Reviewerly/exHarmony huggingface dataset:
Damage Information of Fiber Reinforced Concrete Based on Digital Image 3D-DIC Technology

The meso-scale study can reflect the failure nature of fiber reinforced concrete, and the meso-level analysis can be used as the basis for the study of macro-mechanical response of concrete under load. With the aid of 3D-DIC digital image technology, the micro-cracking damage experiment of fiber reinforced concrete is carried out, and the evolution characteristics of strain field in the process of compression loading are analyzed. According to the full-field strain nephogram, the crack propagation and evolution of concrete can be judged. The deformation and failure mechanism of fiber reinforced concrete material is studied by using 3D-DIC observation system, which can be used for fiber coagulation. The study of soil meso-deformation and failure mechanism provides important reference.
Donggan speech recognition based on deep neural network

Donggan language, which is a special variant of Mandarin, is used by Donggan people in Central Asia. Donggan language includes Gansu dialect and Shaanxi dialect. This paper proposes a Deep Neural Network based on Donggan language speech recognition method for the Donggan Shaanxi dialect. We designed a text corpus and a pronunciation dictionary for Donggan Shannxi dialect and recorded the corresponding speech corpus. Then we trained the acoustic models of Donggan Shaanxi dialect by deep neural network (DNN). Experimental results demonstrated that the recognition rate of proposed DNN-based method can achieve lower word error rate than that of the monophonic hidden Markov model (HMM)-based method and triphone HMM-based method.
Tools for Readability

Advances in the field of computational linguistics have provided some useful tools to help you assess and improve text readability (Vajjala and Meurers in The 7th workshop on the innovative use of NLP for building educational applications. Association for Computational Linguistics, Canada, 2012 [1]). By taking much of the guesswork out of the writing and editing process, these tools can help you make texts more accessible and more comprehensible for L2 readers. They are not prescriptive guides; they merely provide measurements and feedback to help identify areas of text difficulty. This chapter discusses three such tools that are appropriate and convenient for writing and editing texts for L2 readers.
A Hybrid Intelligence-Based Cognitive Engine

Demand for a reliable and adaptive intelligence generalization system has become an essential task to both the WCS's developers and its numerous services providers. Since WCS's spectrum is naturally known to be unstable, time-dependent and currently not only scarce in capacity but heavily congested and the impacts of its various services and its rapidly evolving applications are constantly making the system to be extremely complex. However as proposed by Mitola in 1999, Cognitive Radio (CR) has been developed with such intelligence capabilities and through it, the present-day WCS's spectrum complexity can be effectively managed, at the same time increases its scarce and the highly varying spectrum utilization particularly in complicated WCS's environments. To address this, CR system through its intelligence mechanism, which is also known as the Cognitive Engine (CE) enforces such adaptive intelligence functionalities to dynamically adjust its input parameters, observing its surrounding environment and ultimately makes its decision to meet the WCS's desired objective. This paper proposes the hybridization of two different Artificial Intelligence systems to design and implement an adaptive intelligence system for CR systems to predict the WCS's required objective.
A Novel Image Encryption Method Based On Chaotic Maps and Least Square Function

In this article a novel image encryption method is proposed which is based on chaotic mapping and least squares. This algorithm uses a 256-bit key to encrypt the input image. The initial conditions and control parameters are obtained from this key. The proposed method is implemented on gray scale and color images, and consists of three basic steps: preprocessing, shuffling and masking. In the preprocessing phase the input image is encrypted by Xor operation and then shuffling is applied by substituting the location of input image pixels. Finally in the masking step, input image is encrypted by producing estimation by least square function and Xor operation. In order to evaluate the proposed method NIST test suit is used, furthermore other testing methods such as key space analysis, histogram test, entropy, and encryption quality, MAE, NPCR and UACI are used. Denote that this innovation depicts significant speed increase in image encryption methods based on chaotic mappings.
Patterns of interrelation between perception and understanding of images and texts with different degree of blur

The efficiency of information processing depends on a holistic and integrated process of understanding. Eye tracking helps us to study the peculiarities of the understanding of visually perceived stimuli. The purpose of this study was to identify and characterize the relationship between perception and understanding, on the example of images and texts with different degrees of blurring (visual complexity). The work involved 25 participants, ages 19 to 25 years. Methods include poll, recording eye movements (eye tracking), wavelet filtering of text and images, and content analysis. Stimuli were consistently presented on the screen included text stimuli (from 7 texts), episodes from Russian fairy tales, and images (from 8 comics total), each consisting of eight pictures, united by one complete plot. Visual stimuli were presented in order from the clearest to the most blurred image. The results of the study showed that the participants made more gaze fixations on plot-relevant details and characters. They reveal key details of images for a general understanding of the content. The willful regulation of cognitive processes allows distinguishing the main characters and keywords to recreate or anticipate the meaning of visually perceived information constructs while reading texts or pictures with a high degree of blur.
Dimension Reduction by Word Clustering with Semantic Distance

InDeguchi, Toshinori informationIshii, Naohiro retrieval, Latent Semantic Analysis (LSA) is a method to handle large and sparse document vectors. LSA reduces the dimension of document vectors by producing a set of topics related to the documents and terms statistically. Therefore, it needs a certain number of words and takes no account of semantic relations of words. In this paper, by clustering the words using semantic distances of words, the dimension of document vectors is reduced to the number of word-clusters. Word distance is able to be calculated by using WordNet. This method is free from the amount of words and documents. For especially small documents, we use word’s definition in a dictionary and calculate the similarities between documents.
An Algorithm To Determine The Maturity Improvement Plan For Information System Risk Management. Application On A Case Study

A good and relevant Risk Management process is a key issue when Information System effective governance is concerned.Therefore, several paradigms have been devised to help achieving such goal.Among these paradigms, maturity models are quite popular.The main aim of a maturity model is to help users improve their activities capability.However, one of the major challenges encountered when using these models is the definition of the improvement plan after the evaluation.This challenge is all the stronger and costly when it comes to an activity whose elements or phases have an important interdependence such as IS risk management.In this article, we propose an algorithm called "Path Prerequisites" to help users define a graduate improvement plan, easily and efficiently, from a given maturity level to a target one, while handling criteria dependencies constraints.The algorithm is based on an acyclic graph representation of the control objectives and the dependencies among them and it corresponds to a guided (backwards) traversal of the graph.We assess the algorithm by applying it to a study case.
Fast Acquisition in Long Pseudo Code DSSS System Based on External Memory

In order to achieve acquisition in large Doppler shift, strong interference and low SNR environment with limited memory resource, a parallel frequency domain acquisition method based on external memory is proposed. The method can be applied to a long pseudo code system with a pseudo code length of 8192, and achieving fast acquisition within time of 64 symbols in large Doppler shift and low SNR environment. Meanwhile, the memory consumption problem in long pseudo code DSSS system is solved by using external memory, which can save more than 75% of on-chip memory. The method is simulated by Matlab and implemented on hardware platform. The result shows that the system can achieve acquisition when Es/N0 is 3dB.
On the Difficulties of Raising the Level of Abstraction and Facilitating Reuse in Software Modelling: The Case for Signature Extension

Reuse is central to improving the software development process, increasing software quality and decreasing time-to-market. Hence it is of paramount importance that modelling languages provide features that enable the specification and modularization of reusable artefacts, as well as their subsequent reuse. In this paper we outline several difficulties caused by the finality of method signatures that make it hard to specify and use reusable artefacts encapsulating several variants. The difficulties are illustrated with a running example. To evaluate whether these difficulties can be observed at the programming level, we report on an empirical study conducted on the Java Platform API as well as present workarounds used in various programming languages to deal with the rigid nature of signatures. Finally, we outline signature extension as an approach to overcome these problems at the modelling level.
SQUID

Graph databases such as chemical databases, protein databases, and RNA motif databases, are simply a collection of graphs. Querying a graph database involves the computation of a subgraph isomorphism problem (which is NP-complete) for each graph in the database. Therefore, an index is required to filter out false positives and reduce the number of subgraph isomorphisms to compute.
An Improved PSO Algorithm with an Area-Oriented Learning Strategy

The classical particle swarm optimization (PSO) trains the particles to move toward the global best particle in every iteration. So, it has a great possibility of being trapped into local optima. To deal with this issue, this paper improves the learning strategy of PSO. Therefore, an area-oriented particle swarm optimization (AOPSO) is proposed, which contributes to leading the particles to move toward an area surrounded by some suboptimal particles besides the best one. 10 test functions are employed to compare the performance of AOPSO with the classical PSO and 3 other improved PSOs. AOPSO performs the best in 5 test functions and relatively better than some of the other algorithms in the rest, which sufficiently demonstrates the effectiveness of AOPSO.
Augmented Reality as a Crucial Element in Modern Electric Distribution Company

The predominant goal of this article is to touch upon the usefulness of the so-called Augmented Reality (AR) technology with regard to its utilization in companies distributing electric power. AR is understood to be a real-time combination of real world imagery, typically in the form of images recorded by a camera, with digitally generated one. Additional elements have to be added to the camera-captured image in real time and the whole experience should be interactive in nature, as well as allow the user to move freely. After analyzing the current applications of the discussed technology, the authors propose its utilization within the scope of companies operating in the electric power-related branch of industry, with the major focus being put on enterprises specializing in electric power distribution, both in terms of network infrastructure management and organization of training sessions pertaining to key procedures and machine handling.
Integral Assessment of the Sustainability of Socio-Ecological and Economic Systems and the Quality of Life of the Population in the Regions of the Arctic Zone of the Russian Federation

Abstract The article deals with the integral assessment of the state and sustainability of socio-ecological and economic systems (SEES). The integral assessment of SEES, 9 regions of the Arctic Zone of the RF (AZR) and one region of the central Russia (Tver Region) is used as a foundation for the analysis of the state of SEES and the quality of life of the population, as well as the ability of SEESs to maintain their properties and quantitative characteristics in case of an external impact on the system or any intrasystem changes. Eight scenarios of impact on subsystems and systems as a whole at the regional level were developed to assess sustainability. The article presents the results of the evaluation of the quality of environment and the quality of life of the population in the regions in 2003-2015. The main goal of the study was to identify situations in which SEES, upon the impact assigned upon it, will not be able to maintain its properties and parameters and will move to another class, both in separate subsystems and as the whole system.
Mirror PCA: Exploiting Facial Symmetry for Feature Extraction

Feature extraction technique aiming at obtaining discriminative information from high-dimensional face images is of great importance in face recognition. One widely used method for extracting primary feature is Principal Component Analysis (PCA), which uses projection matrix for dimensionality reduction. There are many improvements of PCA but no one pays attention to the fact that both facial images and facial expression are symmetrical to some degree. Facial symmetry is a helpful characteristic, which benefits of feature extraction. In this paper, Mirror Principal Component Analysis (Mirror PCA) method is proposed for extracting representative facial features, which takes advantage of the facial symmetry in a face image. In order to verify the effectiveness of the proposed method, we compare the Mirror PCA method with other four methods on four famous face databases. The experimental results indicate that the representation capacity of our method is superior to others.
Based on Quantitative Assessment of Geological Structure Complexity by Support Vector Regression

In the study, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the gas content under five influential factors, including buried depth, bedrock depth and three quantitative assessment indexes (kd the fold structure complexity, kz the fold structure complexity, kq the inclined angle structure complexity). The prediction result demonstrated that the estimated error of the SVR model, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), is less than that of backpropagation neural network (BPNN) and multivariate linear regression (MLR) model by applying identical training and test samples. This study suggests SVR can be used as a powerful approach to foresee the gas content in coal with different geological structures.
Free Relatives, Feature Recycling, and Reprojection in Minimalist Grammars

This paper considers how to derive free relatives—e.g. John eats [ $$_{DP}$$ what Mary eats]—in Minimalist Grammars. Free relatives are string-identical to indirect questions—e.g. John wonders [ $$_{CP}$$ what Mary eats]. An analysis of free relatives as nominalised indirect questions is easy to implement, but empirical evidence points instead to wh-words ‘reprojecting’ in free relatives. Implementing a reprojection analysis in Minimalist Grammars requires innovations to revise the stipulation that the probe always projects the head, and to allow features to be reused non-consecutively.
Data Mining Approach of Text Classification and Clustering of Twitter Data for Business Analytics

The increasing popularity of micro-blogging sites like Twitter, which facilitates users to exchange short messages (tweets) is an impetus for data analytics tasks for business development. Twitter has a huge amount of data. Twitter’s API allows you to do complex queries like pulling every tweet about a certain topic. So, Companies can know more about consumers’ sentiments towards their products and services and use them to better understand the market and improve their brand. In this paper selected a popular food brand to evaluate a given stream of customer comments on Twitter. Several metrics in classification and clustering of data were used for analysis. A Twitter API is used to collect twitter corpus and feed it to a classifier algorithm that will discover the polarity lexicon of English tweets, whether positive or negative. A clustering technique is used to group together similar words in tweets in order to discover certain business value.
A Policy-Driven License Manager for Controlling Access to Arbitrary Applications

Seeking to fill a gap in licensed software access control solutions, a license management solution based on administrator-controllable policies is presented. This solution, Lisense, is a client-server style license manager and access control solution that can be applied to any application binary without the need to alter source code or recompile the application. While Lisense provides several builtin policies like seat counting and controlling number of concurrent users running a wrapped application simultaneously, it also allows definition of new, external policies via a simple Python API, providing a high level of flexibility to the administrator in designing and developing access control systems beyond those already available. In this paper, we place Lisense in context of other access control solutions available, discuss its implementation, show an example deployment, and utilize the API to write a custom policy.
Integration of Activity and Competency Assessment Methods

This article highlights the significance and importance of the application of interactive education in the teaching of the humanities. At the present day, integrated teaching of two, three and more subjects similar in content is extensively used in schooling. The authors of this paper reviewed innovative approaches to the integrated teaching of the subject “Manasologyc with such subjects as “History of Kyrgyzstan” and “Geography of Kyrgyzstan” in higher education institutions. One of important tasks set before the interactive lesson consists in free selection of independent works of students. The integration process enables carrying out a comprehensively wide, in-depth analysis of the training material.
Investigation and User’s Web Search Skill Evaluation for Eye and Mouse Movement in Phishing of Short Message

There are many studies on eye and mouse movement. However, there are not many studies that try to evaluate the skill of Web search while considering the relationship between the line of sight and the movement of the mouse. In this study, we analyze the data acquired from the viewpoint of the differences in information literacy of subjects and investigate the method of quantitatively evaluating the skill of web search.
Dynamic service composition in enterprise cloud bus architecture

Purpose Service composition phenomenon based on non-scenario aspects are become the latest issues in enterprise software applications of the multi-cloud environment due to the phenomenal increase in a number of Web services. The traditional service composition patterns are hard to support the dynamic, flexible and autonomous service composition in the inter-cloud platform. To address this problem, this paper aims to describe a dynamic service composition framework (SCF) that is enriched with various structural and functional aspects of composition patterns in a cloud computing environment. The proposed methodology helps to integrate various heterogeneous cloud services dynamically to acquire an optimal and novel enterprise solution for delivering the service to the end-users automatically. Design/methodology/approach SCF and different composition patterns have been used to compose the services present in the inter-cloud architecture of the multi-agent-based system. Further, the proposed dynamic service composition algorithm is illustrated using a hybrid approach, where service are chosen according to various needs of quality of service parameters. Besides, a priority-based service scheduling algorithm is proposed that facilitates the automation of delivering cloud service optimally. Findings The proposed framework is capable of composing the heterogeneous service and facilitate the structural and functional aspects of service composition process in enterprise cloud-based applications in terms of flexibility, scalability, integrity and dynamicity of the cloud bus. The advantage of the proposed algorithm is that it helps to minimize the execution cost, processing time and get better success rate in delivering the service as per customer’s need. Originality/value The novelty of the proposed architecture coordinates cloud participants, automate service discovery pattern, reconfigure scheduled services and focus on aggregating a composite services in inter-cloud environments. Besides, the proposed framework supported several non-functional characteristics such as robustness, flexibility, dynamicity, scalability and reliability of the system.
Augmented Reality in Laboratory’s Instruments, Teaching and Interaction Learning

This article presents the research carried out to develop an application of augmented reality for Android devices that allows to know and explore an Electrical and Electronic laboratory of the University of the Armed Forces ESPE, as a means of dissemination and learning of equipment as instruments with which they work in this laboratory. Such elements deploy the respective documentation, a virtualized model and in some cases a deployment in its components for the respective maintenance. The results obtained are based on the clear streamlining of the teaching method with the complementation of all the information that is appended to the application. This results in an aggregate use of the laboratory for the diversification and correct use of its elements. The use of the AR application generates an extension of the knowledge already acquired in classes.