Reviewerly/exHarmony huggingface dataset:
Psychological and pedagogical training of teachers of engineering universities in the process of continuing professional education

The article deals with topical issues of professional psychological and pedagogical training of educators of engineering universities; proposed a set of criteria for assessing the scientific and educational activities of teachers of technical disciplines; describes the achievements of domestic and international practice of training educators of technical universities; identified areas for improving the efficiency of this process. It is determined that the development of the psychological and pedagogical professionalism of university teachers is a priority task of the system of continuing professional education of the teaching staff. The authors reveal the essence of the concept of «pedagogical professionalism of a technical university educator», which is considered as an integrative property of a person, uniting all scientific and educational activities, acting as a complex of psychological and pedagogical competencies expressed in his or her ability and readiness to effectively solve problems of professional activity. At the same time, the main components of this category stand out: the identity of the educator as a carrier of culture and socio-historical experience and the educator as a researcher and teacher of scientific knowledge for students.
Discoursing children characteristics of Zenith bank®, Nigeria, advertising: an expression of clause as representation

This study illuminated the strategy of Zenith Bank®, employing characteristics of children as fascinating instruments to persuade parents-cum-guardians to open a ZECA account for their children.Mrs. Bonke and the author randomly collected the advertisements for one year and nine months with a camera, utilizing a laptop to adjust the advertising frameworks.The author stratified the advertisements, enabling nine advertisements, entwined with features of children, for appropriate analysis.The application of Systemic Functional Linguistics' 'Clause as Representation' facilitated the calibration of the recurrences of the clause processes and circumstantial devices.Nevertheless, tables and graphs explicated the frequency and flow of the grammatical components.The study revealed that Material (holds, give and starts), Mental (matters to, smiles and experience), and Behavioral (speaks and mimic) processes dominated the texts; whereas Location (in your best interest, today and away from me) and Cause (for the first time and of building towards a brighter future) are major enhancing informative mechanisms.The verbal modes and non-verbal modes exhibited general facilities and specific elements in the communications.Some of the universal aspects are open a ZECA account, special memories, the grey background and the Zenith Bank logo.The Hold, The Smile, The Step and The Mimic are parts of the definite building blocks of the advertisements, which appeared in the form of maternal affiliation, paternal partnership, and the child's image.The investigation further demonstrated these modes in connotative figures.Among others, the images of hands in Plates 1 and 9 reflected strong ties among Zenith Bank's stakeholders.Owing to the innovativeness of these advertisements, one might call for the production of advertisements in marital domains because such constructs might engender deep love in family affairs.
Random forest ensemble using a weight-adjusted voting algorithm

A classification ensemble is a learning method that aggregates different classifiers to obtain more accurate class predictions. Among many developed methods, Random Forest is known as one of the most accurate ensemble methods. It combines many randomized decision trees using simple majority voting scheme. Wave, a weighted voting algorithm, has proven to outperform simple majority voting when combined with bagging. In this paper, we investigated whether random forest using the wave voting scheme can further improve classification accuracy. Experiments show that the larger the ensemble size, the more accurate it is than other methods including single tree, bagging, AdaBoost, random forest (simple majority voting). The result also shows that random forest with wave is more accurate than bagging with wave when the ensemble size is large enough.
Knowledge Guided Metric Learning for Few-Shot Text Classification

The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfactory performance. However, human beings can distinguish new categories very efficiently with few examples. This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate relation network parameters. Metrics can be transferred among tasks when equipped with these generated parameters, so that similar tasks use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the state-of-the-art few-shot text classification models.
Balancing exploration and exploitation: An image-based approach to item retrieval with enhanced diversity

We propose a machine learning approach based on relevance feedback to visually retrieve e-commerce items, represented by images, from huge catalogs. A threefold strategy is presented to deal with the exploration–exploitation dilemma: retrieve user-relevant items; retrieve items that do not comply with user learned preferences, to avoid convergence to local minima; and retrieve items from uncertainty regions, to improve the learning curve, all in a stochastic manner. To mitigate the cold start problem, we present a novel sampling method to improve the diversity of retrieved items that employs a combination of a multidimensional projection method with an adaptive spatial data structure. We performed quantitative and qualitative user experiments using an annotated catalog of clothing items. Results revealed that our approach can rapidly improve the retrieval of appropriate items in a few user iterations while providing higher diversity than other relevant and recent approaches.
The Research on the Construction of Primary Mathematics Corpus Based on MATTER Cycle Method

Mathematics is an important subject which is the basis of all sciences, especially primary maths is the most basic and practical.It not only helps students to grow up in the future, but also cultivates students' psychological quality and mental flexibility.The study of mathematical language in primary schools can helps to improve the quality of teaching and deepens students' understanding and application of maths.In view of the computer lack of corpus support in primary mathematics problem solving, this paper analyzes the exam questions, review questions and the relevant knowledge, and constructs the primary mathematics corpus by using MATTER cycle method.This research not only provides support for machine solving, but also provides material for the related research of primary maths knowledge.
A cross-lingual sentiment topic model evolution over time

Sentiment analysis in various languages has been a hot research topic with several applications. Most of the existing models have been reported to work well with widely used language. Were the lass directly applying these models to poor-quality corpora often leads to low results. Thus, to deal with these shortcoming we propose a cross-lingual sentiment topic model evolution over time (CLSTOT) which jointly models time with topic and sentiment. In CLSTOT, we consider the mapping between sentiment-aware topics under different cultures and analyze their evolution over time. The topic-specific sentiment is extracted using the entire data and not for each single document. As long as providing sentiment-topic, we can predict the timestamps for each test document by finding its most likely location over the timeline. This is achieved by using inference algorithm which is based on Gibbs Sampling. The experimental results on Chinese and English newsreader dataset; Chinese from SinaNews2, and English from Yahoo1, show that CLSTOT achieves significant improvement over the state-of-the-art.
Trainability of ReLU networks and Data-dependent Initialization

In this paper, we study the trainability of rectified linear unit (ReLU) networks. A ReLU neuron is said to be dead if it only outputs a constant for any input. Two death states of neurons are introduced; tentative and permanent death. A network is then said to be trainable if the number of permanently dead neurons is sufficiently small for a learning task. We refer to the probability of a network being trainable as trainability. We show that a network being trainable is a necessary condition for successful training and the trainability serves as an upper bound of successful training rates. In order to quantify the trainability, we study the probability distribution of the number of active neurons at the initialization. In many applications, over-specified or over-parameterized neural networks are successfully employed and shown to be trained effectively. With the notion of trainability, we show that over-parameterization is both a necessary and a sufficient condition for minimizing the training loss. Furthermore, we propose a data-dependent initialization method in the over-parameterized setting. Numerical examples are provided to demonstrate the effectiveness of the method and our theoretical findings.
Joint Attention Estimator

Joint attention has been identified as a critical component of successful human machine teams. Teaching robots to develop awareness of human cues is an important first step towards attaining and maintaining joint attention. We present a joint attention estimator that creates many possible candidates for joint attention and chooses the most likely object based on a human teammate's hand cues. Our system works within natural human interaction time (< 3 seconds) and above 80% accuracy. Our joint attention estimator provides a meaningful step towards ensuring robots enable human social skills for successful human machine teaming.
Developing and Evaluating Teaching Material for Information Technology Moral Education for High School Students with Special Needs

Due to the recent development of information society, use of the Internet is becoming indispensable in assisting people with disabilities to live independent lives.So it is an urgent issue that information technology moral education is provided for children with special needs.However, little research has been conducted on information technology moral education to help such children acquire the necessary communication skills and behaviors.In this research, educational materials were developed for the information technology moral education of high school students with special needs.The purpose of these teaching materials was to help children learn about the protection of their personal information, and it dealt with content related to the "publication of photographs on SNS."The results of the student questionnaire survey showed that "I know what photos should not be published on SNS" increased significantly after the lecture compared to before the lecture.In addition, more than 80% of students responded that "the content of the lecture was easy to understand," suggesting the effectiveness in this material.
Speaker ethnic identification for continuous speech in malay language using pitch and MFCC

&lt;span&gt;Voice recognition has evolved exponentially over the years. The purpose of voice recognition or sometimes called speaker identification, is to identify the person who is speaking. This can be done by extracting features of speech that differ between individuals due to physiology (shape and size of the mouth and throat) and also behavioral patterns (pitch, accent and style of speaking). This paper explains an approach of voice recognition to identify the ethnicity of Malaysian people. Pitch and 13 Mel-Frequency Cepstrum Coefficients (MFCCs) are extracted from 52 recorded continuous speech in Malay for use as features to train the classifiers using Tree, Naïve Bayes, Nearest Neighbors and Support Vector Machine (SVM) and another 10 recorded speeches are used for testing. The results reveal that the use of a combination of pitch and 13 coefficients for features extraction and training the data using SVM provide better accuracy (57.7%) than the use of only 13 coefficients (53.8%).&lt;/span&gt;
Sequential Learning for Domain Generalization

In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approaches have been proposed with different motivating intuitions, but they typically optimize for a single step of domain generalization -- training on one set of domains and generalizing to one other. Our sequential learning is inspired by the idea lifelong learning, where accumulated experience means that learning the $n^{th}$ thing becomes easier than the $1^{st}$ thing. In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain. The performance at domain $n$ then depends on the previous $n-1$ learning problems. Thus backpropagating through the sequence means optimizing performance not just for the next domain, but all following domains. Training on all such sequences of domains provides dramatically more `practice' for a base DG learner compared to existing approaches, thus improving performance on a true testing domain. This strategy can be instantiated for different base DG algorithms, but we focus on its application to the recently proposed Meta-Learning Domain generalization (MLDG). We show that for MLDG it leads to a simple to implement and fast algorithm that provides consistent performance improvement on a variety of DG benchmarks.
Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm

Liver disease is an important public health problem. Over the past few decades, machine learning has developed rapidly and it has been introduced for application in medical-related. In this study we propose Support Vector Machine optimization parameter with genetic algorithm to get a higher performance of Root Mean Square Error value of SVM in order to estimate the liver disorder. The experiment was carried out in three stages, the first step was to try the three SVM kernels with different combination of parameters manually, The second step was to try some combination of range parameters in the genetic algorithm to find the optimal value in the SVM kernel. The third step is comparing the results of the GA-SVM experiment with other regression methods. The results prove that GA has an influence on improving the performance of GA-SVM which has the lowest RMSE value compared to another regression models.
Functional optimality of personal the meaning of the profession

The purpose of this study was to determine the functional potential of various types of personal meaning of the profession. The article describes the results of an empirical study that involved a sample of 647 subjects. As methods of mathematical-statistical processing were used: the Pearson correlation criterion, one-way analysis of variance, followed by post hoc comparison of the mean values of groups by the Scheffe method, the criterion GT-Vegeliusa. The analysis made it possible to identify the optimal, suboptimal and pessimistic personal meaning of the profession from the point of view of its regulatory functions.
Influence of Random Walk Parametrization on Graph Embeddings

Network or graph embedding has gained increasing attention in the research community during the last years. In particular, many methods to create graph embeddings using random walk based approaches have been developed. node2vec [10] introduced means to control the random walk behavior, guiding the walks. We aim to reproduce parts of their work and introduce two additional modifications (jump probabilities and attention to hubs), in order to investigate how guiding and modifying the walks influences the learned embeddings. The reproduction includes the case study illustrating homophily and structural equivalence subject to the chosen strategy and a node classification task. We were not able to illustrate structural equivalence and further results show that modifications of the walks only slightly improve node classification, if at all.
A Comparison of Fuzzy Logic Controller and PID Controller for Differential Drive Wall-Following Mobile Robot

This paper presents a comparison between PID controller and Fuzzy rule based controller of a differentially steered wall following mobile robot. Four different maps, generated using Mapper3 software are used to simulate the robot in MobileSim platform. The code was implemented using C++ language in ARIA development package. The robot simulation is performed with Pioneer P3-DX robot. In this study, the data was collected for each cases such as: left wall following and right wall following. In order to validate the results, maximum of 40 runs were conducted for each map and the results were compared with the illustrated methods.
Emoticon Prediction on Textual Data Using Stacked LSTM Model

Text messaging since a decade has taken an indispensable spot in modern lifestyle. It bought revolutionary changes to textual communication, such as instant responses, usage of abbreviations like Laugh Out Loud (LOL) or You Only Live Once (YOLO), etc., or the use of emoticons. The authors aim to predict an appropriate emoji by applying machine learning algorithms on a proposed model for a custom dataset. This model uses long short-term memory (LSTM) and recurrent neural network (RNN), with transfer learning. The experiment work has been carried out on custom dataset, and it has been observed that the proposed model achieved a significant prediction accuracy of 67.8% which is significantly better than the existing models in this field. It has also been observed that in some cases more than one candidate emoji may be used, which decreases the accuracy.
A Survey of Published Attacks on Intel SGX

Intel Software Guard Extensions (SGX) provides a trusted execution environment (TEE) to run code and operate sensitive data. SGX provides runtime hardware protection where both code and data are protected even if other code components are malicious. However, recently many attacks targeting SGX have been identified and introduced that can thwart the hardware defence provided by SGX. In this paper we present a survey of all attacks specifically targeting Intel SGX that are known to the authors, to date. We categorized the attacks based on their implementation details into 7 different categories. We also look into the available defence mechanisms against identified attacks and categorize the available types of mitigations for each presented attack.
Analysis semiotic and persuasive discourse to the billboard of legislative candidates 2019 in Indonesia

The present study aims to scrutinize the efficiency of using social media on EFL Yemeni learners in Indian universities during COVID-19 pandemic. This research covers four social media_ Facebook, Twitter, YouTube, and WhatsApp. The research methodology that is used in this research is quantitative design using an online questionnaire. The questionnaire was distributed online using a closed-ended questionnaire. Google forms was exploited in this study. The findings indicate that learners spend more time using social media for learning the English language, and listening is the most improved skill when using social media for learning the English language. Results also indicate that using social media makes learners acquire and practice new vocabularies and decrease their spelling errors. Finally, YouTube is the most used social media platform used for learning the English language. More research is needed to clarify the impact of other social media such as&nbsp; Snapchat, Instagram, Wechat ,etc. on EFL learners. Further research is also recommended for investigating the impact of social media on each skill separately.&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp
3D Photography using Context-aware Layered Depth Inpainting

We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show fewer artifacts compared with the state of the arts.
Vietnamese handwritten character recognition using convolutional neural network

Handwriting recognition is one of the core applications of computer vision for real-word problems and it has been gaining more interest because of the progression in this field. This paper presents an efficient model for Vietnamese handwriting character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model can achieve high performance on hard recognition tasks. The proposed architecture of the CNN network for Vietnamese handwriting character recognition consists of five hidden layers in which the first 3 layers are convolutional layers and the last 2 layers are fully-connected layers. Overfitting problem is also minimized by using dropout techniques with the reasonable drop rate. The experimental results show that our model achieves approximately 97% accuracy.
Korean Erroneous Sentence Classification With Integrated Eojeol Embedding

This paper attempts to analyze the Korean sentence classification system. Sentence classification is the task of classifying an input sentence based on predefined categories. However, spelling or space error contained in the input sentence causes problems in morphological analysis and tokenization. This paper proposes a novel approach of Integrated Eojeol (Korean syntactic word separated by space) Embedding to reduce the effect of poorly analyzed morphemes on sentence classification. The paper also proposes two noise insertion methods that further improve classification performance. Our evaluation results indicate that by applying the proposed methods on the existing sentence classifiers, the sentence classification accuracy on erroneous sentences is increased by 8% to 15%.
Training LSTM for Unsupervised Anomaly Detection Without A Priori Knowledge

Unsupervised anomaly detection on time-series is widespread in the industry and an active research topic. Recently, impressive results have been obtained by leveraging the progresses of deep learning, and in particular through the use of Long Short Term Memory (LSTM) neural networks. Yet, latest state-of-the-art unsupervised LSTM-based solutions still require a priori knowledge about normality as they need to train the model on time-series without any anomaly. In contrast, we propose a novel anomaly detector, coined as LSTM-Decomposed (LSTM-D), that does not require this normality knowledge. More specifically, we pre-process the timeseries with a spectral based information reduction such that the LSTM-based detector receiving the time-series becomes less likely to learn the anomaly, and hence miss its detection. We motivate our intuitions through simple examples and verify the performance improvement with respect to state-of-the-art solutions in a reference and publicly available data set.