Reviewerly/exHarmony huggingface dataset:
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequately capture the diversity of human behavior since they assume that all behavior in a given dataset is generated by a single cost function.In this paper, we propose a probabilistic IRL framework that directly learns a distribution of cost functions in continuous domain. Evaluations on both synthetic data and real human driving data are conducted. Both the quantitative and subjective results show that our proposed framework can better express diverse human driving behaviors, as well as extracting different driving styles that match what human participants interpret in our user study.
Estimating marginal likelihoods from the posterior draws through a geometric identity

Abstract This article develops a new estimator of the marginal likelihood that requires only a sample of the posterior distribution as the input from the analyst. This sample may come from any sampling scheme, such as Gibbs sampling or Metropolis–Hastings sampling. The presented approach can be implemented generically in almost any application of Bayesian modeling and significantly decreases the computational burdens associated with marginal likelihood estimation compared to existing techniques. The functionality of this method is demonstrated in the context of probit and logit regressions, on two mixtures of normals models, and also on a high-dimensional random intercept probit. Simulation results show that the simple approach presented here achieves excellent stability in low-dimensional models, and also clearly outperforms existing methods when the number of coefficients in the model increases.
CODE2VEC Based Cognitive Agent System to Retrieve Relevant Code Component from Repository

The cognitive agent system helps to retrieve most relevant code component by introducing latest techniques. In this paper the authors used latest approach of code embedding which undergoes code2vec tokenization model by tokenizing and converting the code components present in the dataset into a numeric representation to create a input for neural network environment and also implemented cosine similarity matching technique to acquire the relevancy and perform retrieval of code component.
When Hardness of Approximation Meets Hardness of Learning

A supervised learning algorithm has access to a distribution of labeled examples, and needs to return a function (hypothesis) that correctly labels the examples. The hypothesis of the learner is taken from some fixed class of functions (e.g., linear classifiers, neural networks etc.). A failure of the learning algorithm can occur due to two possible reasons: wrong choice of hypothesis class (hardness of approximation), or failure to find the best function within the hypothesis class (hardness of learning). Although both approximation and learnability are important for the success of the algorithm, they are typically studied separately. In this work, we show a single hardness property that implies both hardness of approximation using linear classes and shallow networks, and hardness of learning using correlation queries and gradient-descent. This allows us to obtain new results on hardness of approximation and learnability of parity functions, DNF formulas and $AC^0$ circuits.
A context and semantic enhanced UNet for semantic segmentation of high-resolution aerial imagery

Abstract Semantic segmentation of high-resolution aerial images is of paramount importance in a wide range of remote sensing applications. The ever-increasing spatial resolution of aerial imagery brings about two specific challenges that incur labelling ambiguities: intra-class heterogeneity and inter-class homogeneity. To address these two challenges, a novel end-to-end semantic segmentation network for high-resolution aerial imagery, namely Context and Semantic Enhanced UNet (CSE-UNet), is proposed in this paper. Specifically, we exploit multi-level Receptive Field Block (RFB) based skip pathways to enhance the representational power of multi-scale contextual information, and therefore tackle the issue of intra-class heterogeneity. To solve the inter-class homogeneity problem, we propose a dual-path encoder where an auxiliary multi-kernel based feature encoding path is embed to produce strong semantic features at all levels to enlarge the inter-class differences. Experimental results shows that our proposed CSE-UNet achieves competitive performance and outperforms UNet and several other deep networks on the ISPRS Potsdam and Vaihingen datasets.
CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing

Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing didn't consider how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.
Size of corpora and collocations: The case of Russian

With the arrival of information technologies to linguistics, compiling a large corpus of data, and of web texts in particular, has now become a mere technical matter. These new opportunities have revived the question of corpus volume that can be formulated in the following way: are larger corpora better for linguistic research or, more precisely, do lexicographers need to analyze bigger amounts of collocations? The paper deals with experiments on collocation identification in low-frequency lexis using corpora of different volumes (1 million, 10 million, 100 million and 1.2 billion words). We have selected low-frequency adjectives, nouns and verbs in the Russian Frequency Dictionary and tested the following hypotheses: 1) collocations in low-frequency lexis are better represented by larger corpora; 2) frequent collocations presented in dictionaries have low occurrences in small corpora; 3) statistical measures for collocation extraction behave differently in corpora of different volumes. The results prove the fact that corpora of under 100 M are not representative enough to study collocations, especially those with nouns and verbs. MI and Dice tend to extract less reliable collocations as the corpus volume extends, whereas t-score and Fisher’s exact test demonstrate better results for larger corpora.
To The Question Of The Use Of Digital Technologies In Educational Institutions

One of the priority areas for the development of education in the Russian Federation is the introduction of digital technologies at all levels of education, starting from the preschool age. Digitalization of society will be possible only if various educational systems of all levels are ready to switch to new educational information systems. Informatization of education has created the basis for a transition to a new level; digitalization is aimed at training specialists who are guaranteed to be in demand on the labor market, are fluent in mobile and Internet technologies, and are also focused on continuous learning (advanced training) through e-learning. The role of the teacher is fundamentally changing, and, in connection with this, the forms of his work with students are changing. The teacher no longer just presents information, he teaches digital technologies, teaches to navigate in the information space, including not only the skills of working with digital technologies, but also the ability to organize new communication through new computer. Speaking of the introduction of a model of digital educational environment, it is necessary to have an idea of if the education system is ready for this today.
Motivation Of Students To Project Activities In A Modern University

The relevance of the study is determined by the need of both theoretical and practical training of students in a modern university. An important component of the practical preparation of the bachelor is the project activity. The purpose of the article is to study the motivation of bachelors to project activities in intersubject project groups as part of the educational process. The main research method was the survey and questionnaire of first-year students who first carry out project activities at the university. The article reveals the problem of the need to consider the leading students' motivation for project activities, empirical data on the motivation of university students to develop projects, their ideas about the possibilities and concerns about the implementation of project activities at the university are presented. The authors proceed from the assumption that it is necessary to consider students' ideas about the possibilities and shortcomings of project activities when organizing students' work on projects. The students' attitude to project activities and the conditions that allow it to be effectively implemented at the very early stages of student learning are revealed. These include the following: supporting the initiative of the students themselves in choosing the topic of projects, developing teamwork skills, taking into account the complexity of the project work, without affecting the main educational activities of students.
Cooperative Pursuit Control Based on Networked Visual Motion Observer in Three Dimensions

This paper proposes a vision-based target pursuit control based on cooperative estimation of target motion. A vision-based observer using visual sensor networks, so called Networked Visual Motion Observer, is presented in one of the authors' previous work to estimate three dimensional target motion, i.e., translational and rotational motion. We first introduce rigid body motion and camera model, and formulate the problem to be considered. Then, we propose a novel observer-based pursuit control law based on motion estimation. We next show that all rigid bodies with a visual sensor converge to each desired relative pose with respect to the target when the target is static. Furthermore, the pursuit performance for a moving target is evaluated based on L2 stability. Finally, we demonstrate the effectiveness of the proposed control law through simulations and experiments.
Research on China Academic Library and Information System

China Academic Library & Information System (CALIS) is the world's largest university library consortium, which is responsible for resource coordination and joint construction, document information sharing services, and staff training, application system construction, etc.It uses cloud computing (CC) technology to build a platform for cloud services and sharing, which is a model for the development of China's university library alliances (CULA) in the past 30 years, and has played an important role in promoting the CULA development, which has independently designed and developed an online cooperative cataloging system and an online public retrieval (OPAC) system for the retrieval of the joint catalogue of Chinese and foreign books and periodicals.This article mainly adopts the method of case analysis to analyze the development process, organizational structure, operation mode, and development trend of CALIS, with the purpose of revealing the general laws of the CULA construction.
Investigation of the Present Situation of Intelligent APP in College Students’ Vocabulary Learning

This research adopts the method of literature survey method, investigation method and interview method, etc. Through literature research method, this thesis combs and summarizes the research of mobile vocabulary learning at home and abroad. Based on the multimodal discourse theory and the second language acquisition theory, the author investigates the learning attitude, habits and strategies on vocabulary of college students and collects their opinions on learning words by using Bai Ci Zhan App through questionnaire method. And the author investigates the current situation and existing problems of using mobile Apps to study vocabulary. Intelligent App application in college English vocabulary learning can effectively enlarge students' vocabulary breadth and depth and motivate their interest in English learning.
Error-Based Noise Filtering During Neural Network Training

The problem of dealing with noisy data in neural network-based models has been receiving more attention by researchers with the aim of mitigating possible consequences on learning. Several methods have been applied by some researchers to enhance data as a pre-process of training while other researchers have attempted to make models of learning aware of noise and thus able to deal with noisy instances. We propose a simple and efficient method that we call Error-Based Filtering (EBF) that is used during training as a filtration technique for supervised learning in neural network-based models. EBF is independent of the model architecture and can therefore be involved in any neural network-based model. Our approach is based on monitoring and analyzing the distribution of values of the loss (error) function for each instance during training. In addition, EBF can be integrated with semi-supervised learning to take advantage of the identified noisy instances and improve classification. An advantage of EBF is to achieve competitive performance compared with other state-of-the-art methods with many fewer additional tasks in a procedure of training. Our evaluation of the efficacy of our method on three well-known benchmark datasets demonstrates an improvement on classification accuracy in the presence of noise.
Multi-modal Network Representation Learning

In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.
Parametric Identification Based on the Adaptive Unscented Kalman Filter

The detailed adaptive unscented Kalman filter algorithm is provided.Step-by-step schemes of filtering algorithms used for the software development are given.Nonlinear filtering algorithm efficiency is investigated with considering an example of a nonlinear continuous-discrete model.The statistic estimator based on the continuous-discrete adaptive unscented Kalman filter with noise is proposed for the nonlinear system parameters estimation.The solution to the problem of solar radiation parameters estimation based on the maximum likelihood method and the adaptive unscented Kalman filter is shown.The obtained results lead to significant improvement of satellite trajectory prediction quality.
REGARDING THE BIOPOLYMERS HEAT STABILITY FORMATION

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология жəне техникалық ғылымдар сериясы" ғылыми журналының
Document Similarity from Vector Space Densities

We propose a computationally light method for estimating similarities between text documents, which we call the density similarity (DS) method. The method is based on a word embedding in a high-dimensional Euclidean space and on kernel regression, and takes into account semantic relations among words. We find that the accuracy of this method is virtually the same as that of a state-of-the-art method, while the gain in speed is very substantial. Additionally, we introduce generalized versions of the top-k accuracy metric and of the Jaccard metric of agreement between similarity models.
Application of GPS Positioning Technology in Civil Engineering Survey

Abstract In the process of continuous improvement of my country’s scientific and technological level, the development and application of new technologies are relatively fast, and the application of new technologies in various fields is relatively extensive. The full application of GPS positioning technology in the surveying and mapping line is of great significance, which can greatly improve the efficiency and accuracy of civil engineering surveying. With the continuous development and application of GPS measurement technology, it can promote the further development of my country’s engineering measurement industry. In the actual operation of GPS positioning technology, the signals sent by the satellites can be obtained in time, and then the signal information is processed to accurately determine the specific spatial location of the measurement point. The three-dimensional navigation performance and positioning function of GPS positioning technology is relatively strong, with strong anti-interference ability, and can be fully applied in the measurement process of various civil engineering. In order to improve the application effect of GPS positioning technology, we need to further study the GPS positioning technology and discuss the specific application of mechanical positioning technology in the civil engineering measurement process, so as to provide corresponding reference for the popularization and application of GPS positioning technology.
Empirical Evidence for a Descriptive Model of Principles of Information Security Course

The purpose of this study is to examine the nature and content of the rapidly evolving undergraduate Principles of Information/Cybersecurity course which has been attracting an ever-growing attention in the computing discipline, for the past decade.More specifically, it is to provide an impetus for the design of standardized principles of Information/Cybersecurity course.To achieve this, a survey of colleges and universities that offer the course was conducted.Several schools of engineering and business, in universities and colleges across several countries were surveyed to generate necessary data.Effort was made to direct the questionnaire only to Computer Information System (CIS), Computer Science (CS), Management Information System (MIS), Information System (IS) and other computer-related departments.The study instrument consisted of two main parts: one part addressed the institutional demographic information, while the other focused on the relevant elements of the course.There are sixty-two (62) questionnaire items covering areas such as demographics, perception of the course, course content and coverage, teaching preferences, method of delivery and course technology deployed, assigned textbooks and associated resources, learner support, course assessments, as well as the licensure-based certifications.Several themes emerged from the data analysis: (a) the principles course is an integral part of most cybersecurity programs; (b) majority of the courses examined, stress both strong technical and hands-on skills; (c) encourage vendor-neutral certifications as a course exit characteristic; and (d) an end-of-course class project, remains a standard requirement for successful course completion.Overall, the study makes it clear that cybersecurity is a multilateral discipline, and refuses to be confined by context and content.It is envisaged that the results of this study would turn out to be instructive for all practical purposes.We expect it to be one of the most definitive descriptive models of such a cardinal course, and help to guide and actually, shape the decisions of universities and academic
Professional Learning Communities Of Teachers As Reflexive Practice

The article is devoted to the phenomenon of building a professional learning community of teachers as a reflective teaching practice within school itself. The main characteristics of a professional learning community of teachers are substantiated and described: values shared by all teachers, focus on improving students’ educational results, orientation towards the development of pedagogical leadership, support culture expressed in respect and trust in each other, collective nature of actions based on collaboration and cooperation, exploratory nature of the community’s activity, single information space, simple and understandable research tools that all teachers use; ongoing professional communication, vertical and horizontal connections, construction of matrix networks, open and public nature of the community. The authors of the article defined reflective practice as a key tool for professional development of a teacher. Characteristic features of reflective practice are: equal positions, tasks for self-reflection and mutual evaluation, reflective position of participants; object of reflection. Speaking about the improvement of teaching and learning, the authors noted that reflective practice is not a single action, but a cycle of continuous learning. Research has shown that building a professional learning community through methodology based on teachers ‘mutual attendance of lessons with subsequent reflection allows the participants to focus on certain elements that facilitate deep insight into their practice in the context of professional activity when solving specific pedagogical problems.
Asymmetric color image encryption using singular value decomposition and chaotic Tinkerbell map in fractional Fourier domain

An asymmetric single-channel color-image cryptosystem is presented that uses singular value decomposition and Tinkerbell map in the fractional Fourier domain. The input image is split into red, green, and blue channels. The red component is used as plaintext, and green and blue components are used as phase masks in the cryptosystem. MATLAB is used for computations of results presented in this paper. The proposed cryptosystem is validated for color images of size 256 × 256 × 3 pixels. The analysis of the attacks establishes the scheme’s resistance to occlusion and noise attack, and the key-space is big enough to endure a brute-force attack. The analysis based on the correlation distribution of neighboring pixels, along with statistical analysis has been performed to examine the efficacy of the cryptosystem. The results indicated high levels of security possessed by the proposed encryption scheme.