Reviewerly/exHarmony huggingface dataset:
Wide and Deep Reinforcement Learning Extended for Grid-Based Action Games

For the last decade, Deep Reinforcement Learning (DRL) has undergone very rapid development. However, less has been done to integrate linear methods into it. Our research aims at a simple and practical Wide and Deep Reinforcement Learning framework to extend DRL algorithms by combining linear (wide) and non-linear (deep) methods. This framework can help to integrate expert knowledge or to fuse sensor information while at the same time improving the performance of existing DRL algorithms. To test this framework we have developed an extension of the popular Deep Q-Networks Algorithm, which we call Wide Deep Q-Networks. We analyze its performance compared to Deep Q-Networks and Linear Agents, as well as human agents by applying our new algorithm to Berkeley’s Pac-Man environment. Our algorithm considerably outperforms Deep Q-Networks both in terms of learning speed and ultimate performance, showing its potential for boosting existing algorithms. Furthermore, it is robust to the failure of one of its components.
An energy-efficient adaptive course control system for ocean surface ships

In order to improve the performance and the energy efficiency (rudder actions) of the ship controlsystem in presence of changing environmental conditions and system uncertainty, this paper develops anovel adaptive fuzzy-PID course controller with a dynamic compensator and a nonlinear feedback for theautonomous surface ship. Firstly, an adaptive PID control strategy, whose control parameters are real-timeadjusted by the fuzzy system, is designed to achieve the optimal control effect and the robust performance.Then, considering the uncertainty and unpredictable external disturbances, the Least Square Support VectorMachines (LSSVM) approach is employed to online identify and suppress the disturbances for the purpose ofcompensating the Fuzzy-PID controller. Furthermore, the nonlinear feedback is added in the control law todeal with the control inputs, and then the whole control system is named as “NFPL”. The effectiveness andquality of the designed controller are investigated in the numerical simulations. Results demonstrate goodadaptability and robust performance of the designed control system in spite of the existence of the timevarying environmental disturbance, and the advantages in the reduction of the settling time and rudderenergy consumption as well as fast response are also verified.
Analysis and implementation of computer-based system development of stemming algorithm for finding Arabic root word

Abstract At present many experts in the field of information technology have designed and developed algorithms to solve stemming problems, especially in Arabic. But of the many stemming analyses in Arabic, there is no standardization of a good stemming algorithm in analysing the accuracy of the text in the Koran. The development of stemming in the Koran is significant to work because it supports the Sharaf classification in the Koran to understand the meaning of every word in the Qur’an. One stemmer or stemming an algorithm to find the primary form of an Arabic word is the Khoja Stemmer algorithm. The way of working from Khoja Stemmer is to try to find the root of an Arabic word by removing the longest prefix and the longest suffix of a word, then try to determine the root of the remaining words using the root word dictionary. In this study, the Khoja Stemmer built was able to calculate the average stemming of the Koran by 95.295%. But the root words produced by Khoja Stemmer if manually checked, there are still several errors. Thus, an Al-Quran dictionary is needed to analyse each stemming result conducted by Khoja stemmer in stemming the Koran.
A Critical Look at Benchmarking Datasets: Problem of finding relationship between sentences

Competitions on many data sets attract the attention of the researchers and thanks to the competition, better systems are developed. Recently, however, it has been found that algorithms use annotation artifacts on some datasets. These findings have led to questioning of the results-oriented assessment. It was previously shown that the use of the second sentence is sufficient to prediction of the relationship between two sentences in a popular benchmark dataset. In this study, the Turkish version of the dataset was created and similar findings were obtained for both languages. In addition to the attack methods in the literature, word-based and sentence-based new attacks were applied, and when the sentences were replaced, the estimates were shown to contain logical inconsistencies. For example, if the relationship between two sentences is contradiction, then the relationship should not change when the sentences change. However, it was seen that this is not the case in a significant part of the samples. As a result, it is revealed that the result oriented achievements obtained in this dataset are not real by examining the internal dynamics of the system. This critical approach should not be left on all popular datasets.
Full-field deformation measurement of a large-rotation object using digital image correlation

Digital image correlation method is widely used in macro- and mesoscopic mechanical tests because of the advantages of non-contact, high precision, full field measurement, and simple experimental equipment. But it still has many shortcomings in theory and application. For example, the traditional DIC method has difficulty obtaining the desired result when the surface of the test object is rotated substantially or the deformation involves large rotation (i.e. measuring helicopter blade deformation dynamically). Some scholars argue that a rotation angle greater than 7&deg; will not work with the DIC algorithm, and this phenomenon is called decorrelation [1-3]. The application of various nonlinear optimization algorithms greatly reduces the computation time of the DIC iteration process.
An Effective Way to Boost Black-Box Adversarial Attack

Deep neural networks (DNNs) are vulnerable to adversarial examples. Generally speaking adversarial examples are defined by adding input samples a small-magnitude perturbation, which is hardly misleading human observers’ decision but would lead to misclassifications for a well trained models. Most of existing iterative adversarial attack methods suffer from low success rates in fooling model in a black-box manner. And we find that it is because perturbation neutralize each other in iterative process. To address this issue, we propose a novel boosted iterative method to effectively promote success rates. We conduct the experiments on ImageNet dataset, with five models normally trained for classification. The experimental results show that our proposed strategy can significantly improve success rates of fooling models in a black-box manner. Furthermore, it also outperforms the momentum iterative method (MI-FSGM), which won the first places in NeurIPS Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.
No Reference Image Quality Assessment by Information Decomposition

No reference (NR) image quality assessment (IQA) is to automatically assess image quality as would be perceived by human without reference images. Currently, almost all state-of-the-art NR IQA approaches are trained and tested on the databases of synthetically distorted images. The synthetically distorted images are usually produced by superimposing one or several common distortions on the clean image, but the authentically distorted images are often simultaneously contaminated by several unknown distortions. Therefore, most IQA performances will greatly drop on the authentically distorted images. Recent researches on the human brain demonstrate that the human visual system (HVS) perceives image scenes by predicting the primary information and avoiding residual uncertainty. According to this theory, a new and robust NR IQA approach is proposed in this paper. By the proposed approach, the distorted image is decomposed into the orderly part and disorderly part to be separately processed as its primary information and uncertainty information. Global features of the distorted image are also calculated to describe the overall image contents. Experimental results on the synthetically and authentically image databases demonstrate that the proposed approach makes great progress in IQA performance.
Incorporating Digital “Teach-nology” and Mobile Learning Application in Teacher Education in VNU, Hanoi

Today's students are digital natives who learn best when they have in hand digital tools for various interactive experiences. Digital transformation in education, mobile/wearable devices, and applications, BYOD/BYOTs do this, but it can be a complex process, especially in pedagogical aspects. There is also change the conception of ICT use in education by well-known educational technology preferably digital trend in advance. In Vietnam, mobile device ownership is increasing day by day. Relatively, discussions on the benefits of mobile device applications in education.
Acquisition of the ability to recognize the 3D position using movable binocular eye-camera and self-body

This paper proposes the method that the humanoid with the movable binocular eye-camera unit can acquire the ability to recognize distance to an object by itself. Movable eye-camera has advantages such as quick object tracking or wide field of view. However, it’s difficult to get the distance to an object because of the errors of intrinsic and extrinsic camera parameters. We solve this problem by using the neural network that receives the convergence angle and the object position in binocular images as input and outputs the object position in the real world. In the training phase, the humanoid moves its arm randomly and recognizes hand position in binocular images. At the same time, it collects hand position in the real world calculated by forward kinematics as teaching data. We confirmed the validity of the method by using musculoskeletal humanoid "Musashi" and its movable binocular eye-camera unit.
The Research on Sensorless Control of Permanent Magnet Synchronous using SMC and SMO

Abstract This work proposes a way to achieve the sensorless control of permanent-magnet synchronous motor using sliding mode controller (SMC) and Sliding Mode Observer(SMO). In this Paper, the SMC is designed to replace the speed control loop PI controller from the original permanent magnet synchronous motor control system with the sliding mode variable structure control theory. Combined with the modified SMO, the “SMC and modified SMO” motor control system is proposed. Compared with the “PI and modified SMO” mode, the performance of the motor control is increased substantially, the interference torque speed attenuation is decreased by 87.5%, and the speed control error was also reduced by 67.7%. There is no overshoot with the reaction speed, and significant improvement in response speed, anti-interference performance, control accuracy and observation precision are achieved. Overall, both the system control and observation performance are significantly improved.
Analysis and Recognition of Hand-Drawn Images with Effective Data Handling

Everyday data is being produced at a mind-boggling rate and at current pace, we are generating more than 2.5 quintillion bytes of data each day. With more data, there comes the chance of generating and understanding more useful information out of it. With this view in mind, we are presenting our work on Quick Draw dataset. It is a repository of approximately more than 50 million hand-drawn drawings of about 345 different objects, contributed by over 15 million users. The work we have presented is a useful approach to perform a country-wise analysis of the styles that people use while making strokes to draw an object or image. Since we are dealing with a huge dataset, we are also presenting a powerful technique to effectively reduce the data and its dimension which results in much more simplified and concise data. Based on the simplified data, we are enhancing the performance of our models to recognize these hand-drawn objects based on the strokes that a user has used for drawing them. For the recognition of objects, we have worked on traditional machine learning models - Nearest Neighbor (K-NN), Random Forest Classifier (RFC), Support Vector Classifier (SVC) and Multi-Layer Perceptron model (MLP) by selecting the best hyper-parameters. To make the recognition results better, we have also presented our work on deep learning models - CNN, LSTM, and WaveNet networks.
Time Series Models of High Frequency Solar Radiation Data

In this era of Internet of Thing (IoT) and Big Data, observations are collected not as daily, weekly, monthly, quarterly or yearly, but are taken at a finer time scale. These observations are in hourly, minutely, second and then divide into fractions of second have become available mainly due to the advancement in data acquisition and processing techniques. In this chapter, high frequency time series data of solar radiationSolar radiation of every 30 s will be gathered and model using Box and Jenkins methodology. This methodology comprises of model identification, model estimation, model verification and finally model adequacy.
Evaluation of Sentence Embedding Models for Natural Language Understanding Problems in Russian

We investigate the performance of sentence embeddings models on several tasks for the Russian language. In our comparison, we include such tasks as multiple choice question answering, next sentence prediction, and paraphrase identification. We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings. We conduct two series of experiments, using both unsupervised (i.e., based on similarity measure only) and supervised approaches for the tasks. Finally, we present datasets for multiple choice question answering and next sentence prediction in Russian.
Introduction to Human–Computer Interaction (HCI) Design

Four criteria for evaluating an HCI design—efficiency, learnability, user satisfaction, and utility—are described. This is followed by a description of four HCI design goals—know the user, prevent user errors, optimize user abilities, and be consistent—and a brief description of HCI design steps.
Determining key energy relations, quantum states and associated matrix formalisms relative to the collision between two diatomic molecules under a strong magnetic field

We carry out a dynamical analysis of the collision process between two diatomic molecules in the presence of a strong magnetic field.In order to exemplify, these molecules can be, say, typically, lasing molecules as, for instance, relative to carbon-monoxide gasdynamic lasers.The molecules in question behave as quantum anharmonic oscillators after the Morse potential.In our analysis, we simulate the kinetic energy lost after the collision by assuming, equivalently, two additional molecules (fictitious and identical to the molecules which collide) with the above kinetic energy.Furthermore, the corresponding density of states is determined and associated matrix formalisms are established to calculate the total number of states.
Connecting two types of representations of a permutation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e32" altimg="si17.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math>

In this paper, we connect two types of representations of a permutation σ of the finite field Fq. One type is algebraic, in which the permutation is represented as the composition of degree-one polynomials and k copies of xq−2, for some prescribed value of k. The other type is combinatorial, in which the permutation is represented as the composition of a degree-one rational function followed by the product of k 2-cycles on P1(Fq)≔Fq∪{∞}, where each 2-cycle moves ∞. We show that, after modding out by obvious equivalences amongst the algebraic representations, then for each k there is a bijection between the algebraic representations of σ and the combinatorial representations of σ. We also prove analogous results for permutations of P1(Fq). One consequence is a new characterization of the notion of Carlitz rank of a permutation on Fq, which we use elsewhere to provide an explicit formula for the Carlitz rank. Another consequence involves a classical theorem of Carlitz, which says that if q>2 then the group of permutations of Fq is generated by the permutations induced by degree-one polynomials and xq−2. Our bijection provides a new perspective from which the two proofs of this result in the literature can be seen to arise naturally, without requiring the clever tricks that previously appeared to be needed in order to discover those proofs.
Comparative Analysis of Rule-Based, Dictionary-Based and Hybrid Stemmers for Gujarati Language

Gujarati is an Indo-Aryan language spoken substantially by people of Gujarat state of India. It is highly and actively used for communication in Gujarat government's educational institutes and offices, local industries, businesses as well as in media such as newspapers, magazines, radio and television programs. In all these areas, Internet is the keen requirement today. Its utilization will be increased if contents are provided on web in regional language using the notion of Natural Language Processing (NLP). In NLP, stemming plays a vital role in retrieving accurate contents and producing effective results for web search query. It identifies the root word from morphological variants of respective word. There are three typical approaches to perform stemming: rule-based approach, dictionary-based approach and hybrid approach. In this paper, we present a comparative empirical study of these three approaches for Gujarati language. The aim of the study is to evaluate the effectiveness of different types of stemmers for Gujarati language. Firstly, we discuss the rule-based algorithm and present its evaluation with 152 different suffix stripping rules. Next, we illustrate stemming mechanism developed using Gujarati dictionary that contains around 20000 root words. Lastly, we discuss the hybrid approach that is a combination of rule-based and dictionary-based approaches. Experimental results reveal that hybrid approach retrieves more accurate stemmed words compared to rule-based and dictionary-based approaches.
Effective Barcode Hunter via Semantic Segmentation in the Wild

Barcodes are popularly used for product identification in many scenarios. However, locating them on product images is challenging. Half-occlusion, distortion, darkness or targets being too small to recognize can often add to the difficulties using conventional methods. In this paper, we introduce a large-scale diverse barcode dataset and adopt a deep learning-based semantic segmentation approach to address these problems. Specifically, we use an efficient method to synthesize 30000 well-annotated images containing diverse barcode labels, and get Barcode-30 k, a large-scale dataset with accurate pixel-level annotated barcode in the wild. Moreover, to locate barcode more precisely, we further propose an Effective Barcode Hunter - BarcodeNet. It is a semantic segmentation model based on CNN (Convolutional Neural Network) and is mainly formed with two novel modules, Prior Pyramid Pooling Module (P3M) and Pyramid Refine Module (PRM). Additional ablation studies further demonstrate the effectiveness of BarcodeNet, and it yields a high mIoU result of 95.36% on the proposed synthetic Barcode-30 k validation-set. To prove the practical value of the whole system, we test the BarcodeNet trained on train-set of Barcode-30 k on a manually annotated testing set that only collected from cameras, it achieves mIoU of 90.3%, which is a very accurate result for practical applications.
Effective integration of educational technologies in terms of TPCK framework at technical university

The framework of Technological Pedagogical Content Knowledge (TPCK) has been developed as a valuable tool for understanding the ways for effective technology use in the teaching process. Due to continuous innovations in education technologies, educators experience some difficulties how to implement the appropriate technology and correlate it with learning goals, students’ needs, content of the subjects and pedagogical strategy. It caused the separation of the educational technology and situational educational process. TPCK framework can bridge the gap and promotes how to combine classical pedagogical strategies and contemporary technologies. This study focuses on the description and analysis of practical TPCK framework implementation at English Language classes for bachelors at technical university. The paper suggest examples of redesigned learning activities according to TPCK framework concepts in order to enhance students’ language skills. The results of the practical implementation observations and students’ as well as teachers’ reflective blogs demonstrated the benefits and challenges of the approach. Students’ feedback evidenced the meaningful impact on their academic achievements and improvement of language competence. Moreover, the described tool can be used as an indicator to assess teachers’ knowledge of content, pedagogy and level of digital competence in terms of the professional development.
Explore Convolutional Neural Networks in Virtual Reality

We visualize the functionality of Convolutional Neural Networks (CNN) in Virtual Reality to help newcomers understand the general functioning of these algorithms. Our interactive visualization allows users to explore CNNs layer by layer.
Photonic circuits with time delays and quantum feedback using nanofibres and long coupling fibres

We study photonic circuits consisting of cold-atom ensembles coupled to light modes of optical nanofibres and long lengths of connecting or feedback fibres, such that the propagation time of light along these fibres is comparable to the timescale of the atomic dynamics. This provides a non-Markovian setting for quantum dynamics and feedback. The fibre-based platform enables implementation of a simple configuration for time-delayed quantum feedback where light emitted from the atoms propagates along a length of fibre and is reflected back towards the atoms by a fibre bragg grating (FBG). We will present the initial results of our experiments.
Adaptive Block Matching Based Quantization for Lossy Image Compression

We propose an efficient method of quantization of discrete cosine transform (DCT) coefficients for lossy image compression. The main novelty of our approach is in utilization of masking ability of image regions having large values of error of block matching (searching patches similar to a given one). We propose to calculate a quantization level for a given image region in a proportion to a block matching error for this region, and describe the method of additional quantization of DCT coefficients for JPEG compression. We collect the mean opinion scores for a designed image test set containing compressed images for three different quantization schemes, and show by numerical analysis of 300 test images ofTAMPERE17 database, that the proposed method for the same compression ratio is able to provide significantly better MOS value, than one based on the conventional quantization. We also show that for different fixed values of the CSSIM4 metric, the proposed quantization provides a compression ratio increase by 30%-50% comparing to the conventional quantization.
Metalearning as One of the Task of the Machine Learning Problems

The concepts of metalearning as one of the tasks of machine learning are considered.The basic principles of metalearning g and examples of solving problems of machine and metalearning in various fields of human activity are given.It is planned for a decision support system construction based on an inductive approach for complex processes modeling and forecasting.