirectional Long Short-Term Memory algorithm (BiLSTM) and improved hierarchical attention mechanism is proposed. Firstly, an emotion analysis model is constructed based on text-level analysis and subjective and objective analysis, and the dimensionality curse problem of one-hot representation is solved by integrating the weighted word vector of TF-IDF. Then, by constructing a bidirectional long short-term memory neural network, the full acquisition of context information is realized, which increases the fine-grained elements of emotion analysis. Finally, by introducing an improved hierarchical attention mechanism, the network model can focus on different parts to achieve text classification and emotion analysis. Through simulation experiments, the proposed emotion analysis method and the other two methods are compared and analyzed under the condition of using the same database. The results show that the precision, recall, and F1 value of the method proposed in this paper are the best under 7 different emotion classifications, with the highest reaching 95.8%, 95.9%, and 96.1%, respectively, and the algorithm performance is better than the other two comparisons algorithm. It is proved that the proposed model has excellent performance.
The Influence of E-Scaffolding Sources in a Mobile Learning Environment on Students’ Design Skills and the Technology Fatigue Associated with a 3D Virtual Environment

This article examines the influence of teacher/peer e-scaffolding in a mobile learning environment on students’ design skills and the technology fatigue associated with a 3D virtual learning environment. The sample consisted of 32 postgraduate students who were divided into two groups according to their e-scaffolding source (teacher/peer). The findings showed that peer e-scaffolding outperformed teacher e-scaffolding in the mobile learning environment in terms of students’ development of design skills. The findings also showed that students became moderately fatigued after designing the 3D virtual environments in both experimental groups. This study can act as a guide for teachers and instructional designers by helping them to select the most suitable e-scaffolding source when teaching 3D virtual environment design skills. This may result in better and easier skill development.
What Is It They Want?

The pandemic caused by the SARS-CoV-2 virus made it necessary for schools around the world to adopt emergency remote teaching (ERT) practices; many schools opted to move to online classes. This chapter reports on student perceptions of ERT at a Mexican university. Data were collected through focus groups with 106 undergraduate students and analyzed qualitatively using thematic analysis. Themes that were identified include negative aspects such as barriers to learning and incomplete university experience, but also positive aspects such as convenience of online learning and technological affordances of online learning. Findings show that synchronous online classes helped the participants feel more connected during an emotionally fraught time.
Proportional Myoelectric Control in a Virtual Reality Environment

Translating input modalities such as hand interactions, speech, and eye tracking in virtual reality offers an immersive user experience. Especially, it is crucial to track the user’s hand gestures, since they can help in translating user intentions into actions in virtual environments. In this work, we developed a virtual reality application which incorporates electromyography-based deep learning methods for recognizing and estimating hand movements in an online fashion. Our application automates all user controls, providing an immense potential for rehabilitation purposes.
Performing activity as a means of developing professional thinking of an instrumentalist

We consider the process of conceptualizing the intellectual and emotional expression of the artistic meaning of the work and the technical means of performing technique adequate to this meaning. The method of transferring knowledge, skills and abilities from a teacher to a student is interpreted as pedagogical communication, requiring an appeal to the language resource of instrumental activity, which provides options for using a particular language unit in the transmission of a musician's speech-thinking attitudes. We analyze the concepts of “compositional means of intonation”, “musical thinking”, “language of a musician’s performing activity”, “speech expression”, “literary text meaning”. The key condition for the realization of the idea is the artistic performing technique, which has its own form and content, immanent and emancipated from the author’s text; it is for this reason that the performing technique forms an independent object of the theory of performance. Performing technique in all parts of its structure and at all stages of formation requires active creative thinking, which, in turn, is dialogical in nature and is based on a developed skill in the language of music, as well as the skill of musical speech. The process of mastering artistic technique is a teaching performing activity based on the analysis of the instrumentalist's language and speech. The revealed best traditions of Russian piano pedagogy allow us to consider musical and performing activity as a creative process, and this process is possible only with a pianist’s participation with an artistic technique, through which they expresses cultural meanings corresponding to the interpretative plan.
High-Frequency Normalizing Flow for Image Rescaling

It is desirable to develop efficient image rescaling methods to transmit digital images with different resolutions between devices and assure visual quality. In image downscaling, the inevitable loss of high-frequency information makes the reverse upscaling highly ill-posed. Recent approaches focus on joint learning of image downscaling and upscaling (e.g., rescaling). However, existing methods still fail to recover satisfactory high-frequency signals when upscaling. To solve it, we propose high-frequency flow (HfFlow), which learns the distribution of high-frequency signals during rescaling. HfFlow is an overall invertible framework with a conditional flow on the high-frequency space to compensate for the information lost during downscaling. To facilitate finding the optimal upscaling solution, we introduce a reference low-resolution (LR) manifold and propose a cross-entropy Gaussian loss (CGloss) to force the downscaled manifold closer to the reference LR manifold and simultaneously fulfill recovering missing details. HfFlow can be generalized to other scale transformation tasks such as image colorization with its excellent rescaling capacity. Qualitative and quantitative experimental evaluations demonstrate that HfFlow restores rich high-frequency details and outperforms state-of-the-art rescaling methods in PSNR, SSIM, and perceptual quality metrics.
Study on Basic Chemistry Class Using Flipped Learning in Non-Face-to-Face Environments－Focusing on Differentiated Collaborative Learning

This study is a case report on the design and operation of classes using flipped learning using level-specific collaborative learning in general chemistry subjects operated in a non-face-to-face online environment.&lt;br/&gt; The pre-learning diagnosis and result analysis used as a teaching strategy for this purpose were used in the adaptive teaching process organized to fill the students' learning needs and to increase their motivation to participate. In addition, clustering by learning level contributed to meaningful learning through balanced student to student interaction in small group activities.&lt;br/&gt; As a result of running the class, there was an improvement in academic performance of the students. There was a positive effect on their interest in learning, their understanding of the concepts, and their maintenance of the leanring process. Also, there was a high level of satisfaction in the area of interaction-related class activities overall. In the comparison of the upper and lower groups on the results of class operations, we found that the upper group had a significant effect on the group activities centered on the interaction between the students and that the lower group had a significant effect on learning achievement and maintaining learning progress.&lt;br/&gt; Finally, our study will contribute to the development and operation of basic science classes for the enhancement of interactions in non-face-to-face environments, as well as in the face-to-face teaching environment. It is believed that this will contribute to a more effective operation of the curriculum.
Learning "Best" Kernels from Data in Gaussian Process Regression. With Application to Aerodynamics

This paper introduces algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. We adopt the setting of kernel method solutions in ad hoc functional spaces, namely Reproducing Kernel Hilbert Spaces (RKHS), to solve the problem of approximating a regular target function given observations of it, i.e. supervised learning. A first class of algorithms is kernel flow, which was introduced in a context of classification in machine learning. It can be seen as a cross-validation procedure whereby a "best" kernel is selected such that the loss of accuracy incurred by removing some part of the dataset (typically half of it) is minimized. A second class of algorithms is called spectral kernel ridge regression, and aims at selecting a "best" kernel such that the norm of the function to be approximated is minimal in the associated RKHS. Within Mercer's theorem framework, we obtain an explicit construction of that "best" kernel in terms of the main features of the target function. Both approaches of learning kernels from data are illustrated by numerical examples on synthetic test functions, and on a classical test case in turbulence modeling validation for transonic flows about a two-dimensional airfoil.
BiasHash: A Bayesian Hashing Framework for Image Retrieval

Hashing methods have been widely used for similarity search in multimedia due to its low memory requirements and efficient and scalable search. Recently, an approach called Semantic Preserving Hashing (SePH) has been proposed, which uses the semantic probabilities of training data, approximates them with the learnt hash codes and then uses the kernel logistic regression for learning the projection of features to the learnt hash codes. In this paper, we extend this method using a Bayesian framework to learn these projection functions, motivated by the probability distribution that visual features tend to approximate. The proposed Bayesian ridge-based Semantic Preserving Hashing (BiasHash) approach is shown to outperform seven state-of-the-art methods on three benchmark datasets.