Reviewerly/exHarmony huggingface dataset:
Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Nowadays, China's sports industry has attained effective development, but the athlete's efficiency in the training process is too complex to have a scientific guarantee. Machine learning technology's help in guiding the sports training process has become a hot spot. In this work, we investigate the use of deep learning in real-time analysis of basketball sports data, utilizing research approaches such as scientific reporting, audio/video analysis, experimental research, and mathematical statistics. The suggested basketball stance action recognition and analysis system are made up of two pieces that are sequentially connected. The bottom-up stance estimate approach is utilized to locate the joint locations in the first segment, which is then used to extract the target's posture sequence from the video. The analyses are needed for a Support Vector Machine (SVM) algorithm based on the deep learning method of the space-time graph. The basketball activity of the set classification is recognized and extracted from the segmented stance sequence. The study used an auxiliary method, which is contrasted to standard training, in order to get higher accuracy and also correct player errors in a timely manner. The approach can help players rectify technical errors, develop muscle memory, and increase their abilities. The results revealed that the algorithm generated 97.7% accuracy in evaluating data from basketball training.
Blindly verifying partially unknown entanglement

Quantum entanglement has shown distinguished features beyond any classical state. Many methods have been presented to verify unknown entanglement with the complete information about the density matrices by quantum state tomography. In this work, we aim to identify unknown entanglement with only partial information of the state space. The witness consists of a generalized Greenberger-Horne-Zeilinger-like paradox expressed by Pauli observables, and a nonlinear entanglement witness expressed by density matrix elements. First, we verify unknown bipartite entanglement and study the robustness of entanglement witnesses against the white noise. Second, we generalize such verification to partially unknown multipartite entangled states, including the Greenberger-Horne-Zeilinger-type and W-type states. Third, we give a quantum-information application related to the quantum zero-knowledge proof. It further provides a useful method in blindly verifying universal quantum computation resources. These results may be interesting in entanglement theories, quantum communication, and quantum networks.
INCSA-UNET: Spatial Attention Inception UNET for Aerial Images Segmentation

Building segmentation from aerial images is essential in applications such as facilitating urban planning and estimating the population. Fully convolutional networks (FCNs) and especially UNET have achieved promising results in segmentation problems, after deep learning methods have significantly advanced the performance of many computer vision problems. However, in Convolutional Neural Networks (CNNs) with the standard convolution operations, there are problems such as the overfitting and precise extraction of the boundaries of the objects with different sizes and shapes. In this study, we have used Inception blocks with UNET to enhance feature extraction by implementing two-level Inception approach covering the entire encoding stage. In the proposed architecture, structured form of dropout (DropBlock) is used to prevent overfitting, and spatial/channel attention modules are applied to enhance important features by focusing key areas. We evaluate the proposed INCSA-UNET architecture on publicly available Massachusetts dataset and apply two fold cross-validation experiments for better analyzes. The experimental results show that the proposed architecture does not significantly increase the number of parameters of UNET and has a significant improvement in terms of F1 and Kappa quantitative measures.
Validity, Reliability, Intra-rater Instrument Parameter Teaching Factory and Learning Outcomes of Industrial Clothing

The research aims to review the quality instrument of teaching factory parameters and learning outcomes instruments on fashion industry competence, especially internal consistency (Cronbach's alpha), reliability of retesting (Pearson correlation), and construct validity of existing parameters.Respondents were school principals and teachers involved in teaching factory management in a vocational high school for the Fashion Design skill program that taught factory learning, namely 30 people, while the research sample for testing the learning outcomes instrument was 90 students.Data collection instruments used questionnaires and observation sheets.Data analysis techniques using.Data analysis using Pearson or Product Moment, Cronbach Alpha Intraclass Correlation Coefficients (ICC).The results showed that the teaching factory's Corrected Item Total Correlation parameter was positive and > 0.3, so it was declared valid.Cronbach's alpha value of all dimensions of the teaching factory parameter is greater than 0.6, so that it can be declared to have good reliability.The learning outcome instrument is valid because the Corrected Item Total Correlation is positive and > 0.3, and Cronbach's alpha value is greater than 0.6 so that the learning outcome variable is declared to have good reliability.The measurement of the level of agreement between raters shows an average agreement between raters of 0.832, while for one rater, it is 0.712.The results show the average agreement between raters is 0.894, while for one rater, it is 0.809.The research aims to review the quality instrument of teaching factory parameters and learning outcomes instruments on fashion industry competence, especially internal consistency (Cronbach's alpha), reliability of retesting (Pearson correlation), and construct validity of existing parameters.Respondents were school principals and teachers involved in teaching factory management in a vocational high school for the Fashion Design skill program that taught factory learning, namely 30 people, while the research sample for testing the learning outcomes instrument was 90 students.Data collection instruments used questionnaires and observation sheets.Data analysis techniques using.Data analysis using Pearson or Product Moment, Cronbach Alpha Intraclass Correlation Coefficients (ICC).
SISTEM PREDIKSI RASA BUAH JERUK MENGGUNAKAN METODE k-NEAREST NEIGHBOR

Every buyer always wants to get a good product, including in terms of buyers of citrus fruits, but many buyers do not know how to choose sweet oranges if they have not been tested first, so buyers usually experience disappointment when buying citrus fruits. The purpose of this study is to assist buyers in choosing citrus fruits, the k-NN method in identifying the taste of citrus fruits. The results of testing with the k-fold cross-validation method obtained a value of 96.55%, while testing with the holdout method obtained a value of 91.67%. The benefit of this research is that by using the application that was built, buyers can predict the taste of citrus fruits without having to taste them first.
Processing of the Conceptual Semantics of Verbs and Clauses in English Learners under the Background of Wireless Communication and Artificial Intelligence

The development of wireless communication and artificial intelligence decision-making has allowed our scientific research to be carried out on a more convenient and precise basis. This kind of all-round progress is also very obvious in the field of linguistic research. The research presented to explain the observation that people may use one of the alternate clauses to describe the picture they see. This linguistic observation leads us to the assumption that there may exist a functional cortex connecting visual information of an event with a group of corresponding clauses. Between the visual information and a group of clauses can be understood as a shared conceptual content of both. It is well supported by the PET and the ERP experiments. In other words, if a group of alternate clauses are connected with the same visual information, they are said to have the same conceptual content. In our linguistic model, this shared conceptual content is represented as a conceptual frame. In most linguistic studies, however, alternate syntactic structures are said to be derived from different meanings of the same verb via different argument structures. The difference between the conceptual frame and its corresponding argument structures needs more substantial evidence from brain imaging for the plausibility of conceptual frame. To answer this question, we attempt to investigate the relationship between a group of alternate clauses and two different pictures via priming effect manifested by an ERP component. Also, we make full use of wireless communication and artificial intelligence decision-making technology to construct. This technical background allows this research to fully simulate the practical environment while also making more assumptions about uncertainty. In the clause-picture priming experiment, clauses are treated as prime stimuli, designated as S1. The pictures are treated as target stimuli, designated as S2. One picture is conceptually congruent with all the clauses, and the other is not. The ERP component N270 is expected to be sensitive to the degree to which the S2 (the target stimulus) is in agreement with S1 (stimulus). After many simulation experiments, the experimental framework of wireless communication and artificial intelligence decision-making technology architecture provides a lot of reference data for research. The results of the experiment show that the semantics of each individual clause of an event was consistent with the semantics of its following congruent picture but inconsistent with that of the incongruent picture. Therefore, each clause of an event, though has different grammatical structures, shares the same semantics. This shared semantics is understood as the conceptual frame.
Design IoT-Based Blind Stick for Visually Disabled Persons

Within the current time, the bulk of open places like general stores, open gardens, shopping centers, college campuses, etc. are beneath video surveillance. There’s a necessity to supply basic security and monitor unordinary irregularity exercises in these regions. The disadvantage of the typical method is that it is necessary to undertake manual operations in a daily basis even though there might be results of human errors. This research concentrates on the detection of errors and the recognition of actions of humans using videos. Computer visioning can be transformed for many years now, and it is viewed as a major initiative for wide-range applications that were utilized in place of human supervision. We evaluate effective approaches for determining anomalies in videos. The applications of the convolution neural network, used recently, have promised the layers of convolution for object recognition and detection, mostly in image. Experimental findings on the errors in datasets indicate the superiority of the proposed approach in contrast to sophisticated frame levels and pixel levels in tasks for error detection.
Bibliometric Analysis of Publications of Universiti Teknologi Malaysia

This study analyses the scholarly publications of Universiti Teknologi Malaysia (UTM) in conjunction with 50 years of UTM establishment. A bibliometric analysis is performed to analyse the UTM’s publication and research trends over the 50 years, especially during the post-research university (RU) period, publications impacts, subject area expertise and publications performance in comparison to other RU. Based on the analysis, engineering and computer science are the most prominent fields among the research fields in UTM in terms of a number of publications and citations thus far. Among the top 10 fields in UTM, materials science, chemical engineering, energy and chemistry areas are seen to have a high potential to further excel in citation provided that the publication numbers of these fields are further increased. The publications output for some UTM fields can still be considered low despite having a reasonable number of authors and talents. UTM is the top university in the number of publications from 2015 till 2020 in four fields, namely engineering, computer science, materials science, and chemical engineering, compared to other RU.
TEACHER ACTION RESEARCH IDEAS

The study analyzes action research in educational practice and its contribution to the quality of education. The author describes the difference between action research and the random application of methods, tools, or non-standard procedures in a teacher's work and analyzes teachers 'views on action research, as well as teachers' attitudes to research.
Updating a dataset of labelled objects on raw video sequences with unique object IDs

We present an update to the previously published dataset known as SFU-HW-Objects-v1. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking, which was not possible using SFU-HW-Objects-v1.
Baseline Correction Algorithm Based on Catastrophe Point Detection and Lipschitz Exponent’s Analysis

In order to quantitatively analyze the proportions of independent components in mixtures, it is necessary to extract line spectra corresponding to those components from the spectrum signal of some mixture and evaluate the amplitude of the spectral lines. Multiple factors cause the drift and tilt of a spectrum signal’s baseline, such as environment noises, instrument bias, and sample size, which affect the identification and quantitative analysis of the line spectra superimposed on the baseline. Therefore, the baseline of a spectrum signal should be removed before the line spectra are identified. A baseline correction algorithm based on Catastrophe Point detection and Lipschitz exponent’s analysis is proposed in this paper. With the algorithm, the strong spectral lines are identified and removed, and then the spectral baseline is evaluated without the interference of strong spectrum signals. First, catastrophe points are located based on the local modulus maxima theory of wavelet coefficients. Second, according to the Lipschitz exponent theory, the strong spectral peaks’ regions are identified and removed by a smoothing filter. Then the slowly varying spectrum is segmented adaptively and fitted by the least square fitting method. After the segments are attached and the boundaries are smoothed, the baseline of the spectrum is acquired and extracted finally. The algorithm is more accurate than classical ones because identifying the baseline is implemented after strong peaks are removed, so their influences to baseline extracting are eliminated. The results of experiments show that the algorithm is accurately performed for the spectrum signal of a gas mixture, [Formula: see text].
It’s a Bird, It’s a Plane, It’s "That" UAV: RF Fingerprinting During Flight

UAVs are being rapidly deployed in many surveillance-related and monitoring applications worldwide. Thus, identifying a known UAV in a larger pool of devices is important. This task is often complicated in dense urban environments or low-level flight deployment cases where traditional radar-based detection becomes difficult. In this work, we describe deep convolutional neural network architectures and pre-processing steps suitable for capturing RF signals from in-flight UAVs for detection of the type of the UAV. Our objective is to leverage subtle discriminative features that may be embedded in the signal transmissions through RF fingerprinting for identifying (i) if the test signal comes from an unseen UAV (with respect to the training set), and (ii) if it is from a seen UAV, then we identify the label used during training time. Unlike static data collections, the mobile scenario is more challenging due to the rapid fluctuations in the wireless channel and Doppler effects which impair successful classification. We study the efficacy of our approach under different distances, flight/mobility patterns, interference conditions to emulate real-world situations with high fidelity. Our experimental dataset contains signals from seven different make/models, collected within an RF anechoic chamber.
Higher education in the post-pandemic world: prospects for revival and risks for oblivion

Contemporary educational discourse is marked by versatility and inconsistency of viewpoints on the post-pandemic future of the higher school. The most widespread are the two approaches to the comprehension of the prospects of its development tentatively outlined in the presented article as radically pessimistic and unjustifiably optimistic. The proponents of the first approach speak of the «demise» of the neoclassical model of university and emergence of the new digital format of lifelong learning based upon mastering the utilitarian knowledge by means of local educational courses; the proponents of the second approach are confident in the utility of the «natural selection» among the universities and further existence of the institution of higher education as a small number of technologically well-equipped and innovation-oriented educational establishments. «Veritas in medio est», - state those who criticize both approaches, implying a post-digital hybrid model of the higher school which would preserve a culture-generating Concept of the university and simultaneously would have fundamentally new institutional characteristics and funding schemes. Fragmentation of society and culture, revolutionary changes in the methods of production and information transfer, acquisition by social networks of a leading role in the formation of the social consciousness - all this combined with the diktat of the developed consumer society and consequences of the COVID-19 pandemic, which proved to be destructive to the world economy, demands a new educational paradigm free of illusions and unreasonable expectations. Whether the institution of the higher education would become an archaism and would depart into oblivion or it will be reborn in a variety of novel forms, including the hybrid ones, of a university «without walls» and time restrictions? The search for an answer to this question has become determinative for the authors of the presented research.
Variational image decomposition model TGV-Hilbert-BM3D for phase retrieval in FPP and its application for an on-site wall surface bulge

Mass loss from wall surface bulge deformation can be used to estimate the strength loss of reinforcement, bond reduction, and ductility degradation, so it is very important to accurately measure the three-dimensional (3D) shape of on-site wall surface bulge. In this paper, we try to solve the problem by use of fringe projection profilometry. In the fringe projection patterns of wall surface bulge, the contrast of the fringes is very weak, and there are sometimes cracks in patterns. We first present a preprocessing method to inpaint fringes if there are damaged fringes caused by cracks. Then we propose a new, to the best of our knowledge, image decomposition model, total generalized variation (TGV)-Hilbert-block-matching (BM)3D, to effectively extract the fringe component. Finally, we use Fourier transform, phase unwrapping, and carrier-removal methods to obtain the unwrapped phase. We test the proposed method on a simulated fringe projection pattern and two real fringe projection patterns of wall surface bulge. We compare our method with the advanced total variation space-generalized functions space-BM3D, TV-Hilbert- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> , and Beppo-Levi-space-Hilbert-BM3D methods. In addition, we perform ablation experiments to prove that our preprocessing method is necessary. The experimental results demonstrate that our method can effectively measure the 3D shape of wall surface bulge from a single fringe projection pattern for the first time, to our knowledge.
A Method of Domain Dictionary Construction for Electric Vehicles Disassembly

Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.
Design and Exploration of College English Reading, Writing, and Translation Teaching Classroom Based on Machine Learning

The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normalization optimization, and their optimal hyperparameters are analyzed. Consequently, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization, and the polynomial kernel-based SVM algorithm is proved to present the most remarkable improvement and accuracy after optimization, which is as high as 95.23% or 66.96% improvement. Therefore, the polynomial kernel-based SVM algorithm is chosen for the college English (R-W-T) classroom-oriented human pose recognition (HPR) system. Thus, English teachers can better grasp the students’ psychological state and classroom atmosphere and ameliorate unsatisfactory teacher-student interaction in students’ English (R-W-T). The proposal plays a positive role in cultivating college students’ strong (R-W-T) EPL, which is of great significance in improving the English classroom.