Reviewerly/exHarmony huggingface dataset:
The Paradigm of Education in the Era of Knowledge Management at the Stage of the Sixth Technological Mode

The article deals with the principles of knowledge formation and their interaction with the educational process at the stage of the sixth technological mode in the economy. The article gives the analysis of educational institutions of higher education since the beginning of the last century, the concept of “education” from the point of view of the employee, the employer and the state, the problems in the educational system are identified. The reasons for the lagging of the Russian knowledge economy from international needs are considered. The technological structures, the role and importance of knowledge management are characterized, the prerequisites that distinguish a competitive employee of an effective organization are considered. The role of changes in the level of knowledge in the development of professional potential of the employee is shown, the world experience of economic approaches to knowledge management and their advantages are considered. As a result, it is concluded that knowledge management is the basis for the development of the information society at the stage of the sixth technological order.
Visual Stabilization of Wheeled Mobile Robots with Unknown Visual Parameters

For a monocular vision-based wheeled mobile robot system, an image-based visual servoing control strategy is proposed. The lack of depth information and nonholonomic constraints are inherent problems of the system. In addition, the uncertain intrinsic and extrinsic camera parameters are considered which makes the visual servoing of mobile robot more challenging. With this in mind, a two-degrees-of-freedom control structure is designed to separately deal with the nonholonomic constraints and the system uncertainties. An extended state observer is constructed to estimate the uncertainties and compensate to the controller. Then a switching controller is established for the compensated system to dispose of the nonholonomic constraints. The simulation results show the good performance of the proposed scheme.
Application of OVT attribute analysis in acquisition method

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Application of OVT attribute analysis in acquisition methodAuthors: Ma TaoNi YudongLi YangshengGao RuiGu LijunWang TongjunMa TaoBGP, CNPCSearch for more papers by this author, Ni YudongBGP, CNPCSearch for more papers by this author, Li YangshengBGP, CNPCSearch for more papers by this author, Gao RuiBGP, CNPCSearch for more papers by this author, Gu LijunBGP, CNPCSearch for more papers by this author, and Wang TongjunBGP, CNPCSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3214328.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractOVT (Offset-Vector Tile) technology has been widely used in processing and interpretation of high-density and wide-azimuth seismic data and has achieved good results. However, it is seldom used in acquisition methods. In the past, geometry evaluation in acquisition technology is mostly based on data analysis in CMP domain, but less on data analysis in OVT domain. There is no complete geometry evaluation technology based on OVT attributes. Therefore, it is necessary to develop the application of OVT attribute analysis in acquisition technology. According to research results of the project, this paper introduces the application of OVT attribute analysis technology in parameter demonstration of geometry and field geometry shooting technology based on OVT attribute requirements. In addition, combining with the actual production data of a project, the application of OVT attribute analysis in acquisition method is further elaborated.Presentation Date: Tuesday, September 17, 2019Session Start Time: 9:20 AMPresentation Time: 11:00 AMLocation: Poster Station 12Presentation Type: PosterKeywords: 3D, acquisition, offsetPermalink: https://doi.org/10.1190/segam2019-3214328.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Ma Tao, Ni Yudong, Li Yangsheng, Gao Rui, Gu Lijun, and Wang Tongjun, (2019), "Application of OVT attribute analysis in acquisition method," SEG Technical Program Expanded Abstracts : 218-222. https://doi.org/10.1190/segam2019-3214328.1 Plain-Language Summary Keywords3DacquisitionoffsetPDF DownloadLoading ...
Querying in a Workload-Aware Triplestore Based on NoSQL Databases

RDF and SPARQL are increasingly used in a broad range of information management scenarios (e.g., governments, corporations, and startups). Scalable SPARQL querying has been the main issue for virtually all the recent RDF triplestores. This paper presents WA-RDF, a middleware that addresses workload-adaptive management of large RDF graphs. Our middleware not only employs all the most used NoSQL data models but also provides a novel RDF data partitioning approach based on a fragmentation strategy that maps RDF data into multiple NoSQL databases. This workload-aware partitioning scheme provides, in turn, efficient processing of SPARQL queries over these NoSQL databases. Our experimental evaluation shows that the solution is promising, outperforming three recent baselines.
Sources of motivation of high school students for participation in physics competitions

Abstract This article studies motives on why high school students participate in physics competitions in the form of a case study (with qualitative and quantitative elements) focused on one contest. We bring a brief insight into primary motivations for their participation and how it is changing later. The pinpoint of our study is that it is hard to address new participants these days. Their most often motivation for starting in the competition is to get better in physics, general interest in physics and interest in solving challenging tasks. For those who continue in participation – they usually want to get even better in physics. That’s because participants often realise that they are not as good as they thought. A motivation which is getting stronger is also to get an invitation to an educational camp. We also briefly discuss possible demotivational factors.
Imbalance Robust Softmax for Deep Embeeding Learning

Deep embedding learning is expected to learn a metric space in which features have smaller maximal intra-class distance than minimal inter-class distance. In recent years, one research focus is to solve the open-set problem by discriminative deep embedding learning in the field of face recognition (FR) and person re-identification (re-ID). Apart from open-set problem, we find that imbalanced training data is another main factor causing the performance degradation of FR and re-ID, and data imbalance widely exists in the real applications. However, very little research explores why and how data imbalance influences the performance of FR and re-ID with softmax or its variants. In this work, we deeply investigate data imbalance in the perspective of neural network optimisation and feature distribution about softmax. We find one main reason of performance degradation caused by data imbalance is that the weights (from the penultimate fully-connected layer) are far from their class centers in feature space. Based on this investigation, we propose a unified framework, Imbalance-Robust Softmax (IR-Softmax), which can simultaneously solve the open-set problem and reduce the influence of data imbalance. IR-Softmax can generalise to any softmax and its variants (which are discriminative for open-set problem) by directly setting the weights as their class centers, naturally solving the data imbalance problem. In this work, we explicitly re-formulate two discriminative softmax (A-Softmax and AM-Softmax) under the framework of IR-Softmax. We conduct extensive experiments on FR databases (LFW, MegaFace) and re-ID database (Market-1501, Duke), and IR-Softmax outperforms many state-of-the-art methods.
META-GLARE’s Supports to Agent Coordination

Clinical Guidelines (GLs) provide evidence-based recommendations to suggest to physicians the “best” medical treatments, and are widely used to enhance the quality of patient care, and to optimize it. In many cases, the treatment of patients cannot be provided by a unique healthcare agent, operating in a unique context. For instance, the treatment of chronic patients is usually performed not only in the hospital, but also at home and\or in the general practitioner’s ambulatory, and many healthcare agents (e.g., different specialist, nurses, family doctor) may be involved. To grant the quality of the treatments, all such agents must cooperate and interact. A computer-based support to GL execution is important to provide facilities for coordinating such different agents, and for granting that, at any time, the actions to be executed have a “proper” person in charge and executor, and are executed in the correct context. Additionally, also facilities to support the delegation of responsibility should also be considered. In this paper we extend META-GLARE, a computerized GL management system, to support such needs providing facilities for (1) treatment continuity (2) action contextualization, (3) responsibility assignment and delegation (4) check of agent “appropriateness”. Specific attention is also devoted to the temporal dimension, to grant that each action is executed according to the temporal constraints possibly present in the GL. We illustrate our approach by means of a practical case study.
Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment

In this work we describe a rule-based classifier (DEQAR-CC), which employs a combination of selected rules after a two-phase training process, and without the need of a previous discretization for the numerical variables. It was compared in the application to a real imbalanced dataset regarding the toxicity during and after radiation therapy for prostate cancer. In this comparison with other predictive methods (rule-based, artificial neural networks, trees, Bayesian and logistic regression), DEQAR-CC showed a better global prediction performance than the rest of classifiers, in an evaluation regarding several performance measures and by using cross-validation. Finally, it was employed to obtain a predictive model for genitourinary toxicity, obtaining an interpretable classification scheme which simply combines two rules with two variables.
Improved Performance of CDL Algorithm Using DDELM-AE and AK-SVD

Due to the poor robustness and high complexity of the concentrated dictionary learning (CDL) algorithm, this paper addresses these issues using denoising deep extreme learning machine based on autoencoder (DDELM-AE) and approximate k singular value decomposition (AK-SVD). Different from the CDL algorithm, on input, DDELM-AE is added for enhancing denoising ability and AK-SVD replaces K-SVD for improving running speed. Additionally, experimental results show that the improved algorithm is more efficient than the original CDL algorithm in terms of running time, denoising ability, and stability.
Initial Conditions of Reaction-Diffusion Algorithm Designed for Image Edge Detection

Image edges refer to steep brightness changes and provide fruitful information on image structures. This paper proposes initial conditions of a reaction-diffusion algorithm designed for image edge detection. The algorithm utilizes a network in which FitzHugh-Nagumo neurons are placed at grid points and connected to their neighboring ones. Each FitzHugh-Nagumo neuron simulates a process of excitation and inhibition observed in biological nerve axon, and is described with a pair of time-evolving ordinary differential equations (ODEs) stated by activator and inhibitor variables. The network is originally derived from discretization of FitzHugh-Nagumo type reaction-diffusion equations described by partial differential equations (PDEs). It is known that the network creates stationary pulses at edge positions of image brightness distribution provided as initial conditions. Steep brightness changes initiate processes of excitation and inhibition, and pulses having excited states at edge positions remain at their original positions, as time proceeds. However, the network is insensitive to relatively weak edges having gradual brightness changes. The novel initial conditions proposed here are composed of absolute gradients of multi-scale images. The algorithm with the initial conditions is applied to test images of BSDS300 dataset, and its performance is quantitatively confirmed by F-measure, in comparison with other edge detection algorithms.
Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm

In this paper, we propose an algorithm to generate optimal trajectory path considering the complex operating environment of excavator front part composed of the boom, arm, and bucket by using genetic algorithm. In order to express motion in space, we propose a method of coordinate plane space of grid cell, and define the fitness value by path distance. After generating chromosome candidates for each motion unit based on the polygonal structure of the front part of the excavator, we calculate the fitness value about each chromosome. The crossover and mutation operations between the chromosomes selected through roulette wheel of top 20% are repeatedly performed to generate paths with optimal fitness values. This paper verifies the structural analysis of the front part of excavator and the utility of the genetic algorithm to optimize the path in the grid space.
Cyber Security: A New Approach of Secure Data Through Attentiveness in Cyber Space

Cyber Security assumes a critical job in the field of Information and Communication Technology [ICT]. Securing the information has become one of the greatest difficulties in the present day. When we consider cyber security, the main aspect that strikes in our mind is ‘cyber crimes’ which are expanding enormously day by day. Different Governments and organizations are taking numerous measures so as to keep these cyber crimes. Other than different measures cyber security is as yet a big concern to many. This paper for the most part centers around difficulties looked by cyber security on the most recent technologies. It also focuses around most recent about the cyber security methods, ethics and the trends changing the appearance of cyber security. Governments, military, associations, budgetary foundations, colleges and different organizations gather, process and store a great deal of private data on PCs and transmit that data over systems to various PCs. With the tenacious quick advancement of volume and enhancement of cyberattacks, incite attempts are required to anchor touchy business and individual data, and to anchor national security. The paper talk with respect to the idea of the internet and shows how the web is unbound to transmit the mystery and budgetary data. We demonstrate that hacking is as of now typical and dangerous for overall economy and security and exhibited the diverse techniques for Cyber attacks in India and around the globe.
The study of non-Gaussianity, signal to quantum noise ratio and nonclassicality for hypergeometric state of radiation field

With the advent of quantum computing, engineered quantum states have become very important. A particularly important engineered quantum state is hypergeometric state, which is an intermediate state in the sense that it can be reduced to various quantum states of interest at different limits. Earlier some nonclassical properties of this state were studied. Here, we have studied a set of closely connected but independent properties of states which had not been studied till now. Specifically, we have computed the amount of non-Gaussianity present in this state, signal to quantum noise ratio for this state and higher-order nonclassicality witnessed the criterion of Hillary type amplitude squared squeezing. The analysis of the investigation has revealed that the hypergeometric state is non-Gaussian and well as nonclassical and the amount of non-Gaussianity and the depth of nonclassicality can be controlled by controlling the state parameters.
Edge Detection of Depth Image Based on Contour Curve

Abstract Edge is one of the most important features of an image because it recognizes the world through images and understands the position and contour of objects in images through edges. Edge monitoring of depth image is the basis of object recognition and location. The contour curve calculates the feature points and maps them to the original contour space. The original contour is matched by a sequence of markers. At the initial stage of recognition, the dissimilar targets and different gestures are eliminated quickly. The candidate list of targets is generated for accurate matching to improve the recognition efficiency. The curve is a nonlinear extension of the first principal component, emphasizing the search for the “middle” through the distribution of data, starting from an arbitrary point of the image, estimating the pixel points near the point and obtaining the gradient value. If the point satisfies the characteristics of the edge point, then the point detour depth traverses to find edge points that are perpendicular to the direction of the gradient and marks the visited points. And satisfy the self-consistent smooth curve without parameters, these curves give an overview of the data. Determine the degree of approximation; when the judgment has converged to the target contour, continue to converge and penetrate the concave area of the target contour.
The glorious jubilee of Rujero Sergeevich Gilyarevsky

The author offers the brief biographical information on Rujero Sergeevich Gilyarevsky whose 90-th anniversary is celebrated. She reviews the main stages of his academic and pedagogical career, in particular, his scholarly works, his two theses studies (candidate’s and doctoral), numerous publications that have been contributing to the librarianship, library and information sciences. The author emphasizes the scope of Gilyarevsky’s professional interests and retraces expanding of the subject scope of his publications – from catalog structuring (1954) to cloud technologies, information management and scientometrics. Rujero Gilyarevsky analyzes the problems of the libraries (and e-libraries, in particular), their future, professional values of the librarians within the digital communication environment, bibliography as an element of information culture. R. Gilyarevsky has complete mastery of several foreign languages. The selected bibliography of R. Gilarevsky’s publications, including those co-authored by his colleagues, is appended.
Software Design of the Detection Device for the Performance of the Faucet based on LabWindows/CVI

Abstract Flow and sensitivity are two important indicators for measuring faucet performance. This paper designs a PC software based on the LabWindows/CVI development platform for the faucet flow and sensitivity detection system. The main function of the software is to communicate with the lower computer, control related hardware, and achieve data acquisition and analysis during the test process. Finally, the integration and record of test data is realized through MySQL database. The software runs stably and provides good human-computer interaction and automation services.
Speech-Based L2 Call System for English Foreign Speakers

In this paper, we are presenting a language learning system which automatically evaluates English speech linguistically and grammatically. The system works by prompting the learner a question in his native language (text+figure) and waiting for his/her spoken response in English. Different types of features were extracted from the response to assess it in terms of language grammar and meaning errors. The universal sentence encoder was used to encode each sentence into 512-dimensional vector to represent the semantic of the response. Also, we propose a binary embedding approach to produce 438 binary features vectors from the student response. To assess the grammatical errors, different features were extracted using a grammar checker tool and part of speech analysis of the response. Finally, the best two DNN-based models have been fused together to enhance the system performance. The best result on the 2018 shared task test dataset is a D-score of 17.11.
Multi-criteria Recommendations by Using Criteria Preferences as Contexts

Recommender system is a well-known information system which assists decision making by producing recommendations tailored to user preferences. Multi-criteria recommender systems (MCRS) additionally take user preferences in multiple criteria into account, in order to better generate recommendations. The major challenge in MCRS is the process of aggregating user ratings in the multiple criteria. We claim that user preferences in these criteria can be considered as contexts, so that the overall taste on an item can be estimated by a process of context-aware predictions. In this paper, we exploit and summarize different methods which produce the recommendations by using criteria preferences as context information. We examine these methods based on three real-world data sets. Our experimental results demonstrate the effectiveness of these algorithms in the rating prediction task, in comparison with the state-of-the-art multi-criteria recommendation approaches.
Hierarchical Processing Model Based on Multi-modality Interaction Design

The information warfare requires diverse operations and a lot of information, the traditional human factors engineering design methods are prone to problems of high load of human–computer interaction, and it also lacks of quantitative capability. This paper focuses on five modalities of voice, eye control, touch, brain control, and gestures, and according to the man–machine–environment cognitive decision model and the hierarchical processing model for each modality interaction in different applicable scenarios to build the multi-modality interaction layered processing model to enhance the naturalness and friendliness of command interaction.
Design diagrams as ontological source

beginabstract In custom software development projects, it is frequently the case that the same type of software is being built for different customers. The deliverables are similar because they address the same market (e.g., Telecom, Banking) or have similar functions or both. However, most organisations do not take advantage of this similarity and conduct each project from scratch leading to lesser margins and lower quality. Our key observation is that the similarity among the projects alludes to the existence of a veritable domain of discourse whose ontology, if created, would make the similarity across the projects explicit. Design diagrams are an integral part of any commercial software project deliverables as they document crucial facets of the software solution. We propose an approach to extract ontological information from UML design diagrams (class and sequence diagrams) and represent it as domain ontology in a convenient representation. This ontology not only helps in developing a better understanding of the domain but also fosters software reuse for future software projects in that domain. Initial results on extracting ontology from thousands of model from public repository show that the created ontologies are accurate and help in better software reuse for new solutions. endabstract