Reviewerly/exHarmony huggingface dataset:
Symmetric I* Restriction Method of Fuzzy Inference

As one of important parts of fuzzy logic, fuzzy inference plays a vital role in the fields of fuzzy control, artificial intelligence, affective computing, image processing and so forth.Two key problems of fuzzy inference are FMP (fuzzy modus ponens) and FMT (fuzzy modus tollens).How to get the ideal solution for FMP and FMT is a difficult problem in the area of fuzzy logic.Aiming at such problem, from the idea of symmetric implicational reasoning, triple I* method and restriction theory, we put forward and investigate the α-symmetric I* restriction method, and then generalize it to the α(x,y)-symmetric I* restriction method.To begin with, the α-symmetric I* restriction principle and the α(x,y)-symmetric I* restriction principle are established.Furthermore, the equivalent condition to let a basic restriction solution exist is given.Then the unified solutions of the α-symmetric I* restriction method and the α(x,y)-symmetric I* restriction method are achieved for R-implications and (S, N)-implications.Besides, some special cases of optimal solutions are shown.Finally, the corresponding conclusions are provided when the two methods degenerate into the α-triple I* restriction method and α(x,y)-triple I* restriction method.These research results would be an important improvement for the fields of fuzzy inference, fuzzy logic and related applications.
Marine Integrated Database Management System Based on Improved Object-Relational Mapping Technology

Jia, Z., 2019. Marine integrated database management system based on improved object-relational mapping technology. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 133–136. Coconut Creek (Florida), ISSN 0749-0208.In order to adapt to the development of information technology and prevent “data chimneys” and “data islands” caused by data isolation among multiple systems in the ocean, this paper proposes an improved adaptive object-relational mapping (ORM) framework and builds a feasible set based on this framework. The integrated marine database management system integrates all data related to the marine field into a comprehensive marine database to achieve unified management and unified maintenance of data and also provides strong data support for the marine information system by establishing a reasonable data retrieval mechanism.
Computer-aided Design in the Teaching of Physics as a Means of Introducing Elements of STEM Education in Institutions of Higher Technical Education

The article analyzes computer-aided design systems, which are advisable to implement in the educational process of studying physics in the ZVTO, taking into account the principles of STEM education. The basic functionalities of computer-aided design systems for Windows are considered: management of design documentation, parametric solid-state modeling (heavy, high or middle class), computer-aided design of technological processes, engineering design applications. The object of study is a methodology for teaching physics on the basis of computer-aided design systems with the allocation of the main elements of STEM education in ZVTO. The aim of the article is to analyze the content of physics education as a result of the introduction of software and hardware for computer-aided design systems based on STEM education technologies in the vocational training system for technical specialists. We have determined that design activities encompass the technical and engineering components of STEM education in physics education, taking into account interdisciplinary and integrated approaches, and disclose formed STEM competencies in subjects of education. Note that the leading means of STEM-education in the technical field should include computer-graphic. Analyzing the development of innovative activities and modern ICT, the entire design process is performed on a computer using two-dimensional and three-dimensional graphics technologies. Accordingly, computer graphics is a system of methods, algorithms, software for entering, processing and displaying graphic information, converting data into a graphic form that reflects the technical elements of STEM education. It is an important component of modern information systems and computer-aided design systems operate on its basis. The prospect of further research is the development of methods of teaching physics based on computer aided design taking into account the principles of STEM education in institutions of higher technical education.
VIDEO ANIMATION AS TEACHING MEDIA IN ELT

The issue of TPACK (Technology Pedagogical Content Knowledge) offers a problem solution for teachers who are interested in implementing blended learning. Education and Technology have become an important aspect in every student's daily life beyond the school and within the classroom. Experts now create software that facilitates teachers in creating animated videos which contain pedagogical knowledge. One of them is Videoscribe which can be used to present learning content by combining interesting images, sounds, and graphs to attract students to enjoy the learning process. The features provided by this software are varied, they enable teachers to design learning media that can be adjusted to meet the goal of the lesson. This paper will elaborate on how teachers can design an interesting and engaging learning media to meet the students’ needs and to achieve learning objectives by using the software.
Information system of semantic search of the image to the text of advertising announcement

In connection with the rapid development of information technology, modern society is in need of solving the problems of the accumulation, storage and automated processing of semiotically heterogeneous arrays of texts. An important task of the developed technologies is the search for operational methods for issuing a wide variety of information in accordance with the user’s request. The aim of the proposed work is to develop a computer information system that allows you to select the most appropriate illustration for the given text of the advertisement. In this case, the main content of the text is proposed to be presented in the form of a certain set of main and secondary keywords, and the content of the illustration in the form of a multi-level complex of descriptors. The proximity of these contents (and, accordingly, the correlation between the image and the verbal component of the advertisement) can be determined through the maximum number of matches of support words and descriptors, taking into account their importance. An important feature of this system is the use of a thesaurus method of representing the image content, which is a description of the illustration in the form of a certain set of words and the relations between these words within a certain subject area.
Combinatorial Algorithms for String Sanitization

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge. In this paper, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility, in two settings that are relevant to many common string processing tasks. In the first setting, we aim to generate the minimal-length string that preserves the order of appearance and frequency of all non-sensitive patterns. Such a string allows accurately performing tasks based on the sequential nature and pattern frequencies of the string. To construct such a string, we propose a time-optimal algorithm, TFS-ALGO. We also propose another time-optimal algorithm, PFS-ALGO, which preserves a partial order of appearance of non-sensitive patterns but produces a much shorter string that can be analyzed more efficiently. The strings produced by either of these algorithms are constructed by concatenating non-sensitive parts of the input string. However, it is possible to detect the sensitive patterns by ``reversing'' the concatenation operations. In response, we propose a heuristic, MCSR-ALGO, which replaces letters in the strings output by the algorithms with carefully selected letters, so that sensitive patterns are not reinstated, implausible patterns are not introduced, and occurrences of spurious patterns are prevented. In the second setting, we aim to generate a string that is at minimal edit distance from the original string, in addition to preserving the order of appearance and frequency of all non-sensitive patterns. To construct such a string, we propose an algorithm, ETFS-ALGO, based on solving specific instances of approximate regular expression matching.
An Adaptive Pixel Selection Method based on Block-Complexity Estimation for Quality Enhanced Steganography

This work offers a quality enhanced method of content-adaptive image steganography. The proposed method is divided into three main steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is divided into small local regions and the pixel complexity is identified based on a high pass filter bank and the proposed Least Smoothness Prior (LSP) criterion. Following the criterion, seven complexity levels are defined and a block is assigned from one of the seven levels. The data embedding for the higher complexity levels then takes place using a highly efficient algorithm. Experimental results verify the preservation of visual quality of stego image produced by the proposed method. Two image datasets: SIPI and BOWS2 are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ metrics: WPSNR and SSIM show the effectiveness of the proposed algorithm.
Direct Estimation of Surface Strain Fields From a Stereo Vision System

Estimating strain on surfaces of deforming three-dimensional (3D) structures is a critical need in experimental mechanics. Although single-camera techniques excel at estimating deformation on a surface parallel to the imaging plane, they are prone to artifact for 3D motion because they cannot distinguish between out-of-plane motion and in-plane dilatation. Multiview (e.g., stereo) camera systems overcome this via a three-step process consisting of: (1) independent surface registration, (2) triangulation to estimate surface displacements, and (3) deformation estimation. However, existing methods are prone to errors associated with numerical differentiation when computing estimating strain fields from displacement fields unless regularization schemes are used. Such regularization schemes can introduce inaccuracy into strain estimation. Inspired by previous work which combined registration and deformation estimation into a single step for 2D images and 3D imaging stacks, we developed a theory for simultaneous image registration, 3D triangulation, and deformation estimation in a multiview system. The deformation estimation does not require numerical differentiation of displacement fields to estimate strain fields. We present here the theoretical foundations and derivation of two related implementations of this approach, and discuss their strengths and weaknesses.
Robust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms

With the guaranteed discrimination and efficiency of spatial appearance model, Discriminative Correlation Filters (DCF-) based tracking methods have achieved outstanding performance recently. However, the construction of effective temporal appearance model is still challenging on account of filter degeneration becomes a significant factor that causes tracking failures in the DCF framework. To encourage temporal continuity and to explore the smooth variation of target appearance, we propose to enhance low-rank structure of the learned filters, which can be realized by constraining the successive filters within a $\ell_2$-norm ball. Moreover, we design a global descriptor, structural color histograms, to provide complementary support to the final response map, improving the stability and robustness to the DCF framework. The experimental results on standard benchmarks demonstrate that our Implicit Low-Rank Constraints and Structural Color Histograms (ILRCSCH) tracker outperforms state-of-the-art methods.
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that networks trained on less imbalanced subsets of the distribution often yield better performances than their jointly-trained counterparts. We refer to these models as 'Experts', and the proposed LFME framework aggregates the knowledge from multiple 'Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of adaptive learning schedules: Self-paced Expert Selection and Curriculum Instance Selection, so that the knowledge is adaptively transferred to the 'Student'. We conduct extensive experiments and demonstrate that our method is able to achieve superior performances compared to state-of-the-art methods. We also show that our method can be easily plugged into state-of-the-art long-tailed classification algorithms for further improvements.
SOCIAL-PSYCHOLOGICAL SUPPORT OF THE PROCESS OF FORMATION OF THE GIFTED PERSON

The article reveals the socio-psychological mechanisms of the formation of a gifted person; The ways of transformation of the educational process with the aim of increasing its efficiency, integrity and organicity, creating a psychological and pedagogical system of becoming a gifted person in the conditions of pre-university and university education are shown. The qualitative characteristics are given, allowing to determine the level of creative activity at different stages of the formation of a gifted person; proposed a proven in practice socio-psychological system of supporting the process of becoming a gifted person, based on the creation of a developing educational environment as a specially organized socio-cultural and educational space in various types of educational institutions. A generalized model of a gifted person has been developed, qualitative and quantitative criterion indicators for assessing gifted activity have been described, methodological tools have been proposed for diagnosing the level of its formation at different stages of the formation of a gifted person; characterized by developing educational environment as a socio-cultural space; the concept of the formation of a gifted person in the conditions of a developing educational environment as a combination of scientific, theoretical and methodological grounds has been developed; identified and described mechanisms for the implementation of this process in a developing educational environment; A scientifically based and experimentally proven model of a socio-psychological system aimed at the process of becoming a gifted person integrating the structure, content, scientific and methodological substantiation and technological support of this process in a developing educational environment has been proposed.
Semantic Similarity Using Register Linear Question Classification (RLQC) for Question Classification

Question Classification(QC) mainly deals with syntactic parsing for finding the similarity. To improve the accuracy of classification, a semantic similarity approach of a question along with the question dataset is calculated. The semantic similarity of the question is initially achieved by syntactic parsing to extract the noun, verb, adverb, and adjective. However, adjectives and adverbs do give sentences an exact meaning that should also be considered for computing the semantic similarity. The proposed RLQC (Register Linear and Question Classification) model for semantic similarity of questions uses HSO (Hirst and St. Onge) measure with Gloss based measure to enhance the semantic similarity relatedness by considering the Noun, Verb, Adverb and Adjective. The semantic similarity of the question pairs for RLQC is 0.2% higher compared to HSO model. The highest semantic similarity of the proposed model achieves a better accuracy.
Lifting Coalgebra Modalities and MELL Model Structure to Eilenberg-Moore Categories.

A categorical model of the multiplicative and exponential fragments ofintuitionistic linear logic ($\mathsf{MELL}$), known as a \emph{linearcategory}, is a symmetric monoidal closed category with a monoidal coalgebramodality (also known as a linear exponential comonad). Inspired by Blute andScott's work on categories of modules of Hopf algebras as models of linearlogic, we study categories of algebras of monads (also known as Eilenberg-Moorecategories) as models of $\mathsf{MELL}$. We define a $\mathsf{MELL}$ liftingmonad on a linear category as a Hopf monad -- in the Brugui{\`e}res, Lack, andVirelizier sense -- with a special kind of mixed distributive law over themonoidal coalgebra modality. As our main result, we show that the linearcategory structure lifts to the category of algebras of $\mathsf{MELL}$ liftingmonads. We explain how groups in the category of coalgebras of the monoidalcoalgebra modality induce $\mathsf{MELL}$ lifting monads and provide a sourcefor such groups from enrichment over abelian groups. Along the way we alsodefine mixed distributive laws of symmetric comonoidal monads over symmetricmonoidal comonads and lifting differential category structure.
Late payment prediction of invoices through graph features

Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily. Within the different types of cash flow, Accounts Receivable (AR) classifies the balance of money that needs to be paid by the company's customers. In the most typical case, after receiving goods or services, the customer receives an invoice with the amount that is owed to the supplier. However, this often does not happen before the aforementioned date, meaning that the invoice is often paid late. Intervention requires resources and over-intervention could cause unwanted customer dissatisfaction. Knowing whether an invoice is going to be paid late can be vital information. Current methods of late payment prediction focus only on the history between the seller and the buyer and are unusable when this history is not present. Intuitively, one's business depends on the relationships and transactions that it has with its neighbors. Suggesting that neighbor behavior could be useful when predicting the cash flow of a company. Unfortunately, this type of information is not always given and needs to be data mining from non-relational data. This work presents a method for building a relational network of SMEs using entity resolution and improving the current state of the art of late payment prediction using features extracted from the graph.
KESALAHAN BERBAHASA PADA KARANGAN GURU BAHASA INDONESIA SMP SELAMPUNG UTARA

The problems discussed in this study is language errors in the essays ofIndonesian language teacher of junior high school in Kotabumi, LampungUtara. This study aimed to describe teachers' language errors in spelling,diction, morphology, and sentences. The method used in this research was descriptive which means that data sources are in the form of the teacher’s essays. From the analysis the spelling errors that can be identified is the punctuation errors include the use of comma, colon, semicolon, quotation, single quote, and hyphen; the errors on writing include the use of capital letters, foreign language element, numbers, and word combination. Diction errors include the use of non-standard words and words that inappropriate. Morphology errors include the use of the incorrect word formation and the words are not in accordance with a sentence. The errors on the use of sentence include a mismatch, the vagueness of subject and predicate, the absence of a subject, predicate, and object in the sentence, and the sentence which only has a caption, the use of coordinator and subordinator conjunction at the beginning of single sentence, and incoherence.
Domain Adaptation Regularization for Spectral Pruning

Deep Neural Networks (DNNs) have recently been achieving state-of-the-art performance on a variety of computer vision related tasks. However, their computational cost limits their ability to be implemented in embedded systems with restricted resources or strict latency constraints. Model compression has therefore been an active field of research to overcome this issue. Additionally, DNNs typically require massive amounts of labeled data to be trained. This represents a second limitation to their deployment. Domain Adaptation (DA) addresses this issue by allowing knowledge learned on one labeled source distribution to be transferred to a target distribution, possibly unlabeled. In this paper, we investigate on possible improvements of compression methods in DA setting. We focus on a compression method that was previously developed in the context of a single data distribution and show that, with a careful choice of data to use during compression and additional regularization terms directly related to DA objectives, it is possible to improve compression results. We also show that our method outperforms an existing compression method studied in the DA setting by a large margin for high compression rates. Although our work is based on one specific compression method, we also outline some general guidelines for improving compression in DA setting.
Improved Social Spider Algorithm via Differential Evolution

In this study, Social Spider Algorithm (SSA) is improved by combining it with the mutation strategies of Differential Evolution (DE) algorithm. Without altering the main operation of the SSA, we utilized the DE mutation strategies to diversify the individuals in the population. We selected two different mutation strategies to obtain two different hybrids and evaluated them. These mutation strategies are the most basic strategy and currently the best strategy of the DE algorithm proposed in the literature. Two different hybrids are evaluated via several tests performed on five different real-world problems. According to our results, the SSA is improved successfully. The hybrid utilizing the most basic strategy of the DE algorithm has given better results than the currently best strategy with an improvement 15.56% on average in all tests. It has been found that the currently best strategy resulted in worse performance probably since it provided more diversity on the population.
ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER

In computer vision, one of the complex research areas is video surveillance.It is very important to monitor the abnormal events in public places.Due to the technical advances, the usage of cameras is increased for surveillance purpose.As human operators are employed for the observation, their visual attention is reduced after long periods.Hence, an automated Abnormal Event Detection (AED) technique is designed in this study.It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway.Before modeling, a series of preprocessing steps are employed to detect the moving objects.Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event.Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED.