Reviewerly/exHarmony huggingface dataset:
Accelerating Reinforcement Learning with Suboptimal Guidance

Reinforcement learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for learning signals. For control problems, we often have some controller readily available which might be suboptimal but nevertheless solves the problem to some degree. This controller can be used to guide the initial exploration phase of the learning controller towards reward yielding states, reducing the time before refinement of a viable policy can be initiated. To achieve such an exploration guidance while also allowing the learning controller to outperform the demonstrations provided to it, Nair et al. (2017) proposes to use a "Q-filter" to select states where the agent should clone the behaviour of the demonstrations. The Q-filter selects states where the critic deems the demonstrations to be superior to the agent, providing a natural way to adjust the guidance in a manner that is adaptive to the proficiency of the demonstrator. The contribution of this paper lies in adapting the Q-filter concept from pre-recorded demonstrations to an online guiding controller, and further in identifying shortcomings in the formulation of the Q-filter and suggesting some ways these issues can be mitigated — notably by replacing the value comparison baseline with the guiding controller’s own value function — reducing the effects of stochasticity in the neural network value estimator. These modifications are tested on the OpenAI Gym Fetch environments, showing clear improvements in adaptivity and yielding increased performance in all robotics environments tested.
Jointly Discriminating and Frequent Visual Representation Mining

Discovering visual representation in an image category is a challenging issue, because the visual representation should not only be discriminating but also frequently appears in these images. Previous studies have proposed many solutions, but they all separately optimized the discrimination and frequency, which makes the solutions sub-optimal. To address this issue, we propose a method to discover the jointly discriminating and frequent visual representation, named as JDFR. To ensure discrimination, JDFR employs a classification task with cross-entropy loss. To achieve frequency, JDFR uses triplet loss to optimize within-class and between-class distance, then mines frequent visual representations in feature space. Moreover, we propose an attention module to locate the representative region in the image. Extensive experiments on four benchmark datasets (i.e. CIFAR10, CIFAR100-20, VOC2012-10 and Travel) show that the discovered visual representations have better discrimination and frequency than ones mined from five state-of-the-art methods with average improvements of 7.51% on accuracy and 1.88% on frequency.
Real-time Trajectory Replanning for Micro-UAVs in Cluttered Environments with Dynamic Obstacles

In this paper, we extend an existing real-time planner to avoid dynamic obstacles in unknown cluttered environments for Micro-UAVs. We use an optimization-based approach to generate the local trajectory represented by B-splines. We model dynamic obstacles and design a new optimization cost function. Currently, few methods can generate a trajectory avoiding moving obstacles, although most achieve high success rates in static environments We compare our method with the original planer in typical scenarios and the results show that our method is able to deal with dynamic obstacles more effectively.
Natural Language Classification Algorithm of Comments Based on Bayesian Chasing-Clustering Model

The clustering algorithm groups data objects according to their similarity. As an unsupervised learning, it is very convenient to use because there is no need to set labels for the data before training. But at the same time its clustering results are not attached with corresponding grouping information, which means the specific meaning of each type of data. However, in the field of modern Internet, the dimension of information space is very high, especially for user comment sections involving natural language. If the clustering direction is not induced and restricted, it is easy to appear that the clustering results are far from the goal of the algorithm. Therefore, we propose a Bayesian chasing-clustering model, which is improved on the basis of the k-means clustering algorithm. During the training process, it will tend to the clustering way we set in advance, and finally achieve a clustering effect which meets our goals better. In addition, we also propose the S-T method for word embedding representation, which is very suitable for natural language processing problems about comments. We applied it to film review websites and e-commerce platforms, and clustered the website content based on users' comments. Compared with traditional clustering methods, the accuracy rate was improved by 9%-57%.
Fuzzy-Optimal Online Control of a Mobile Robot in the Obstacle Avoidance Problem

The problem of avoiding obstacles by a mobile robot is considered for the case when the goals and constraints are not clearly specified. For a typical case, a comparative analysis of several fuzzy-optimal options for offline and online control in a minimax interpretation was performed. The brute force method, the branch and bound method, and the genetic algorithm are considered as alternative options for the robot navigation.
Variants of knowledge control in the distance learning system of a university

The article is devoted to the study of the distance learning system in the context of a modern university, aimed at the formation of a highly qualified personnel potential of the country. Particular attention is paid to consideration of the essence and specifics of distance learning, its separately taken technologies and resources. The system of monitoring the students’ knowledge has become the key object of the study, in the context of remote supplying the specialists of different levels and directions with professional training. The role of distance technologies in the process of regular quality control of students’ knowledge is comprehended and actualized, the main theses regarding the variants of control are revealed. The questionnaire method was defined as the main method of research, which was carried out in the conditions of the leading university of the Republic of Crimea - V.I. Vernadsky Crimean Federal University. The subject of the questionnaire was to identify the role of distance technologies in the knowledge control system, as well as to determine the most effective options for such control in a modern university. The respondents were the teaching staff of the University of various areas of training, degrees, forms and levels of education. The results of the study can be found in the final part of it.
Sonifying Data: For the Art, for the Science and for What Lies Between

Abstract To explore intersections of art and science is to acknowledge that there are differing degrees of knowing, understanding and experience. This article describes three data sonification projects the author worked on using datasets from researchers. The sonifications were created to be used within scientifically informed musical performances by Grateful Dead percussionist Mickey Hart. The projects posed particular design challenges for the author as he sought to create “data art” that had “sonification integrity.”
Textual Data Storage of Power Equipment Insulation Status Based on Graph Database

The textual data of insulation status contains the original records of the on-site partial discharge detection. In order to improve the data utilization rate and mine the relevant information that affects the insulation status of the equipment, it needs to be processed and stored uniformly. Knowledge graph technology is used to process the insulation status text data, and the insulation status text format is identified, and then the data storage based on the graph database is completed. By comparison with relational databases, it is found that graph databases have more efficient query performance for complex relationships. Graph database is more suitable for storing textual data of insulation status with complex relationships.
The Pronouns of the Odyssey and the Statistical Terms of Shan Hai Jing in Computer Search Technique

By means of computer search and retrieval, this paper is going to analyze the ratio of the pronouns in Homer’s epic The Odyssey and the statistical terms in ancient Chinese descriptive poem Shan Hai Jing (usually translated as Classic of Mountains and Rives). With the help of present computer science search technique, we figure out times of repetition of the target pronouns “he, his, him, I, my, me” in The Odyssey and the maximizing of the alternative frequency of statistical terms “names, figures, pronouns, nouns of position etc.” in Shan Hai Jing. The high repetition frequency of the target pronouns emphasizes “his experience and possession”, reflecting the hero experiencer identity. And then the high alternative frequency of the statistical terms emphasizes “my experience and possession”, suggesting the writer experiencer identity.
IMPLEMENTATION “SMART TOURISM” APPLICATIONS MADIUN CITY TOURISM AREA BASED ON MOBILE

This proposal is a form of research design which is recorded in writing to compile the design of tourism applications in the City of Madiun. Given the current rapid technological development, a new breakthrough is needed for the dissemination of information to the public. This research is aimed at the Madiun City government to facilitate the dissemination of Madiun City tourism information. This research uses the waterfall method because this method is often used in research and is a sequential method where the previous step must be completed first before moving to the next step. Starting from the analysis of needs data, design of application design, design, implementation, and maintenance. This application is made using the Javascript and PHPMyAdmin programming language which is based on a website whose solution will be converted into an Android application using the Website 2 APK Builder application and Coreldraw Application as an interface design design. With the application of Smart Tourism the Madiun City-based tourism area is based on mobile is expected to be an application that makes it easy for tourists to find information clearly attractions in the City of Madiun
Integrated Design of Local Trajectory Planning and Tracking Control for Autonomous Driving

This paper describes a real-time local trajectory planning and tracking control algorithm, based on the constrained optimization framework, applicable to autonomous vehicles operating in unstructured environment. The primary novelty is in use of closed-loop information prediction along the given global path in the framework of trajectory planning, i.e. reference velocities and trajectory points used in trajectory planning are generated by low-level tracking control. A continuous-curvature path-smoothing algorithm based on parametric cubic Bezier curves is firstly adopted to smooth the given global path, presented by an ordered sequence of waypoints. An obstacle-free dynamic-feasible trajectory planning problem is carefully formulated by a sequential convex optimization procedure. The solution trajectory points are used for feed-forward and feed-back tracking control. The proposed algorithm was at the core of the planning and control modules for an eight-wheel independent drive vehicle, which demonstrated the obstacle-avoidance ability in a task-based offroad autonomous driving test.
Skill Oriented Online Master’s Course “Neural Network Modeling of Complex Technical Systems”

In this paper we consider the application of online course “Neural Network modeling of Complex Technical Systems” in the Master’s degree programs in the field of nanotechnology and nanoengineering in Bauman Moscow State Technical University. The course has rather practical than theoretical nature. The aim of this course is skill oriented learning. Nowadays neural network models have become a powerful tool of scientific research for engineers and students. The methods studied during the study of the discipline can be applied to estimation, modeling, classification, clustering, forecasting and more. The neural networks modeling plays a significant role in Master’s education and student’s research work. Neural Networks models are successfully presented in graduation theses. Thanks to online educations students can practice at their own pace and study modern neural networks software products, methods of data preparing, designing and training neural network and then apply these algorithms in practice. According to the steps of neural network modeling algorithm the course consists of three main parts and conclusive one. In this paper course structure and study results are presented.
THE CHAIN FOUNTAIN

If a beaker is fully filled with a chain and an initial impulse is given on one end of the chain, then the chain inside the beaker starts coming out of the beaker and grows in height passes. After sometime, the velocity with which the chain comes out of the beaker becomes constant and depends only upon the height of the beaker and the height to which the chain rises.
On a New Property of the Steiner Inellipse

We consider an old problem of partitioning a triangle into three similar triangles and three parallelograms by lines parallel to the sides of the triangle through a given interior point M. In this note, we show that the locus of those points M for which the sum of the triangle areas is equal to the sum of the parallelogram areas is precisely the Steiner inellipse of the triangle.
Enhancing the Performance of Evolutionary Clustering by Genetic Sequence Resorting

As a global optimization technique, the evolutionary algorithms provide powerful solvers to the data clustering problem. However, the individual representations in evolutionary clustering algorithms exist redundancy and inconsistency problems, which not only wastes the search efforts but also disorders the mutual learning process between individuals. To address the problems, in this paper, we propose a genetic sequence resorting method. This method first identifies a reference parameter vector and then resorts the order of the genetic sequences in each individual according to their distance to the reference vector. In this way, the solutions represented by individuals are unified, which removes redundancy and enhances the efficiency of the mutual learning in the algorithm. Incorporating the above method, a new and generic evolutionary clustering framework is developed. Under this framework, we specifically design two algorithms: one for distance-based convex clustering and the other for density-based nonconvex clustering. Experiments show that our method can effectively improve the performance of evolutionary clustering algorithms on various datasets with both convex and nonconvex cluster shapes.
Key technologies of data monitoring for coal machinery equipment in the intelligent IOT environment

Coal machinery equipment plays an important role in the process of coal mining. It is of great significance for its safe, stable, reliable and healthy operation to carry out the research on data monitoring methods of coal machinery equipment. Aiming at the problems of repeated construction of monitoring and diagnosis system of coal machine equipment, serious “information island” phenomenon and poor expansion of transmission network, this study puts forward the data monitoring method of coal machine equipment based on the perception of Internet of Things (IoT), and focuses on the key technologies of data acquisition, data transmission, data processing and application of coal machine equipment, and establishes the overall structure of coal machinery equipment monitoring based on IoT. In the sensing layer, a cooperative information acquisition system for key components of coal machinery equipment based on ZigBee short-distance wireless communication technology is designed to realize real-time data acquisition. In the network layer, a long-distance wireless data transmission and reception platform based on 4G technology is established, and Message Queuing Telemetry Transport (MQTT) communication protocol is used to send the data to the cloud, the interface is reserved for future communication technology, the real-time transmission and storage of data are realized. In the middleware layer, the processing methods of heterogeneous data are proposed, and the preprocessing technology of big data is studied to realize data sharing. In the application layer, the application direction of coal machine equipment data monitoring is proposed. Based on the interactive technology, a humanized and visual fault monitoring and diagnosis platform is established, which verifies the above theories and methods. This study not only realizes the effective monitoring of coal machine equipment data, but also promotes the sharing of coal machine information, which provides a new idea for the construction of intelligent mine.
Modified Algorithms for Approximate MAP Estimation Using Continuous-Time Particle Filter and Edgeworth Series

The main goal of the paper is to describe new algorithms for the approximate solution of the optimal filtering problem by the MAP criterion using partial sums of the Edgeworth series. Proposed algorithms are based on two continuous-time particle filters. Necessary relations are given to take into account central moments of the posterior distribution up to the 6th order.
Comparison and Analysis several Wavelet from Daubechies Family in Image Compression

The need for quality images affects the large storage requirements and difficulties in transmission. Compression is a solution to the efficiency of storage and transmission. The Wavelet technique has the potential of providing high-quality compression. In this study, several wavelets from the Daubechies family were applied in image compression. Measurement of compression ratio, Mean Square Error (MSE), and peak signal-to-noise ratio (PSNR) determine the `acceptability' of the compression. Our test with the `Lena' image found that the compression ratio based on all wavelets gave almost identical results, but the db2 wavelet gave the smallest compression ratio and db36 gave the largest compression ratio. In terms of fidelity, DB5 gives the best results.
The Proposal of Customized Convolutional Neural Network Using for Image Blur Recognition

This paper describes one of the parts of our research topic devoted to the image recognition and computer vision. In the field of automation, it is not new, that industry lines are under controls of camera systems. It is necessary to have a clear and visible image for automatic detection of various damages, fails, alarms, objects and right in time control. We divided our research into several stages. In the first stage, we are dealing with the recognition of the blur in the image. We are dealing with classification problem whether the image is blured or in focus. In the initial steps of our research, we have created a dataset of images, which is divided into four categories. In this paper we describe suitable methods to solve this task, we discuss the pros and cons and we also propose our own classification model based on artificial neural networks.
Tetris is NP-hard even with <i>O</i>(1) Rows or Columns

We prove that the classic falling-block video game Tetris (both survival and board clearing) remains NP-complete even when restricted to 8 columns, or to 4 rows, settling open problems posed over 15 years ago. Our reduction is from 3-Partition, similar to the previous reduction for unrestricted board sizes, but with a better packing of buckets. On the positive side, we prove that 2-column Tetris (and 1-row Tetris) is polynomial. We also prove that the generalization of Tetris to larger k-omino pieces is NP-complete even when the board starts empty, and even when restricted to 3 columns or 2 rows or constant-size pieces. Finally, we present an animated Tetris font.
Polarization-entangled photon pairs generation via interference of nonorthogonal quantum states

We report a new method for generation of polarization-entangled photon pairs in maximally entangled polarization states. Our approach is based on quantum interference between two polarization states which are non-orthogonal. We present necessary and sufficient conditions for generation the photon pair in a maximally entangled state. The effect of dependence of entanglement on the phase between the interfering states of photons is investigated theoretically and demonstrated experimentally with quantum states produced via spontaneous parametric down-conversion in the double-crystal scheme.
Proposal for a System Model for Offline Seismic Event Detection in Colombia

This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system.
3D Area Monitoring Using Group Control Technique and Optimization Algorithms

The paper considers the monitoring problem for non-determined 3D environment using vehicles group. The task for vehicles group include 3D area monitoring with distribution width of robots and keeping inside the desired region. Path planning of vehicles group based on data from the sensor system output and does not require mapping. There are two methods of group control offered in this paper for task solution. First method based on Delaunay triangulation in 3D using optimization algorithms. Second method also based on Delaunay triangulation using minimax optimization of distance between vehicles and obstacle/neighbors. The results of simulation for each method using Matlab represented in this paper. These results will be applied for unmanned aerial vehicle group movement research.
VLASTWA: a vocabulary learning and strategy teaching web-app

This research study tends to evaluate the efficaciousness and usability of a Vocabulary Learning And Strategy Teaching Web-App (VLASTWA) – a customised and targeted web-app for (1) teaching the KeyWord Method (KWM) which is a widely investigated vocabulary learning strategy, and (2) facilitating new vocabulary learning via KWM. In this experimental study, with a between/within design, native Persian participants (n=160, age:18-60) learnt to use KWM, applied it in acquiring 22 new words, and tested this newly learnt vocabulary immediate (T1)/delayed (T2) recall. The effectiveness of the use of KWM taught within the web-app and the traditional Pen and Paper (P&amp;P) in the experimental groups were compared with the control app and P&amp;P groups via immediate/delayed recall of learnt vocabulary. The results suggest VLASTWA was efficient for learning new vocabulary while highlighting how meaningful and interactive it can be in accompanying and enriching Foreign Language (FL) vocabulary learning.