Reviewerly/exHarmony huggingface dataset:
Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.
Digital Competence of Urban and Rural Residents (Generational Aspect)

Individuals and social groups, including generational groups, differ in their level of knowledge of digital technologies and networks. We are talking about the problem of sufficient or insufficient adaptation of representatives of various social and age-generational groups to digital technologies (the problem of adaptability to digital technologies). This problem is becoming more acute as more sophisticated computer programs and tools become available. The older group is very far behind the other groups in terms of computer proficiency; for example, two-fifths of this group admit that they do not know computer technology when forming digital competence among people, it is also important to keep in mind the solution of a broader and more complex task—the formation of a digital culture. Digital culture implies the widespread use of digital technologies in the daily lives of people in general (and at the same time-the necessary and sufficient adaptation to digital technologies), and not only in narrow sectors of educational and work activities. As a kind of program goal—a society with a high and inclusive digital culture.
ISTIC’s Neural Machine Translation System for CCMT’ 2021

This paper demonstrates an overview and the technical details of the neural machine translation system developed by the Institute of Scientific and Technical Information of China (ISTIC) for the 17th China Conference on Machine Translation (CCMT’ 2021). ISTIC participated in the following four machine translation (MT) evaluation tasks: MT task of Mongolian-to-Chinese daily expressions, MT task of Tibetan-to-Chinese government documents, MT task of Uyghur-to-Chinese news, and MT task of Russian-to-Chinese in low resource languages. Our system is based on Transformer architecture and several effective strategies are adopted to improve the quality of translation, such as corpus filtering, back translation, data augmentation, context-based system combination, model averaging, model ensemble, and reranking. The paper presents the system performance under different parameter settings.
Comparative Analysis between InceptionResnetV2 and InceptionV3 for Attention based Image Captioning

Generating a caption from an image is a challenging task that involves combining the concepts of computer vision and natural language processing which has motivated the researchers to model an automated process and in recent times multitude of approaches have been introduced which specifically target this problem. This paper solves the problem of learning the relationship between image and the language by establishing an entity-object relationship that maintains continuity in the caption. This is achieved by using an attention mechanism which allows the decoder to target specific regions at different time steps. To understand the image content, we use an encoder which extracts information about the different objects and the relationships between them. This paper evaluates and compares two models that incorporate two different encoders i.e., InceptionResnetV2 and InceptionV3, both having attention unit along with LSTM as the decoder. Experiments on the MS-COCO dataset show that the models generate sensible and accurate captions in a majority of cases.
Quantitative Hilbert Irreducibility and Almost Prime Values of Polynomial Discriminants

Abstract We study two polynomial counting questions in arithmetic statistics via a combination of Fourier analytic and arithmetic methods. First, we obtain new quantitative forms of Hilbert’s Irreducibility Theorem for degree $n$ polynomials $f$ with $\textrm {Gal}(f) \subseteq A_n$. We study this both for monic polynomials and non-monic polynomials. Second, we study lower bounds on the number of degree $n$ monic polynomials with almost prime discriminants, as well as the closely related problem of lower bounds on the number of degree $n$ number fields with almost prime discriminants.
Live Migration of Operating System Containers in Encrypted Virtual Machines

With the widespread use of Docker and Kubernetes, OS-level virtualization has become a key technology to deploy and run software. At the same time, data centers and cloud providers offer shared computing resources on demand. The use of these resources usually leads to a larger trusted computing base and less control over the data.
Deep learning Applications to classify Cross-Topic Natural Language Texts Based on Their Argumentative Form

Argument mining is a public blockchain for optimizing the analysis for reasons in broad data sets. Despite their utility for this role, most current techniques for contention processing are optimized for particular text types and fail when implemented in interdependent texts. This paper suggests a new sentences annotation structure that can be reliably applied to random Web texts by crowd staff. Commentaries for over 25,000 cases layer protocol contentious topics were gathered. Cross-topic tests show that our attn computer program generalizes the best to unknown subjects, outperforming standard BiLSTM prototypes by 6% in specificity and 11% in F-score.
Design of Collaborative Control Scheme between On-chain and Off-chain Power Data

The transmission and storage process for the power data in an intelligent grid has problems such as a single point of failure in the central node, low data credibility, and malicious manipulation or data theft. The characteristics of decentralization and tamper-proofing of blockchain and its distributed storage architecture can effectively solve malicious manipulation and the single point of failure. However, there are few safe and reliable data transmission methods for the significant number and various identities of users and the complex node types in the power blockchain. Thus, this paper proposes a collaborative control scheme between on-chain and off-chain power data based on the distributed oracle technology. By building a trusted on-chain transmission mechanism based on distributed oracles, the scheme solves the credibility problem of massive data transmission and interactive power data between smart contracts and off-chain physical devices safely and effectively. Analysis and discussion show that the proposed scheme can realize the collaborative control between on-chain and off-chain data efficiently, safely, and reliably.
Use of Computer Vision Based Hand Tracking in Educational Environments

Computer vision and it's use is starting to represent a major part of our daily life. Analysis of captured pictures and the use of the information that they contain can support specific tasks in an increasing number of applications. In this paper we present two out of the box solutions for making teamwork-based education more interesting by using wireless plugins. For this reason we used two different systems for detecting palms and fingers of user's hands. We developed two different applications to test each of the two out of the box solutions. The first application supports teacher-student cooperation. Students on remote computers can draw various shapes and lines fully via hand tracking and the shapes are then simultaneously redrawn on the teacher's or student's computer. It uses Google's MediaPipe library for hand tracking and Node.js for server communication. The second application is a simple video game aimed primarily at preschool children. By playing the game, children learn basic shapes, colors and logical thinking. The application uses the handtrack.js library for hand-tracking and HTML/CSS with JavaScript for the base application. Both applications demonstrate how computer vision can be easily integrated into remote educational user interfaces. We hope that this will stimulate other developers of educational applications to integrate into their application also computer vision based interaction.
An efficient hexagonal image framework using pseudo hexagonal pixel for computer vision applications

Nature-inspired computing has been a real source of motivation for the development of many meta-heuristic algorithms. The biological optic system can be patterned as a cascade of sub-filters from the photoreceptors over the ganglion cells in the fovea to some simple cells in the visual cortex. This spark has inspired many researchers to examine the biological retina in order to learn more about information processing capabilities. The photoreceptor cones and rods in the human fovea resemble hexagon more than a rectangular structure. However, the hexagonal meshes provide higher packing density, consistent neighborhood connectivity, and better angular correction compared to the rectilinear square mesh. In this paper, a novel 2-D interpolation hexagonal lattice conversion algorithm has been proposed to develop an efficient hexagonal mesh framework for computer vision applications. The proposed algorithm comprises effective pseudo-hexagonal structures which guarantee to keep align with our human visual system. It provides the hexagonal simulated images to visually verify without using any hexagonal capture or display device. The simulation results manifest that the proposed algorithm achieves a higher Peak Signal-to-Noise Ratio of 98.45 and offers a high-resolution image with a lesser mean square error of 0.59.
The Educational Needs of the Alpha Generation

Sociologists, pedagogues and psychologists are constantly rethinking the characteristics of the Alpha generation - children born between 2010 and 2025. Technology follows them, shapes and marks them, they are growing up to be the most technologically “skilled” and most educated generation ever. Although Marc Prensky introduced the concept of digital natives back in 2001, it seems that the Alphas are the first true digital natives to understand and speak the original language of digital technology. Educators now face the challenge of adapting the ways they interact and communicate with Alphas, and of adapting the educational system. The assessment of the educational needs of new generations using the criteria and logic of a system that is already obsolete is a complete failure. We must anticipate changes in education at the level of digitalization, virtualization, gamification of teaching, but also at the content level. The European Commission has invited EU members to a strategic dialogue in order to prepare a proposal for a recommendation for successful digital education by 2022. Educational professionals need to communicate with the IT sector, find a common language and integrate it into the curriculum components. The paper presents the results of a qualitative analysis of 20 semi - structured interviews in which 10 ICT experts and 10 university pedagogues consider the issues of the future of the educational system as well as the integration of technology into the teaching process. Research results indicate a partial agreement in the vision of the future of computers in education, but also a different view of the role of the educational system, which further emphasizes the need for a dialogue in considering this topic.
A Tourism Knowledge Model through Topic Modeling from Online Reviews

Ontologies and knowledge models have gained more recognition because of their extensive use in recommender systems. The lack of automatic approaches in ontology engineering, however, becomes a challenge to fulfill increasing needs for such knowledge models in the field of tourism. In this study, a system for building tourism knowledge models from online reviews is proposed. The main contribution of the study is the application of topic modeling to build a knowledge model that, in turn, allows for an automated labeling process to train classifiers. Given a collection of unlabeled tourism online reviews, Latent Dirichlet Allocation (LDA) is applied to automatically label each document. Each topic discovered by LDA is labeled with one specific category, representing its semantic meaning based on an existing general ontology as a reference. These automatically labeled documents are used for classification, and the result is compared with manual annotation. Experiments on Indonesian tourism datasets showed that the automatic labeling approach using LDA provides for a precision score of 70%. In classification tasks, this approach can achieve comparable or even better classification performance than the manual labeling. The results obtained suggest that the developed system is capable of building a tourism knowledge model and providing acceptable-quality training data for the development of tourism recommender systems.
Medical health data sharing scheme based on blockchain and attribute-based encryption

With the development of medical digitization, smart medical care and medical big data technologies, secure storage and sharing of medical data have become the hotspot of research in the field of medical technology. However, Fragmentation of medical data is much more serious, and many data becomes data islands that are difficult to apply to medical research. At the same time, the centralized control of medical data by third-party institutions has security bottlenecks and performance bottlenecks, which also lead to frequent data leakage incidents. To solve this problem, we propose a medical health data sharing scheme based on blockchain and attribute-based encryption. The model implements decentralized, tamper-proof, traceable data sharing based on a distributed blockchain system. And an attribute-based encryption is used to protect the privacy of patient's medical data. The cryptographic method is used to implement fine-grained access control of the data. The model can reduce the cost of data sharing and improve the efficiency of data utilization while protecting the data security. Finally, compared with the existing medical data sharing methods, our model has certain advantages and good scalability in some aspects.
A dynamic outlier ensemble for databases in wind tunnel experiments

In wind tunnel experiments, a portion of abnormal samples, which are usually referred to as outliers, often contaminates databases. These outliers are detrimental when applying these databases to system identification or process control. To this end, an efficient outlier detection model dedicated to wind tunnel becomes thus necessary. In literature, several outlier ensembles have been proposed and proved more effective than single detectors. However, all these outlier ensembles are static. Sometimes, they are too robust and conservative to discover all outliers. In order to improve the detecting performance, in this paper, we propose a dynamic outlier ensemble. In our dynamic model, we assign the most competent base detector to each test pattern. To accomplish such assignments, we estimate competences of all base detectors by computing their performance on a local region of the corresponding test pattern. Several datasets from real-life wind tunnel experiments are used to validate the proposed outlier detector. The superiority has been approved by comparing our detector with several competitors.
The Impact of the COVID-19 Pandemic on Education at Various Levels

Aim. The presented study aims to analyze the impact of the pandemic on Russian education at different levels.Tasks. The authors consider the specific features of distance learning at different levels of education: general, vocational, and extended; identify the positive and negative effects of modern information technologies on the organization and results of education.Methods. This article uses general scientific methods of cognition, including analysis, synthesis, comparison, analogy.Results. The major problems that arose during the mass introduction of innovative technologies at educational institutions during the pandemic and the positive results of their application are identified. Despite the ambiguous attitude to distance learning, this format has certain positive aspects due to the modernization of the educational process.Conclusions. Digital technologies open up additional opportunities for personal development and self-improvement, giving access to information, digital tools, materials, and services. They make it possible to transfer the interaction between students and the academic staff to a new level, expand opportunities for self-check, improve motivation, and increase interest in gaining knowledge.
Toxic Comment Classification Using Bi-directional GRUs and CNN

In this era, where Internet communities have a very prominent role in our lives, a notable positive change in our mood can be transpired by just a single post or comment. Just like online communities have its advantages, it also has its downside of making everyone vulnerable to the menaces of maltreatment and harassment online. As a solution to this problem, we have used the corpus provided by Conversation AI for toxic comment classification which contains labeled comments. We have implemented a multi-headed classification model using bi-directional-GRUs and convolution neural networks so that sequential and high-dimensional data are handled efficiently. The GRU being used is bi-directional, so that the model understands the context of data in both forward and reverse direction. This model will detect different types of toxicity like insults, obscenity, threats, etc., and helps in removing those comments which are detected to have any of these characteristics.
Test-Time Adaptation for Video Frame Interpolation via Meta-Learning

Video frame interpolation is a challenging problem that involves various scenarios depending on the variety of foreground and background motions, frame rate, and occlusion. Therefore, generalizing across different scenes is difficult for a single network with fixed parameters. Ideally, one could have a different network for each scenario, but this will be computationally infeasible for practical applications. In this work, we propose MetaVFI, an adaptive video frame interpolation algorithm that uses additional information readily available at test time but has not been exploited in previous works. We initially show the benefits of test-time adaptation through simple fine-tuning of a network and then greatly improve its efficiency by incorporating meta-learning. Thus, we obtain significant performance gains with only a single gradient update without introducing any additional parameters. Moreover, the proposed MetaVFI algorithm is model-agnostic which can be easily combined with any video frame interpolation network. We show that our adaptive framework greatly improves the performance of baseline video frame interpolation networks on multiple benchmark datasets.
Usability Evaluation of Learning Management System

In the current era of globalization, there are a lot of things have been done with technology. One of them is education. LMS is an example implementation of technology in Education. LMS is a platform that provided by schools or universities, to support teaching and learning activities. Many literatures show that with the LMS, online learning can be fulfilled properly. To make an LMS comfortable and easy to use, it is necessary to have a good user interface design and user experience. User interface and user experience are a very important part of developing an LMS. Without a good user interface and user experience, the LMS cannot be used by the user properly. Based on the research, there are two methods commonly used to evaluate user interface design and user experience. Usability measurement and heuristic evaluation are two methods that are always used to evaluate user interface and user experience. In addition, there is an important part of the LMS, it was features. Based on the research, the most common features in LMS are discussion forums and learning materials. Therefore, users can discuss the materials each other and also share learning materials.
The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement

Modern smartphones can continuously stream multi-megapixel RGB images at 60 Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates. During a snapshot photograph, the natural unsteadiness of the photographer's hands offers millimeter-scale variation in camera pose, which we can capture along with RGB and depth in a circular buffer. In this work we explore how, from a bundle of these measurements acquired during viewfinding, we can combine dense micro-baseline parallax cues with kilopixel LiDAR depth to distill a high-fidelity depth map. We take a test-time optimization approach and train a coordinate MLP to output photometrically and geometrically consistent depth estimates at the continuous coordinates along the path traced by the photographer's natural hand shake. With no additional hardware, artificial hand motion, or user interaction beyond the press of a button, our proposed method brings high-resolution depth estimates to point-and-shoot “table-top” photography – textured objects at close range.