Reviewerly/exHarmony huggingface dataset:
Determining Countability Classes

This paper provides a corpus-driven investigation into establishing classes of nouns based on grammatical environments relevant to countability, such as combination with cardinal modifiers or appearing as a bare singular. We investigate the countability environments of Allan (1980) and assess their predictive power across a large corpus (350 million words). We show, by applying machine learning methods, that while the environments Allan (1980) distinguishes are predictive, the occurrence of nouns as bare singular and/or bare plural is substantially more powerful as a diagnostic. Using the most important environments, we induce, through automatic clustering, a set of countability classses, which distinguish between varieties of countable, non-countable and pluralia tantum nouns.
Examining the level of motivation of foreign students during studying in Kazakhstan

The article deals with the problem of motivation in the student sphere. If we talk about the motivation of students, it represents the processes, methods and means of inducing them to cognitive activity, active development of the content of education. As motives can act in conjunction emotions and aspirations, interests and needs, ideals and attitudes. Therefore, motives are complex dynamic systems in which choice and decision-making, analysis and evaluation of choice are carried out. Motivation for students is the most effective way to improve the learning process. Motives are the driving forces of the learning process and assimilation of the material. Motivation for learning is a rather complicated and ambiguous process of changing the attitude of the individual, both to a separate subject of study and to the entire educational process. Motivation is the main driving force in human behavior and activity, including in the process of forming a future professional. Therefore, the question of incentives and motives of educational and professional activity of students becomes especially important.
Feature-Based Overview of Online Comments of Web-Based Healthcare Products

The increasing accessibility and prevalence of opinion-rich tools, such as the review sites for the healthcare products offered online, render it impossible for consumers to select the best product from a vast range of items. The amount of consumer opinions accessible for fashionable items can be thousands. Both reviews are challenging for consumers to interpret, and if they read just a handful of these reviews, they will have a selective perception of the product. Product manufacturers may often have trouble maintaining, monitoring, and recognizing the consumers’ opinions on the goods. Several study works have in the past been suggested for solving these problems, but they have several limitations: the structures introduced are entirely invisible and the feedback are not so easy to perceive and require longer to evaluate since, aside from individual feature opinions, the function-based description system applied is broader than those used for examination. Here, we suggested a dynamic framework for the summary of consumer views on online healthcare products centered on functionality that works according to the product domain. Each period after extraction, we conduct the following work: Initially, the recognition of the characteristics of the product from the views of customers is carried out. Their respective views are then derived for each feature and their alignment or (negative/positive) polarity is identified. Finally, feature-based analytics were summarized by taking the corresponding extracts from each feature opinion and putting them in their respective cluster-based features. These type of feature-based extracts shall be absorbed easily by the end user.
Early Action Prediction Using 3DCNN With LSTM and Bidirectional LSTM

Predicting and identifying suspicious activities before hand is highly beneficial because it results in increased protection in video surveillance cameras’. Detecting and predicting human's action before it is carried out has a variety of uses like autonomous robots, surveillance, and health care. The main focus of the paper is on automated recognition of human actions in surveillance videos. 3DCNN (3 Dimensional Convolutional Neural Network) is based on 3D convolutions, there by capturing the motion information encoded in multiple adjacent frames. The 3DCNN is combined with Long short team memory (LSTM) and Bidirectional LSTM for prediction of abnormal events from past observations of events in video stream. It is observed that 3DCNN with LSTM resulted in increased accuracy compared to 3DCNN with Bidirectional LSTM. The experiments were carried out on UCF crime Dataset.
Helsinki deblur challenge 2021: Description of photographic data

Image deconvolution is a classical inverse problem that serves well as a computational test bench for reconstruction algorithms. Namely, the direct operator can be modelled in a straightforward way either by convolution or by multiplication in the frequency domain. Further, the ill-posedness of the inverse problem can be adjusted by the form of the point spread function (PSF). An open photographic dataset is described, suitable for testing practical deconvolution methods. The image material was designed and collected for the Helsinki Deblur Challenge 2021. The dataset contains pairs of images taken by two identical cameras of the same target but with different conditions. One camera is always in focus and generates sharp and low-noise images, while the other camera produces blurred and noisy photos as it is gradually more and more out of focus and has a higher ISO setting. The data is available here: https://doi.org/10.5281/zenodo.4916176
Language Understanding for Field and Service Robots in a Priori Unknown Environments

Contemporary approaches to perception, planning, estimation, and control have allowed robots to operate robustly as our remote surrogates in uncertain, unstructured environments. This progress now creates an opportunity for robots to operate not only in isolation, but also with and alongside humans in our complex environments. Realizing this opportunity requires an efficient and flexible medium through which humans can communicate with collaborative robots. Natural language provides one such medium, and through significant progress in statistical methods for natural-language understanding, robots are now able to interpret a diverse array of free-form navigation, manipulation, and mobile-manipulation commands. However, most contemporary approaches require a detailed, prior spatial-semantic map of the robot’s environment that models the space of possible referents of an utterance. Consequently, these methods fail when robots are deployed in new, previously unknown, or partially-observed environments, particularly when mental models of the environment differ between the human operator and the robot. This paper provides a comprehensive description of a novel learning framework that allows field and service robots to interpret and correctly execute natural-language instructions in a priori unknown, unstructured environments. Integral to our approach is its use of language as a “sensor”—inferring spatial, topological, and semantic information implicit in natural-language utterances and then exploiting this information to learn a distribution over a latent environment model. We incorporate this distribution in a probabilistic, language grounding model and infer a distribution over a symbolic representation of the robot’s action space, consistent with the utterance. We use imitation learning to identify a belief-space policy that reasons over the environment and behavior distributions. We evaluate our framework through a variety of different navigation and mobile-manipulation experiments involving an unmanned ground vehicle, a robotic wheelchair, and a mobile manipulator, demonstrating that the algorithm can follow natural-language instructions without prior knowledge of the environment.
Development and testing of a model of unified funds of assessment materials for reviewing subject and methodological competencies of biology teachers

The article is devoted to the problem of development and testing of diagnostic tools for monitoring the ability of teachers of general educational institutions to solve professional and pedagogical problems. The model of unified fund of assessment materials (UFAM) is aimed at reviewing subject and methodological competencies of biology teachers. It provides examples of tasks in biology with the methods of teaching it. A brief analysis of the model review results has been provided.
Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills

This study developed a fuzzy logic and Gagné learning hierarchy (FL-GLH) for assessing mathematics skills and identifying learning barrier points. Fuzzy logic was used to model the human reasoning process in linguistic terms. Specifically, fuzzy logic was used to build relationships between skill level concepts as inputs and learning achievement as an output. Gagné learning hierarchy was used to develop a learning hierarchy diagram, which included learning paths and test questions for assessing mathematics skills. First, the Gagné learning hierarchy was used to generate learning path diagrams and test questions. In the second step, skill level concepts were grouped, and their membership functions were established to fuzzify the input parameters and to build membership functions of learning achievement as an output. Third, the inference engine generated fuzzy values by applying fuzzy rules based on fuzzy reasoning. Finally, the defuzzifier converted fuzzy values to crisp output values for learning achievement. Practical applications of the FL-GLH confirmed its effectiveness for evaluating student learning achievement, for finding student learning barrier points, and for providing teachers with guidelines for improving learning efficiency in students.
DINAMIKA PEMBELAJARAN DARING MELALUI BELAJAR DARI RUMAH (BDR) PADA PAUD MIFTAHUL ULUM SAAT PANDEMI COVID 19

The purpose of this study was to determine and analyze the dynamics of online learning through learning from home (LFH) in early childhood in Miftahul Ulum. Namely, it can be seen from 1) The Role of Parents in Early Childhood Education (PAUD) Online Learning from Home, and 2) Problems of online learning through learning from home. The method used in this research is qualitative with the object of research for early childhood students and parents who accompany online learning through learning from home. The results of the study found that the dynamics of online learning through learning from home were not easy for both teachers, students and parents. There are many obstacles faced, both in terms of the role of parents in accompanying or accompanying students at home and from the side of the teacher who are constrained in the process of observation and assessment for students.
Methodological features of using elements of distance learning in the training of competent specialists

Abstract The paper considers the problem of using distance learning of students of engineering and chemical education in the framework of the digitalization of the learning system. Authors consider distance learning of profile disciplines as an element of a personality-oriented approach in the context of professional self-education and as a form of preparation for future professional activity. The rationale for the feasibility of variable forms of a holistic distance learning system based on methodological support is presented, the basic principles for constructing educational trajectories are determined. The authors present some results of a pedagogical experiment over a seven-year study period, confirming the effectiveness of the developed distance learning technology.
Robust Image Denoising with Texture-Aware Neural Network

Image denoising is a well-studied yet still hot research topic in the image processing community. Recently, image denoising with deep neural networks has achieved superior performance, however they can not recover tiny details from noisy images. Motivated by this problem, we propose a Texture-Aware Neural Network named TANet, which is composed of main network part with attention mechanism, residual structure and Texture-Aware Modular. Proposed Texture-Aware Modular owns dual paths, denoised image from main denoising network and clean image are input different path respectively. From Texture-Aware Modular, we get two sets intermediate codes and calculate corresponding perceptual loss. This perceptual loss is designed to generate auxiliary super-vision for tiny detail recovery from mixed residual details and noise set. Extensive experimental results demonstrate that the proposed TANet is on a par with the state-of-the-art denoising methods.
M16 Artificial Intelligence in Building Predictive Models of Acute Kidney Injury After Cardiac Surgery

Acute Kidney Injury (AKI) is one of the most frequent adverse consequences of cardiopulmonary bypass (CPB). Mean Arterial Pressure over time has been previously described by our group as a contributor. Other mechanisms and risk factors may exacerbate the risk. We used an artificial intelligence (AI) based approach to build a predictive model.
Study of Simulation Environment of Cloud Broker and Classification of Cloud Service Broker

Cloud broker could be generally defined as one of the intermediaries between the users and cloud providers to offer a service of integration, customization and aggregation of cloud services in CC. The first known cloud broker was introduced in 2008 with the name CloudSwitch which provides federated services only for Amazon EC2. The cloud in this broker is secure and seamless. Secondly, the cloud broker is introduced in 2011 with the name of the RightScale which offers a cloud management application and platform across multiple clouds in CC. Recently, CloudSim and Cloud Analyst are taking a great place in the CC simulator. The responsibilities of the cloud broker carry the important task first. Due to this, cloud consumers can easily select the cloud service provider to meet their requirement. Paper is categorized into two parts; first is related to complete cloud brokering architecture and second elaborate the simulation environment, CloudSim and Cloud Analyst.
Path Planning using Neural A* Search

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off. Furthermore, Neural A* successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs. Project page: this https URL
Pedagogical and psychological features of the formation of the creative activity of future teachers through personality-oriented education

Changes in society, modern requirements for professional knowledge and personal qualities of specialists have created the need to update the content of education in pedagogical universities, innovative forms and methods of teaching, the widespread introduction of modern information technologies into practice. Another important aspect of the process of professional adaptation of future educators is to determine the set of necessary conditions for the acquisition and full implementation of professional knowledge, skills and competencies in these areas.
A Study on Consideration Factors of Planning and Operating Online Convention

The purpose of this study is to elicit essential factors that need to be considered in planning and operating online conventions and analyzing relevance and priority. To do this, consideration factors were analyzed by preliminary research, opinions from experts in MICE industry and AHP questionnaires. The consideration factors were the six categories above (i.e., participant''s personal experience, program planning, event operation, technicality, online convention service, and hardware) in addition to the 25 sub-categories below are formulated. The responders included 41. As a result of analyzing the relative importance of the six categories, the program planning was considered the most important and followed the participant''s personal experience, event operation, technicality, online convention service, and hardware, in order. Such a result suggests that planning a online-oriented program is the most important criteria. In addition, the five most important sub-categories were a creative content specialized for online, solutions to technical problems, utilizing stable connection for the video conference, utilizing application to encourage participation, and minimizing churn rates are the top 5 out of 25 sub-categories.
Svrat: A Skeleton-Based Intelligent Monitoring System for Violence Recognition and Abuser Tracking

Recently, intelligent monitoring technologies have been developing rapidly, among them, human action recognition and target tracking have made breakthroughs in their respective fields. However, the huge computational cost makes the integration of the two technologies difficult. In this paper, we design a skeleton-based monitoring system that realizes violent action recognition and abuser tracking with a relatively low overall complexity. For violence recognition, we put forward a novel Skeletal Context Attention Network (SCAN), which is lightweight yet effective to exploit spatial and temporal representations of skeleton data. For abuser tracking, we present a Skeleton-Guided Correlation Filter (SGCF) that can track a perpetrator continuously even in some extreme cases, such as drastic changes in speed or color. Experiments on two benchmark datasets show that the proposed system not only outperforms the existing state-of-the-art methods for violent action recognition and abuser tracking, but also implements the both tasks with less computation.
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. We provide a theoretical analysis using the PAC-Bayesian framework and derive novel generalization bounds for meta-learning. Using these bounds, we develop a class of PAC-optimal meta-learning algorithms with performance guarantees and a principled meta-level regularization. Unlike previous PAC-Bayesian meta-learners, our method results in a standard stochastic optimization problem which can be solved efficiently and scales well. When instantiating our PAC-optimal hyper-posterior (PACOH) with Gaussian processes and Bayesian Neural Networks as base learners, the resulting methods yield state-of-the-art performance, both in terms of predictive accuracy and the quality of uncertainty estimates. Thanks to their principled treatment of uncertainty, our meta-learners can also be successfully employed for sequential decision problems.
Volcanic Hot-Spot detection using SENTINEL-2: results from the comparison with MODIS-MIROVA thermal signals.

&amp;lt;p&amp;gt;In the satellite thermal remote sensing, the high-spatial resolution sensors may improve thermal constraining of volcanic phenomena, with direct implications on the comprehension of volcanic processes and monitoring purposes. Here we present a new hot-spot detection algorithm, developed for SENTINEL 2 (S2) data, which combines contextual spectral and spatial analysis, applied on the 8a-11-12 SWIR bands with 20 meters/pixel resolution. The algorithm is able to detect and count the number of hotspot-contaminated pixels (S2Pix), in a wide range of environments and for several types of volcanic activities. The S2-derived thermal trends, retrieved at different worldwide key-cases volcanoes, are than compared with the Volcanic Radiative Power (VRP) from MODIS images processed by the MIROVA system during the period 2016-2019. Dataseries showed an overall excellent correlation between the two imagery suites, enhancing the higher sensitivity of SENTINEL-2 to detect small size and subtle, low-temperature thermal signals. Results outline a relation between the S2Pix and VRP ratios and the volcanic processes (i.e. lava flows, domes, lakes, open-vent activity) producing a distinct pattern in terms of size and intensity of the thermal anomaly. Moreover, the high-spatial resolution of S2 imagery potentiality let to decrypt which is the thermal contribution of the different active volcanic portions, and to understand their evolution in terms of intensity and persistence. Our analysis indicates how the combination of high- (S2) and moderate- (MODIS) resolution thermal timeseries represent an improvement in the space-based volcano monitoring that can be useful for monitoring applications and communities which relate with active volcanoes.&amp;lt;/p&amp;gt;
Highly Efficient and GPU-Friendly Implementation of BFS on Single-node System

With the development of computer technology, the data generated in our daily life is growing rapidly. Data-intensive algorithms take more and more important part in high performance computing. The Breadth-First Search (BFS) is a typical data-intensive algorithm and characterized with intensive irregular memory access, low computation intensity and strong data dependency. Although Graphics Processing Unit (GPU) offers massive parallelism, BFS is not GPU-friendly due to above characteristics. To utilize the power of GPU for BFS, efficient scheduling of massive threads and better utilization of GPU memory hierarchy are required. In this paper, we focus on highly efficient implementation of BFS on NVIDIA GPU platform. We propose three optimization techniques, including fine-grained parallelism, GPU-oriented CSR structure and vertex quick-search to overcome performance bottlenecks in GPU. Fine-grained parallelism can improve the workload balance for parallel implementation in top-down stage. GPU-oriented CSR structure employs a GPU-friendly data layout, which can improve the efficiency of memory access. Vertex quick-search is proposed to reduce redundant graph computations on GPU. Finally, we conduct extensive experiments on GPU-based platform to verify the effectiveness of these techniques. We achieve 237.94 GTEPS for the Kronecker graph with 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">26</sup> vertices and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sup> edges. In terms of energy efficiency, our implementation ranks 1st place on the November 2019 Green Graph500 list.