Reviewerly/exHarmony huggingface dataset:
A Real-Time Actor-Critic Architecture for Continuous Control

Reinforcement learning achieved impressive results in various challenging artificial environments and demonstrated its practical potential. In a real-world environment, an agent operates in continuous time, and it is unavoidable for the agent to have control delay. In the context of reinforcement learning, we define control delays as the time delay before an agent actuates an action in a particular state. The high-variance of control delay can destabilize an agent’s learning performance and make an environment Non-Markovian, creating a challenging situation for reinforcement learning algorithms. To address this issue, we present a scalable real-time architecture, RTAC, for reinforcement learning to continuous control. A reinforcement learning application usually consists of a policy training phase in simulation and the deployment phase of the learned policy to the real-world environment. We evaluated RTAC in a simulated environment close to its real-world setting, where agents operate in real-time and learn to map high-dimensional sensor data to continuous actions. In extensive experiments, RTAC was able to stabilize control delay and consistently learn optimal policies. Additionally, we demonstrated that RTAC was suitable for distributed learning even in the presence of control delay.
Quaternion Mahalanobis Non-local Means for Color Image Denoising

Abstract By improving the quaternion non-local means (QNLM) image denoising algorithm, the quaternion Mahalanobis non-local means (QMNLM) method is proposed. This method combines quaternion with non-local similarity priors and introduces quaternion Mahalanobis distance. Firstly, the Mahalanobis distance between the image patches is calculated in the eigenspace by singular value decomposition of quaternion since the Mahalanobis distance is not robust in the sample space. Secondly, the image data is analyzed with the principal component analysis method, thus the Mahalanobis distance equation is simplified. The image with noise affected by high noise was first processed with a Gaussian low pass filter (LPF). Experimental results show that the noise reduction effect of this method is better than the non-local means (NLM), generalized non-local Means (GNLM) and QNLM methods. The proposed algorithm can effectively eliminate image noise and efficiently improve the image visual effect.
The reading strategy of undergraduate and graduate students in the direction of preparation “Library and Information Activities”

The article presents the results of a study on the use of reading strategies among students of the information and library faculty The developed method is based on the types of reading (by purpose and methods), information resources necessary for each type. The types of “reading for educational purposes” in this strategy are arranged according to their priority: for undergraduate students, first comes educational, then general cultural, self-educational, scientific and leisure reading; for graduate students – scientific, educational, self-educational, general cultural and leisure reading. It was revealed that the differentiation of “reading for educational purposes” is a condition for the implementation of a strategic approach in the formation of the reader’s competency for the future librarian, and allows one to successfully master all the competencies necessary for professional activity and the development of a holistic system of training library staff on a single conceptual basis. The authors reveal the methodology of working with types of reading separately for undergraduate and graduate students. The experimental results of control and experimental groups of students are given. Particular attention is paid to the relationship of reading types with the generated competencies required by future library specialists. The article is intended for teachers and specialists in the field of library and information activities.
Genetic Algorithm Applied in UAV’s Path Planning

The present paper introduces a hybrid genetic algorithm for path planning problem with obstacle avoidance. The genetic algorithm is combined with Ray Casting (RC) algorithm, where RC is responsible to avoid obstacles and to find safe regions for emergency landing. Thus, the path planning system must deal with a non-convex environment when planning and re-planning trajectories. The system must also work embedded on the UAV running under a Raspberry Pi board. The hybrid method is evaluated over 50 benchmark maps from literature with satisfactory results reported.
수학과 인성교육의 국내 연구 동향

This study intends to identify domestic research trends in character education in mathematics in the midst of the reality that each school should establish and implement a character education plan every year after the enactment of the Character Education Promotion Act in 2015. While there are many studies regarding character education, creativity and character education, or creativity education in math. and analytical studies on research trends, studies on character education in teaching mathematics have been rare. The scope of analysis includes not only academic journals (nominated) but also academic conferences and academic dissertations. This study also covers the papers whose contents are related to character education, even if the word ‘character education’ is not included in the title. Thus, a total of 57 papers which were released from 1998 to 2019 were analyzed. Research trends were analyzed according to five categories: research year, subjects, research topic, research method, and terminology. This study found that research related to mathematics and character education has been on a gradual rise since 2011. As to the subjects, 40.5% were others (the case where the subjects of study are not people), 17.5% were elementary school students, and 15.8% were middle school students. However, there was a lack of research on high school students (7%) and especially gifted students (3.5%). In the area of trends of research subjects, research was actively conducted in the area of ‘methods of education in mathematics and character education’ (38.6%), followed by ‘general research in mathematics and character education’( 29.8%), and ‘research related to teachers’ (15.8%). In the area of trends of research methodology, qualitative research (52.8%) was higher than quantitative research (11.3%), and mixed-methods research (35.8%) was also significantly conducted. This is because it is difficult to analyze the effects of character education only through quantitative research due to the nature of character education. The terms commonly used in the title of the papers were “character education, character cultivation, character factors, creativity and character education”, and “human education, mathematical personality, and democratic citizenship development” were also used.
Completing College Writing Assignments on Mobile Phones: Comparing Students’ Attitudes and Engagement Across Disciplines and Age

Large variations in mobile users, mobile devices, and course content make generalizations about mobile learning difficult. Prior to the international pandemic that forced more virtual and mobile instruction, this exploratory study measured how students in one journalism and one English composition course at two universities responded to completing writing assignments using only their phones. Although pre and post differences in student attitudes and engagement between the writing courses were insignificant, further analyses of age differences (freshman vs seniors) revealed significant variance. These results suggest only a 3-year time span could determine how much students embrace mobile assignments.
Batch Photo2Data URI | Homepage | converts images to base64 (data uri) in a batch (bulk, massive) way

Batch Photo2Data URI | Homepage | converts images to data-uri (base64, image2html) and embedds them in html in a batch (bulk, massive) way, to make html pages and html newsletters more efficient to download.
Utilization of microalgae Spirulina platensis as a raw material for making capsule shell

Research on capsule shells has been developed to replace gelatin capsule because it made from pig skin, so it is not halal for consumption. With this problem, carrageenan is used as a substitute for gelatin, because it has properties as a gelling agent but still has a weakness that is not stable in stomach. The purpose of this study was to use microalgae Spirulina platensis because it contains polyhydroxybutyrate (PHB) which are the kind of polymer to improve performance as a drug delivery agent and prolongs the dissolution of the drugs so that the drug will be released into the intestine. The formulation of the solution that used in this research is variation of Spirulina platensis biomass (1%, 1.5%, 2%, 2.5%, 3%) (w/v). The best formulation was determined by capsule shell performance when characterization tests, such as moisture content test, pH, resistance in water, durability in acid solution test, and flexibility. This parameter follow Departemen Kesehatan RI. The best concentration of S. platensis in this research is 3% (w/v), with characterization that obtained as follows: moisture content is 15.77%, pH is 6.5, dissolve in water in 31 minutes 25 seconds, dissolve in acid environment in 35.23, and flexible when merged between cap and body. This capsule can survive in acid condition at pH 1.2 for 75 minutes and release 61.30% of drugs, the at pH 4.5 release 54.90% of drugs at 120 seconds, and the last at pH 6.8 can release 89.58% of drugs. The concentration of biomass affect to the thickness of capsule shell, disintegration time of capsule, and time for dissolution of the capsule shell. With these parameters, it is expected that the capsule form will have stiff and strong structure and not easily degraded at acid environment, so the drug will be released at intestine.
Real-time Informatized caption enhancement based on speaker pronunciation time database

Abstract IBM Watson is one of the representative tools for speech recognition system which can automatically generate not only speech-to-text information but also speaker ID and timing information, which is called as Informatized Caption. However, if there is some noise in the voice signal to the IBM Watson API, the recognition performance is significantly decreased. It can be easily found in movies with background music and special sound effects. This paper aims to improve the inaccuracy problem of current Informatized Captions in noisy environments. In this paper, a method of modifying incorrectly recognized words and a method of enhancing timing accuracy while updating database in real time are suggested based on the original caption and Informatized Caption information. Experimental results shows that the proposed method can give 81.09% timing accuracy for the case of 10 representative animation, horror and action movies.
Upper bound probability of double spend attack on SPECTRE

We obtained analytical expressions of upper estimates for success probability of a double spend attack on DAG-based consensus protocol SPECTRE, depending on network parameters. Using such estimates, it is possible to evaluate the number of confirmation blocks that is sufficient for prevention of such attacks.
Non-reference Image Quality Assessment for Contrast Distortion Based on Pixel Statistics and Color

For most natural images, proper contrast enhancement can achieve better visual quality. However, there are few image quality assessment methods for contrast distortion. We improve a new non-reference image quality assessment model to predict the image quality of contrast changes. Our improvements can be listed in two aspects:1. From the perspective of gray pixel information statistics, we add new perceptual features to the original model, including standard deviation, histogram energy, and skewness. These features enhance the prediction accuracy of the model. 2. Considering the effect of color on the contrast of the image, we extracted two key features related to the overall color of the image, named color saturation and colorfulness. Furthermore, support vector regression (SVR) is used to fuse all features to predict the image quality score, and we achieve better performance on three typical databases (CID2013, CCID2014, and CSIQ).
Motivating Participants in Human-based Evolutionary Computation Systems

Human-based evolutionary computation (humanbased EC), which uses humans as executors of all evolutionary operators, can solve problems for which only humans can judge the quality of solutions. In human-based EC systems, when participants whojoin problem solving are fixed, the upper limit of search performance of the human group should be unpredictable but roughly implicitly decided. Therefore, unlike standard EC using computers, human-based EC systems need to enhance or maintain motivations of participants for contributions to derive better search performance of the group. In the paper, we propose two methods for motivating participants in human-based EC systems. The first method is meant to enhance motivations by differentiating participants. More precisely, it feedbacks rankings on the number of times of producing and evaluating solution candidates to participants in a realtime manner. The second method is meant to maintain motivations by equalizing participants. More precisely, it sets the maximum allowed number of times of producing and evaluating solution candidates equally to all participants. It can also theoretically shorten a time period for problem solving. The two methods are strategically contrary to each other. We reveal though experiments that the first method enhances motivations of participants and also that systems using the second method and not using it produce same quality of solutions. These results suggest that we should feedback another rankings which are not based on the number of times to participants while using the second method to obtain high quality of solutions in a short period of time.
A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity.In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm.Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems.Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm's accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS.Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem.Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately.
잠재성장모형을 적용한 청소년의 자기조절학습 전략이 학업성취도에 미치는 영향 분석

The purpose of this study was to examine the developmental trajectory of academic achievement for adolescent students by applying the potential growth model, and to analyze the effects of self-regulated learning strategies on the developmental trajectory of academic achievement. The study used data collected from the Korean Educational Longitude Study (KELS2013) and the research subjects totaled 6,033 adolescents. The developmental trajectory of adolescent students' academic achievement demonstrates that there were individual differences. The effects of self-regulated learning strategies on the development trajectory of academic achievement were analyzed. Self-efficacy (within self-regulated learning strategies) had a significant effect on academic achievement, which continued to improve. Cognitive regulation strategies did not significantly affect academic achievement. Behavior control strategies had a negative effect on academic achievement. Therefore, it is proposed that a systematic self-regulating learning strategy program be developed for adolescent students, moreover, a way to improve academic achievement is required.
An Effective Search Economics Based Algorithm for Feature Selection

The basic idea of feature selection, as the name suggests, is to select an appropriate subset of features of a problem in question so as to reduce the data complexity, thus accelerating the analysis process. Being an NP-hard problem, it has become a critical research topic in data mining for years. In this study, we propose a novel, effective metaheuristic algorithm, called search economics for feature selection, for solving this problem. The proposed algorithm is built on the idea of investing in different sectors of the market based on the potential of each sector; in other words, it is built on the idea of searching different regions in the solution space based on the so-called potential of each region. By using such a search strategy, the proposed algorithm is less likely to fall into local optimum at early iterations while at the same time retaining the search diversity during the convergence process. The simulation results show that the proposed algorithm provides a better result than all the other state-of-the-art feature selection algorithms compared in this study in solving the eighteen well-known UCI datasets.
KIDER: Knowledge-Infused Document Embedding Representation for Text Categorization

Advancement of deep learning has improved performances on a wide variety of tasks. However, language reasoning and understanding remain difficult tasks in Natural Language Processing (NLP). In this work, we consider this problem and propose a novel Knowledge-Infused Document Embedding Representation (KIDER) for text categorization. We use knowledge patterns to generate high quality document representation. These patterns preserve categorical-distinctive semantic information, provide interpretability, and achieve superior performances at the same time. Experiments show that the KIDER model outperforms state-of-the-art methods on two important NLP tasks, i.e., emotion analysis and news topic detection, by 7% and 20%. In addition, we also demonstrate the potential of highlighting important information for each category and news using these patterns. These results show the value of knowledge-infused patterns in terms of interpretability and performance enhancement.
Bearing-Only Target Tracking Based on Piecewise Backward Smoothing Kalman Filter

Abstract In order to solve the problem that the Unscented Kalman Filter algorithm is sensitive to the initial value selection and the noise is easy to expand the error in the recursive process, a piecewise backward smoothing Kalman filter method is proposed in this paper. To reduce the influence of noise, the observation data is optimized in sections, and the forward and reverse use of historical data is increased. The simulation results under different initial distance error, different observation angle variance and different initial velocity error show that the proposed method is effective in underwater bearings only target tracking, and the effect of Unscented Kalman filter is obviously improved, the estimation error is greatly reduced, and the estimation accuracy and stability are improved.
Trend Mining 2.0: Automating the Discovery of Variable Trends in the Objective Space

Practical multi-criterion decision making not only involves the articulation of preferences in the objective space, but also a consideration of how the variables impact these preferences. Trend mining is a recently proposed visualization technique that offers the decision maker a quick overview of the variables' effect on the structure of the objective space and easily discover interesting variable trends. The original trend mining approach relies on a set of predefined reference directions along which an interestingness score is measured for each variable. In this paper, we relax this requirement by automating the approach to find optimal reference directions that maximize the interestingness for each variable. Additional extensions include the use of an Achievement Scalarizing Function (ASF) for ranking solutions along a given reference direction, and an updated interestingness score formulation for more appropriately handling discrete variables. We demonstrate the working of the extended approach on DTLZ2 and WFG2 benchmarks for up to five objectives and on a biobjective engineering design problem. The results show that the ability of the proposed approach to detect variable trends in high dimensional objective spaces is heavily dependent on the quality of the solutions used.
Research on Recognition Algorithm of Abnormal Behavior of Workers in Two-Stream Convolutional Network

Abstract In order to identify the abnormal behaviors of workers in the factory, this paper proposes an improved algorithm for identifying abnormal behaviors of workers in a two-stream convolutional network. The workers’ body contour shape information extracted from the convolutional neural network is input into the LSTM network in order to extract timing information between frames. Secondary extraction of the dense optical flow image of the video image, sparse extraction of pixels with small changes in optical flow value in the dense optical flow image, and then put the new continuous optical flow image into the continuous optical flow image network. The two networks are fused after softmax classification to get the final recognition result. Experiments on the CAVIAR dataset, CASIA dataset, and self-built behavior dataset show that compared with other abnormal behavior detection methods and traditional two-stream convolution algorithms, the accuracy of the improved algorithm in this paper is improved by 1% -4%.
Sentiment Prediction using Attention on User-Specific Rating Distribution

For document-level sentiment prediction, many methods try to first capture opinion words then infer sentiments based on these words. We observe that different users may use same words to express different levels of satisfaction, e.g., 'great' may mean very satisfaction to some users, or simply a general description to others. Intuitively, we expect the choice of a sentiment expression follows a distribution specific to a user and her sentiment to a product. In this paper, we propose a hierarchical neural network model with user-specific rating distribution attention (H-URA) to learn document representation for sentiment prediction. Our model learns local sentiment distributions from a user's expression, at word-level and at sentence-level respectively. We also learn a global sentiment distribution by using both user and product information. The attention weight is then computed from the local and global sentiment distributions. Experimental results show superiority of our H-URA model compared to strong baselines on benchmark datasets.