Reviewerly/exHarmony huggingface dataset:
International Overview of Eye Tracking Technologies Using Patent Bases

This work presents an international overview on eye tracking technology from the perspective of Industrial Property, more specifically of Patents. In this sense, a methodology of Technological Prospection using the Derwent Innovation patent base is addressed. The results show the growth in the number of patent requirements about eye tracking technology from 1998 to 2018, which is more accelerated from 2010. The results also show the United States (US) as the international leader in the development of this technology, both in the number of patent requirements in the country, which is an indicator of interest in protecting this technology, and in the number of companies involved. On the other hand, the results also indicate that, since 2015, the Chinese Office is the international leader in the number of patent requirements, leaving the US Office in second place. International data were stratified in different areas of technological development, with the aid of the International Patent Classification (IPC), with emphasis on the area of eye tracking for surgery and eye diagnosis, followed by eye tracking for human-machine interface purposes.
Learning Multi-granular Features for Harvesting Knowledge from Free Text

Extracting entities and their relations expressed in free text is essential to correct and populate knowledge graphs. Traditional methods assume that only the information of entities benefits the extraction of relations. They view this task as a two-step task, named entity recognition (NER) and relation classification (RC). However, the inadequate use of information and the error propagation problem constrain methods following this pipeline fashion. Joint extraction methods are proposed to incorporate useful interaction information between the two tasks for improvement, which solve NER and RC simultaneously. Although they have been proved to be superior to pipeline models, their performance is still far from satisfaction. In this paper, we try to combine the idea of data-driven granular cognitive computing and deep learning in joint extraction task. Accordingly, a neural-based joint extraction model named Joint extraction with Multi-granularity Context (JMC) is proposed. It explores the multi-granularity context of natural language sentences and uses neural networks to learn representations of these context automatically. Experiments results on NYT, a large data set produced by the distant supervision technique, show that JMC achieves comparative results to state-of-the-art methods.
Pure Sequent Calculi

Analyticity, also known as the subformula property, typically guarantees decidability of derivability in propositional sequent calculi. To utilize this fact, two substantial gaps have to be addressed: (i) What makes a sequent calculus analytic? and (ii) How do we obtain an efficient decision procedure for derivability in an analytic calculus? In the first part of this article, we answer these questions for pure calculi —a general family of fully structural propositional sequent calculi whose rules allow arbitrary context formulas. We provide a sufficient syntactic criterion for analyticity in these calculi, as well as a productive method to construct new analytic calculi from given ones. We further introduce a scalable decision procedure for derivability in analytic pure calculi by showing that it can be (uniformly) reduced to classical satisfiability. In the second part of the article, we study the extension of pure sequent calculi with modal operators. We show that such extensions preserve the analyticity of the calculus and identify certain restricted operators (which we call “Next” operators) that are also amenable for a general reduction of derivability to classical satisfiability. Our proofs are all semantic, utilizing several strong general soundness and completeness theorems with respect to non-deterministic semantic frameworks: bivaluations (for pure calculi) and Kripke models (for their extension with modal operators).
Study of Out-Of-Hospital Access to HIS System: A Security Perspective

In light of the need for Extramural Hospital Information System (HIS) access through mobile devices outside the hospital, this research analyzes situational information security threats, including the circumstances in which a mobile device may get lost and personal data may be stolen. Moreover, the system needs to be implemented in accordance with the regulations. Based on the security threat analysis, it is proposed to use a security control module to provide a security-enabled HIS proxy module, two-way authentication module, and One-Time Password (OTP). The sending module and cryptographic technology computing module with Micro SD encryption card form a set of HIS extension system, which includes the SMS OTP method to simultaneously verify the two-way authentication mechanism of a user and the device that the user owns.
Propelling Haptic Devices into the Mobile World to Advance K-12 STEM Learning

In this demonstration we illustrate how haptic communication can occur via a mobile application. In particular, we present an Android-based platform that allows connectivity among haptic devices and smartphones. Our app was designed specifically for educational applications, and we used the Hapkit as our first exemplary hardware platform. Our open-source implementation offers several haptic emulations designed for K-12 Physics courses; furthermore, our Android application provides instrumented metrics capable of analyzing latency and other network parameters among communicating hardware and software processes.
Concept Clustering Algorithm Based on the Factor Space Theory

Based on the factor space theory, this paper proposes a kind of clustering algorithm---concept clustering algorithm. Choosing the factor with the biggest resolution as the division factor at each step, the initial complex concept clustering tree can be constructed. To simplify the tree, a back-deduction method is conducted successively, by combining the clusters with a small amount of samples with their nearest neighbors. Eventually, a simple tree with fewer clustering rules can be generated. An example is used to implement the specific clustering process, where the visualization effect is shown step by step.
Three-Dimensional Reconstruction of Hand Using Stereoscopic Images

Abstract This article is devoted to works on using natural user interfaces (NUI) in computer support systems of aircraft service. The concept of such interfaces involves the usage in human-machine communication the same measures as in the communication between people, that is sound or gesture. In the case of gesture communication, it is indispensable to adopt methods related to computer vision algorithms. One of them is a three-dimensional reconstruction of objects based on processing techniques of a pair of two-dimensional images. The above method and the results of its application were presented to obtain a three-dimensional cloud of points describing the hand shape. The obtained software will constitute an element of gesture classifier based on the analysis of the spatial location of the acquired points of the cloud.
Identification of Interference Sources in Spiral Galaxy Images Based on Convolutional Neural Network

Abstract In the spiral galaxy image, the luminosity of the precursor star due to its close proximity to the spiral galaxy reaches an approximate order of magnitude, which makes the precursor stars become an important interference source in the galaxy image and makes the computer difficult to distinguish them. Therefore, it is of great significance to remove the precursor stars from the galaxy image. In this paper, a modified convolution neural network (CNN) is used to classify spiral galaxies and stars in order to find pre-star interference. Convolutional neural network (CNN) is a popular image classification technology, which is included in depth learning. It solves the problem of a large amount of data and serious non-linearity on the basis of neural network. Based on CNN, this paper studies and improves the selection of network level, the optimization algorithm, batch normalization and so on. The two types of astronomical images of the target are trained and predicted, and the classification results are very good. At present, the error rate is around 3%.
On Transforming Narrowing Trees into Regular Tree Grammars Generating Ranges of Substitutions

The grammar representation of a narrowing tree for a syntactically deterministic conditional term rewriting system and a pair of terms is a regular tree grammar that generates expressions for substitutions obtained by all possible innermost-narrowing derivations that start with the pair and end with particular non-narrowable terms. In this paper, under a certain syntactic condition, we show a transformation of the grammar representation of a narrowing tree into another regular tree grammar that overapproximately generates the ranges of ground substitutions generated by the grammar representation. In our previous work, such a transformation is restricted to the ranges w.r.t. a given single variable, and thus, the usefulness is limited. We extend the previous transformation by representing the range of a ground substitution as a tuple of terms, which is obtained by the coding for finite trees. We show a precise definition of the transformation and prove that the language of the transformed regular tree grammar is an overapproximation of the ranges of ground substitutions generated by the grammar representation. We leave an experiment to evaluate the usefulness of the transformation as future work.
Steganographic Protection Method Based on Huffman Tree

This paper presents an advanced method of graphical data protection. The novelty of the proposed method consists in the use of bits correspondence scheme, Huffman tree, and specific data encoding based on variable length of color hue code. The steganographic stability evaluation of the proposed method is presented and discussed in the paper. The application of the method to the technology of archive medical image processing is discussed as well.
Study on Job Analysis and Optimization of Solid Wood Puzzle Operation Based on Job Measurement

Abstract In this paper, three types of solid wood splicing are summarized: short material splicing, narrow material splicing and thin material lamination. The technological requirements of solid wood splicing are also summarized. On this basis, the work of jigsaw splicing is measured and analyzed by means of work sampling, and some suggestions for job improvement are put forward. Finally, the existing problems in the layout of jigsaw puzzle workplaces are analyzed and improved according to the principles of importance, frequency, sequence and functionality.
Semiempirical Neural Network Models of Controlled Dynamical Systems

In this chapter we discuss the hybrid, semiempirical (“gray box”) neural network–based modeling approach. Semiempirical models rely on both the theoretical knowledge of the system and the experimental data on its behavior. As evidenced by the results of numerous computational experiments, such models possess high accuracy and computational speed. Also, the semiempirical modeling approach makes it possible to state and solve the identification problem for the characteristics of dynamical systems. That is a problem of great importance, and it is traditionally difficult to solve. These semiempirical ANN-based models possess the required adaptivity feature, just like the pure empirical ones. First, we describe the properties of semiempirical state space continuous time ANN-based models. Then, we outline the stages of the model design procedure and present an illustrative example. We discuss the continuous time counterparts of real-time recurrent learning and backpropagation through time algorithms required for the computation of error function derivatives. We also describe the homotopy continuation training method for semiempirical ANN-based models. Finally, we treat the topic of optimal design of experiments for semiempirical models of controlled dynamical systems.
Research on Matching Accuracy of Underwater Terrain Matching Algorithm Based on Unscented Kalman Filter

UT transform is the core step of unscented kalman filter (UKF) algorithm. The Sigma sampling type of UT transform has great influence on the computational complexity, state estimation accuracy and computational efficiency of UKF-based terrain matching algorithm. In order to obtain the high precision terrain matching method which is suitable for engineering application, the Comparative analysis of three kinds of UKF-based terrain matching algorithms was carried out in this paper. Firstly, the UKF-based underwater terrain matching model was given. Secondly, the principle and the performance characteristics of scaled sampling method, Cubature sampling method, Gauss-Hermite sampling methods were analyzed. The filtering precision factors of symmetric unscented Kalman filter(SUKF), Cubature unscented Kalman filter(CKF), Gauss-Hermite Kalman Filter(GHKF) was studied from the algorithm principle, Thirdly, The computer simulation was performed using univariate nonstationary growth model(UNGM) to test the previous analysis conclusions. Finally underwater terrain matching simulation model was established, and the comparative analysis of matching acuraccy was carried out based on SITAN, SUKF, CKF and GHKF, the matching effect of underwater terrain matching algorithm is obtained. The simulation results show that the UKF algorithm based on symmetric sampling strategy has better filtering precision and less computational complexity which is suitable for the filtering core of underwater terrain matching algorithm.
A Comparative Study for QSDC Protocols with a Customized Best Solution Approach

In the new era of quantum communications, the QSDC has granted a great acceptance, reassurance, inducement and comfort among the huge amount of transmission techniques. This is mainly because this technique is way better than all others; security wise and efficiency wise; which in turn because it relies on the nature of quantum elements that have been proven to be secure by nature - referring to the quantum mechanics laws. In our search we will introduce a specific technical comparison between our new protocol approach of QSDC that we proposed before, and some other well-known protocols that have been proposed in the last decades. Clarifying the advantages and disadvantages of each and the difference in the implementations of all them we will analysis the security represented in each of them. Finally, we will gather up all the issues related to this approach and come up with a recommended direction to follow in using this QSDC technique with a customize use of qubits and their transmission tools.
The Integral Points on Elliptic y2 = (x − 6)(x2 + 6x + 7)

In this paper, we studied the elliptic curve y2 = (x – a)(x2 + ax + r), a, r ∈ Z+ in the case of a = 6,r = 7 which has no relevant conclusions up to now. And we obtained the conclusion of the integer points on Elliptic y2 = (x – 6)(x2 + 6x + 7), elliptic curve in title has only one integer point (x, y) = (6,0) were proved with the help of the elementary methods such as congruence and Legendre symbol.
Study of the effectiveness of combinatorial protection algorithms based on the hardware and software of the electronic storage of corporate information systems

The paper presents experimental studies of combinatorial algorithms designed to calculate the composition and order of application of vulnerability parry tools, conducted on the basis of a typical electronic repository of the cultural institution information system. Experimental studies of data protection algorithms with the use of a neural network composition calculation unit and the order of application of vulnerability parry tools have been carried out. The proposed approach makes it possible to obtain a gain in time and efficiency (reliability) by using standard means of protecting software tools of large-scale electronic repositories, typical for large cultural institutions.
Constructive concept of address normalization

Abstract The paper addresses the problem of addressing as an identifier for the location of real estate objects. One of the main tasks in this field of activity is the problem of reducing an arbitrary address to normal form. From the analysis of the definitions of the address it follows that the address is a point in the space of the requisites. The constructive concept of the normalization of the address and requisites is introduced. The set of normal addresses (requisites) refers to the union of the base and revoked (canceled) addresses (requisites). The real set of values of each requisite is wider than the set of its normal values. Therefore, the steps of expanding the set of normal values of the properties in the direction of the set of real values of properties are considered. At each step of the proposed method, the rule (function) of normalization of the extended requisite value is considered, i. e. converting it to one of the normal values. The requirements for the synthesis of the properties of the requisite in the form of a name and number, spelling, following the words and the uniqueness of its semantic meaning, as well as to the semantic normalization of addresses are given.
Wigner algebra in a unit circle and deformed coherent states

In this paper the Wigner deformation of the quantum mechanics in a unit circle is discussed. The Wigner deformed algebra in a unit circle is presented with a help of parity operator in a unit circle. The coherent state for the Wigner deformed algebra in a unit circle is also constructed.
Realization of Statically Balanced Articulated Mechanisms and Scotch Yoke Type Balancers

Although numerous gravity balancing schemes had been developed, not too many statically balanced, spatial, articulated mechanisms had been practically implemented. Followed by the brief depiction on the underlined concept of the Scotch yoke type gravity balancer and the virtual-ground scheme, this study emphasizes on the practical design of such a spatial, articulated mechanism and the balancers that it uses. Evolution of our gravity balancer designs is presented and the design issues are discussed. Design problems of the current spatial model are revealed and suggestions for the design improvements of the future models are also included.
A Self-contained Provability Calculus for $$\varGamma _0$$

Beklemishev introduced an ordinal notation system for the Feferman-Schütte ordinal $$\varGamma _0$$ based on the autonomous expansion of provability algebras. In this paper we present the logic $$\mathbf {BC}$$ (for Bracket Calculus). The language of $$\mathbf {BC}$$ extends said ordinal notation system to a strictly positive modal language. Thus, unlike other provability logics, $$\mathbf {BC}$$ is based on a purely modal signature that gives rise to an ordinal notation system instead of modalities indexed by some ordinal given a priori. Moreover, since the order between these notations can be established in terms of derivability within the calculus, the inferences in this system can be carried out without using any external property of ordinals. The presented logic is proven to be equivalent to $$\varvec{\mathbf {RC}_{\varGamma _0}}$$, that is, to the strictly positive fragment of $$\varvec{\mathbf {GLP}_{\varGamma _0}}$$.
Mixing and Mastering

In this chapter, a philosophy of mixing, the tools of the trade and mixing techniques are discussed. Topics include critical listening, session preparation, monitoring, metering, target levels, signal flow, panning, equalization, dynamics, balancing and mastering. The stereo and immersive mixing techniques discussed in this chapter can be applied to sound design, film sound, installation, theater, multimedia and music projects.
Facial Re-identification on Non-overlapping Cameras and in Uncontrolled Environments

Face re-identification is an essential task in automatic video surveillance where the identity of the person is known previously. It aims to verify if other cameras have observed a specific face detected by a camera. However, this is a challenging task because of the reduced resolution, and changes in lighting and background available in surveillance video sequences. Furthermore, the face to get re-identified suffers changes in appearance due to expression, pose, and scale. Algorithms need robust descriptors to perform re-identification under these challenging conditions. Among various types of approaches available, correlation filters have properties that can be exploited to achieve a successful re-identification. Our proposal makes use of this approach to exploit both the shape and content of more representative facial images captured by a camera in a field of view. The resulting correlation filters can characterize the face of a person in a field of view; they are good at discriminating faces of different people, tolerant to variable illumination and slight variations in the rotation (in/out of plane) and scale. Further, they allow identifying a person from the first time that has appeared in the camera network. Matching the correlation filters generated in the field of views allows establishing a correspondence between the faces of the same person viewed by different cameras. These results show that facial re-identification under real-world surveillance conditions and biometric context can be successfully performed using correlation filters adequately designed.
Identifying the Structure of Students’ Explanatory Essays

Recent educational standards stress that students should learn how to read and understand scientific explanations and create explanations of their own. But these skills are difficult for teachers to evaluate, so they often assess them at a shallow level or avoid giving such assignments. Previous approaches for automatically evaluating explanatory and other types of structured essays have relied on the use of shallow features or bag-of-words methods. These methods might allow for a reasonable holistic assessment of an essay, but they fail to identify which concepts students included and which causal connections they made. In this paper, we investigate which natural language processing methods are most successful at locating conceptual information in student explanations and the causal connections between them. We found that a combination of a recurrent neural network for identifying concepts along with a novel causal relation parser produced very good accuracy in two different scientific domains, significantly improving on the prior state-of-the-art.
A Novel Network Data Privacy Protection Algorithm in Big Data

With the advancement of power technology and the popularity of the Internet, power data is increasingly becoming an important factor of production. The traditional data privacy protection algorithm has three problems: long execution time, high concealment rate and poor adaptability. In this paper, a network data privacy protection algorithm based on density clustering is proposed to solve these problems. In the power big data environment, a transformation function that satisfies the characteristics of the network privacy data in a given big data environment is randomly generated. Then the transformation function is used to transform the value of the original network privacy data, and the transformed value is randomized as the result of the random answer. After the density clustering analysis of the network nodes according to the characteristics of the network, the system generates clusters of arbitrary shapes that suit the size. We are able to quantify the validity of the private data by quantifying the amount of information loss of the private data of the network and the amount of information loss of the network privacy data structure Finally, the real network node is inserted into the successfully generated cluster, and the privacy protection of the network data information is completed by adding technologies such as edges.