Reviewerly/exHarmony huggingface dataset:
The Effect of Educational Background on High Jobs and Income

In this era, people tend to assume that educational background is a string enough as consideration for opportunities, placements, and income of jobs. In fact, nowadays most companies also perceive that a good educational background will automatically bring out the maximum results for the company's performance. This refers to the assumption that the efficiency of workers is determined from the background itself, which also will boost the company's profit from human resources. However, to measure the level of influence of educational background on jobs and income, it needs a test that can map the percentage results accurately. Machine learning is an approach of AI (Artificial Intelligence) that is often used to help human problem solving or perform automation. Machine learning requires data to be studied, then it will classify the data based on how humans distinguish an object. Therefore, to complete the test above, it is necessary to use machine learning, namely KNIME. There are 3 algorithms of KNIME that can solve the above problems, which are Decision Tree, Logistic Regression, and Random Forest. Then, the highest level of algorithm accuracy is Random Forest, with a percentage of 84.9%. The accuracy result shows that the effect of educational background on employment and high income is 84.9%. So, it is true that the effect of educational background is very high. Instead, it is not only determined the high employment and income, yet it is still influenced by 25.1% other factors, such as productivity, work experience, etc.
Feature Selection Based on Multi Resolution Analysis of Text Documents for Effective Clustering

Users of digital era are overwhelmed with large volumes of text collections. Most of the text collections are without class labels. Clustering is the only feasible solution to extract valuable insights from the data. Clustering of text collections in high dimensional space is inefficient. Several unsupervised dimensionality reduction methods have been proposed in the literature. Feature selection methods are easy to interpret. Filter feature selection methods have been proved to be scalable and efficient for high dimensional datasets. The aim of this work is to propose an unsupervised univariate filter feature selection method for efficient clustering of very high dimensional text datasets in low dimensional feature space. Wavelets are mathematical functions that can transform a signal into space or time, frequency domain to analyze the signal at different resolutions of transformed domain. Wavelet transforms are efficient in identifying transients of a signal. Stationary wavelet transform using Symlet of order 2 is used to identify the most discriminant features of text documents for efficient clustering in low dimensional feature space. The proposed feature selection method is compared with nine other relevant methods by their quality of clustering solution on seven real text document collections. The proposed method has been able to identify the most discriminative features that have resulted in the best peak clustering performance better than clustering performance with all the features, on four out of seven datasets, with at most 1.5% of top-rated features.
Translation Alignment with Ugarit

Ugarit is a public web-based tool for manual annotation of parallel texts for generating word-level translation alignment. We aimed to develop a user-friendly interactive interface to visualize aligned texts and collect training data in the form of translation pairs to be used later, (i) for training an automatic translation alignment system for historical languages at the word/phrase level, (ii) as a gold standard to evaluate automatic alignment and machine translation systems. Ugarit is now widely used for learning new languages, especially historical languages, and as a reading environment for parallel texts. In the following sections, we present the related works and similar projects; then, we give an overview of the visualization techniques used to present the alignment results. Further, we explain how we could derive the translation graph from the aligned translation pairs. Finally, we discuss the usage limitations of Ugarit, possible improvements, and future development plans.
Integration of Higher and Vocational Education: Trends and Challenges

Today, integration of vocational and higher professional education, a prerequisite for the creation of a continuing education space, is gaining particular relevance. This creates a need for in-depth analysis of how different levels of education are interlinked in the context of the conjugation of universal and professional skills that shape the innovative model of a high school graduate, and a need to highlight the pros and cons of the merger of vocational and higher education. This study uses general research methods: analysis and synthesis, induction and deduction, as well as mathematical and statistical methods. The paper analyzes and evaluates the practices of Novosibirsk State University of Economics and Management that has integrated programs at different levels by restructuring its NSUEM Business College and embedding the vocational education programs in the continuing education model. The study of students’ educational paths from vocational education to bachelor and specialist degrees leads to a conclusion that the model has been a success based on statistical data, which indicate that people who graduate from vocational programs are likely to pursue higher education at NSUEM. Program content assessment proves the continuity of the effort to teach future specialists general cultural and professional skills. On the other hand, research also highlights several challenges that jeopardize the strengths of this integration model and call for its further elaboration and modification towards better learning process and greater professional competence of the graduates.
Problems of Awareness in Choosing of a Future Profession

The dynamic development of the economy sets new trends in the training of engineers of the future. The pool of competencies of the engineer of the future includes communication skills, the development of one’s own motivation for learning, and the ability to make independent decisions. The global community indicates that today’s graduates cannot be competitive in the market. Thus, a pit of vocational training for university graduates was formed. The authors carried out a study that reveals the problems of low motivation and lack of awareness of students about the independent possibility of developing competencies and the work of the future. It is shown that modern schoolchildren and students are not interested in attitudes towards self-education, are not informed about world trends, only some of them show awareness of their choice of profession. Constructing oneself for the needs of the market is also not a priority skill. More than half of the respondents go to university to obtain higher education as such, but not to acquire specific knowledge and skills. They are confident that just having a higher education will give them the opportunity to find a job after graduation. Only a small part of the students surveyed have a clear understanding of building their own line of development through additional education.
Motion Planning and Compliance Control Method for Direct Drive Vacuum Manipulator

In robotics, robot forward and inverse kinematics and dynamics are the foundation of robot research. Aiming at the special working environment and wafer falling off of mag8 vacuum manipulator produced by Shenyang SIASUN Robot Automation Co., Ltd., this paper completes the research on motion planning and compliance control of the manipulator. Firstly, its mechanical structure and motion mode are briefly introduced, which can realize the lifting, rotation and telescopic actions of both arms. Secondly, the robot kinematics model is established by MDH method, and the robot dynamics model is established by Lagrange method. Finally, experiments are carried out through MATLAB robot toolbox to verify the correctness of forward and inverse kinematics and dynamic model. The experimental results show that the dual arm model established in this paper effectively simulates the actual working state, which lays a foundation for further physical platform operation.
Unsupervised and few-shot parsing from pretrained language models

Pretrained language models are generally acknowledged to be able to encode syntax [46], [16], [13]. In this article, we propose UPOA, an Unsupervised constituent Parsing model that calculates an Out Association score solely based on the self-attention weight matrix learned in a pretrained language model as the syntactic distance for span segmentation. We further propose an enhanced version, UPIO, which exploits both inside association and outside association scores for estimating the likelihood of a span. Experiments with UPOA and UPIO disclose that the linear projection matrices for the query and key in the self-attention mechanism play an important role in parsing. We therefore extend the unsupervised models to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to learn better linear projection matrices for parsing. Experiments on the Penn Treebank demonstrate that our unsupervised parsing model UPIO achieves results comparable to the state of the art on short sentences (length <= 10). Our few-shot parsing model FPIO trained with only 20 annotated trees outperforms a previous few-shot parsing method trained with 50 annotated trees. Experiments on cross-lingual parsing show that both unsupervised and few-shot parsing methods are better than previous methods on most languages of SPMRL [39].
A Novel Approach for Detecting Facial Key Points Using Convolution Neural Networks

The task of face recognition is having many real-time applications in which the process of facial keypoint detection is considered to be an intermediate and crucial step. The amount of keypoints that are using for face recognition decides the computational requirements of the algorithm. In this paper, an effort has been made to detect the useful 15 facial key points using convolutional neural networks and compared with the state-of-the-art system with 30 facial key points. We made an effort to identify the 15 facial key points (6 points from eye +4 points from eyebrows +4 points from lips +1 point from the nose) by using the proper hyperparameters for convolutional neural network. It is found that the performance of the proposed system is quite similar when compared to the system with 30 facial key points.
Inferring case paradigms in Koalib with computational classifiers

Abstract The object case inflection in Koalib (Niger-Congo) represents complex patterns that involve phoneme position, syllable structure, and tonal pattern. Few attempts have been made with qualitative and quantitative approaches to identify the rules of the object case paradigms in Koalib. In the current study, information on phonemes, tones, and syllables are automatically extracted from a Koalib sample of 2,677 lexemes. The data is then fed to decision-tree-based classifiers to predict the object case paradigms and extract the interactive patterns between the variables. The results improve the predicting accuracy of existing studies and identify the case paradigms predicted by linguistic hypotheses. New case paradigms are also found by the computational classifiers and explained from a linguistic perspective. Our work demonstrates that the combination of linguistic theoretical knowledge with machine learning techniques can become one of the methodological approaches for linguistic analyses.
BERT Named Entity Recognition with Self-attention Mechanism

Named entity recognition, as one of the classic tasks of natural language processing, has experienced several different development stages since it was proposed. However, the current mainstream model for named entity recognition cannot well represent the polysemy of a word, and the recognition effect is still not ideal. Based on this, this paper constructs a BERT +BLSTM + CRF model to deal with the problem of Chinese named entity recognition. By using the emerging pre training model Bert, we obtain the dynamic word vector, and then input it into BLSTM+CRF model for further training. Experiments show that the BERT+BLSTM+CRF model constructed in this paper is significantly improved in terms of accuracy, recall rate and F1 value compared with other models used to deal with named entity recognition tasks, which indicates the effectiveness of BERT pre-training model in solving named entity recognition problems.
Learning Language and Speaker Information for Code-Switch Speech Synthesis with Limited Data

End-to-end speech synthesis demonstrates remarkable performance in monolingual speech, whereas code-switching (CS) speech synthesis remains a challenge owing to the sparsity of data and diverse syntactic structures across languages. Previous studies show that large mixed-lingual corpora are essential for effective learning text/language representations and target speaker information. In this study, we propose a method using three independent encoders (text, language, and speaker), which requires only a small amount of mixed-lingual data to realize the CS speech synthesis of Mandarin and English. Additionally, to distinguish between Mandarin and English, we investigate two text-representation methods: (1) the implicit method, which uses Pinyin and the CMU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> http://www.speech.cs.cmu.edu/cgi-bin/cmudict dictionary to represent both languages; and (2) the explicit method, which uses language markers i.e., masks, to differentiate the languages. Through our proposed method, we can improve synthesized speech in terms of quality and speaker similarity using a small amount of mixed-lingual data. In addition, the experimental results demonstrate that the proposed method achieves performance improvement of 0.06 in terms of the mean opinion score and absolute improvement of 0.64% in terms of the character error rate compared to the baseline method.
Proxy Model Explanations for Time Series RNNs

While machine learning models can produce accurate predictions of complex real-world phenomena, domain experts may be unwilling to trust such a prediction without an explanation of the model’s behavior. This concern has motivated widespread research and produced many methods for interpreting black-box models. Many such methods explain predictions one-by-one, which can be slow and inconsistent across a large dataset, and ill-suited for time series applications. We introduce a proxy model approach that is fast to train, faithful to the original model, and globally consistent in its explanations. We compare our approach to several previous methods and find both that methods disagree with one another and that our approach improves over existing methods in an application to political event forecasting.
Hedging Strategies in Research Articles: A Comparative Analysis of Indonesian Male and Female English Students

Hedge is claimed as linguistic of full commitment or precision commonly used in academic writing to give the appropriate uncertainty, proper strength of claimed data, politeness, and precise presentation in providing and presenting literary works i.e. research articles. This study aimed at revealing the tendency and preferences of Indonesian English Students in applying hedging strategies, and frequencies of hedges use, and if there were any differences between males and females in using hedges in research articles. Data corpus was composed of 40 articles written in English by male and female postgraduate students majoring English Education. The output was categorized which was referred to a combined framework proposed by Holmes (1988), Hyland (1998), Hyland and Milton (1997), Vartala (2001), and Crompton (1997) covering 9 types of hedges. The statistical result showed that although Indonesian Male English Students (IEMSs) employed Hedges in their research articles more frequently than Indonesian Female English Students (IEFSs) did, MANOVA proved that there was no significant effect of gender on the use of hedges in research articles written by Indonesian English Students. Nevertheless, ANOVA result confirmed significant main effects of gender on the use of some types of hedge (Adverb of Frequency, Quantifiers, Epistemic Lexical Verbs and Adjective&amp; Adverb Informal).
Improving Performance of Sparse Autoencoder by Using DPHOG for Gender Classification

This paper proposes the improvement performance of Sparse Auto Encoder (SAE) by using Dominant Patterns of Histograms of Oriented Gradients (DPHOG). The SAE has a simple structure and fast computation. However, the SAE demonstrates less accuracy than the Convolution of Neuron Networks (CNN). This proposed method selects the dominant features of female and male face images, and then encoding and decoding these features within SAE. The researchers conducted DPHOG with SAE in two datasets and compared them with SAE, HOG with SAE, and CNN. Experimental results showed that DPHOG with SAE produced the highest performance in terms of accuracy, true positive rate, false positive rate and time computation in the two datasets. In addition, the DPHOG with SAE can extract learning features the form small dataset. In contrast, CNN requires a huge dataset for learning features.
Locative media communities, social media and cultures of enthusiasm

Locative media art has been a popular topic to study, yet the focus tends to be on examining the artworks themselves, which have challenged us to think about the complex interrelations of space, place and time in novel ways. In this paper we offer a different view of locative media art by examining the community practices that can emerge through the production of this work. By highlighting how the social media platform, WhatsApp, facilitated communication and arts practice during a recent summer school on the theme of locative media, we demonstrate how cultures of enthusiasm can emerge from the adjacent digital spaces of locative media art. Our reflections highlight the ways that enthusiasm for locative media art and towards other participants was produced through WhatsApp spaces, through both enthusiastic language practices and atmospheres of enthusiasm. Ultimately, we aim to reveal that locative media is not only a technology that is used to shape our relationship to space, place and time, but that it is also a technology which encourages a set of community driven practices. In doing so we contribute to the literature on locative media art by giving attention to the communities of practice that form around it.
Capsule Network based Super Resolution Method for Medical Image Enhancement

The Convolution Neural Network (CNN) based approaches for Super-Resolution (SR) show several inherited flaws. In this paper, several Capsule Network routing mechanisms have been investigated for the current Super Resolution (SR) pipeline with a custom medical image dataset. Previously introduced, Dynamic Routing (DR) and Expectation Maximization (EM) Routing Capsule Networks are re-configured to adapt to the custom dataset. For the first time, the Inverted Dot Product-based Attention Routing mechanism is introduced for the SR. The results show that the DR-based method outperforms the other methods along with different image quality indexes. The introduced Attention Routing based approach shows better results than the popular Fast CNN for SR (FSRCNN) method and improved time performance than the DR-based method.
Nine-axis Posture Sensor-based Fall Detection and Alarm System

With aging, the study of fall detection for elderly people and patients with leg injuries has become an important topic. In the laboratory, the researchers often use acceleration as an indicator for determining a fall, but in practice, the results from a single indicator are not promising. Therefore. we use a nine-axis posture sensor to settle the human posture judged by multi-indicators such as three-axis acceleration, angle, and gyroscope, by using a newly designed threshold judgment algorithm to warn the user through the alarm device to achieve timely assistance.
Alphabetic Image Recognition Based on Multi-channel Diffractive Deep Neural Network

Machine learning has made remarkable achievements in image classification in recent years. It has also inspired the emergence of optical machine learning frameworks as an effective combination of optics and artificial intelligence. Diffractive deep neural network (D2NN) is a recently proposed optical neural network framework, which is designed and trained by computers, and then fabricated by the usage of 3D printing technology. It can recognize handwritten digital dataset and fashion product dataset with only light source, and the recognition effect is comparable to that of electronic neural network. We propose a diffractive light neural network structure is improved on the basis of the traditional diffractive deep neural network (D2NN), providing a multi-channel design model, while the distribution of neuron nodes on the network layer is adjusted in such a way that different network layers correspond to incoming light waves of different frequencies. The neuron nodes on the network layer are arranged in a concentric circle pattern to reduce the parameters generated during network training, and in the output layer we get channels of different frequencies and then merge these channels to obtain the final results. It is experimentally verified that the diffractive optical neural network with multiple channels has a large improvement of 95.44&#x0025; recognition accuracy compared to the 89.92&#x0025; recognition accuracy of the traditional D2NN for recognition of 10 different alphabetic image datasets. The proposed network improves the recognition accuracy by increasing the number of channels while reducing the parameters, which provides a guidance to design optical networks with reduced cost and improved efficiency.
Experimental Analysis of Deep Learning-Based Modulation Classification in Indoor Environments

In this paper, we detail a Deep Learning (DL) based RF signal classification in an indoor environment. Software Defined Radio (SDR) based transmitter and receiver units are used for generating and processing the dataset consists of BPSK, QPSK, GMSK, 16-QAM, 64-QAM, GFSK, and CPFSK waveforms at different positions inside a building layout. We analyze the classification accuracy of individual modulation types at different receiver positions with two different transmitter positions. It is seen that for a given transmitter position, some of the modulation types perform better than the others at different receiver positions. It is also seen that the number of locations where the classification accuracy is greater than a certain threshold value varies with the transmitter positions. The results suggest that DL-based models taking into account the transmitter and receiver positions can be used for efficiently designing cognitive radio-based communication systems for a given propagation environment in indoor scenarios.
Experiential learning practices in the secondary education of nepal

This paper starts with the introduction of experiential learning in general, as seen in all fields of research and practice, before concentrating on experiential learning and its effects on education of children. This document provides examples and advocates for the use of experience learning in Nepalese schools and throughout the world in the current situation.