Reviewerly/exHarmony huggingface dataset:
INNOVATIVE TECHNOLOGIES AS A MEANS OF FORMATION OF VISUAL-COMMUNICATION ENVIRONMENT OF THE MODERN CITY

&#x0D; &#x0D; &#x0D; The article considerers innovative technologies as a means of formation of a visual-communication environment of the modern city. The article estimates the impact of innovative technologies on the architectural appearance of the modern city, the role of these technologies in the improvement of anthropogenic and natural elements of the urban environment is investigated. The points of view of specialists from different areas about the concepts of “city”, “urban environment” are presented, the current trends in the study of the phenomenon of the modern city and its component system are analyzed. The definition of the visual-communication environment of the modern city is given, attention is focused on its distinctive features. The five prevailing innovation directions (architectural bionics, adaptive architecture, arcology, green architecture, smart architecture) are defined, the principles and technologies of which are introduced into the urban environment. Characteristic features are studied and the followers of these trends are enumerated, as well as their realized examples of introducing the innovative directions into the modern cities’ environment. The ability of the visual communication environment to adapt and transform is considered. An analysis is made of the role of a man as a resident-user of the modern city in the chain of relationships between the architecture and innovation.&#x0D; &#x0D; &#x0D;
Weighted measurement fusion Fitting Kalman Filter for Multi-sensor Nonlinear Systems

For multi-sensor nonlinear systems, a new weighted measurement fusion (WMF) method is presented based on fitting Kalman filter (FKF), referred to as WMF-FKF, to enhance the accuracy of the state estimation. The main idea of WMF-FKF is FKF, which is suitable for nonlinear filtering. To evaluate the estimation performance, the simulation is carried out and the results are compared with other fusion algorithms. The results demonstrate that WMF-FKF provides better accuracy than other fusion algorithms and has lower computational cost.
Moving Hand Segmentation from H.264 Compressed Sign Language Videos

Hand segmentation is the most critical step in sign language recognition. Many advanced video applications require manipulation of compressed domain video signals. Storage and network bandwidth are the main constraints due to which the video signals are needed to be compressed. Therefore, we proposed a system to segment hand gesture from a compressed domain video file. The only cue used for segmentation is the motion vectors (MVs). First, the MVs are preprocessed to get the motion information accurately that are related to the moving hand. The specific block’s decision belongs to motion or no motion is done using classifiers based on Markov random field (MRF). The experimental result of this paper includes the testing of the proposed system for American Sign Language (ASL) and Indian Sign Language (ISL) under different environmental conditions of the background. The proposed system shows an average precision, recall, and F-measure of around 94.19%, 62.14%, and 75.11%, respectively.
Distortion Tolerance Data Hiding using Optimization Technique

This paper gives an effective spatial area based picture concealing plan, utilizing Molecule Swarm Streamlining (PSO) with capsule encryption technique. First the plain text was encrypted using capsule encryption method after the PSO method was used to hide the encrypted data into the image. Partical swarm optimization is utilized to locate the finest pixel areas in a dark scale picture pixel information can be installed. The target capacity for PSO is characterized in a manner that mutually for quality and heartiness of the stego picture are adequate. This outcomes in a stego picture which is great in quality as well as ready to handle certain clamor and pressure assaults amid transmission. The outcomes when contrasted and some late information concealing procedures in the spatial area show better stego picture quality at the side of contortion resistance.
Influence of PM2.5 Concentration on Photovoltaic Power Generation Based on Grey Correlation Degree Theory

This paper obtains the quasi-optimal factors and quasi-optimal characteristics of photovoltaic power generation system based on the grey correlation degree model of the interactions between system characteristics and system factors. The system characteristics include solar radiation, photovoltaic module temperature and photovoltaic system power generation; the system factors are PM2.5 concentration and ambient temperature. Based on the annual measured data from the 5.3kW photovoltaic power generation system of Beijing University of Civil Engineering and Architecture, the grey correlation degree was obtained with 42 environmental conditions under 6 PM2.5 level and 7 temperature levels. It is found that PM2.5 mainly affects the module temperature in low temperature, while in high temperature it mainly affects the radiation amount, thereby reducing the power generation of photovoltaic modules.
An Efficient and Compact Review of Face Recognition Techniques

Face detection deals with the specified object(face) within the given database. Several algorithms have been de-fined by different researchers for face recognition. Research, technology advancement and applications incorporating face recognition over the last few decades have grown enormously. It is growing as one of the profound and exciting research fields. Some of the practical and efficient algorithms for face recognition are Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Feature based approach, Gabor wavelet, GPU based approach, 3D model-based face recognition, Linear Discriminant Analysis (LDA) and Using Facial Symmetry. Face recognition is a multidimensional field. Different algorithms give different performances in different situations like illumination, noise, pose and disguise change. All the aforementioned techniques are described briefly in this paper so as to give the general idea. The main focus of the paper is to bring all the different techniques at the same place and make it easy to review them.
Using deep learning to classify English native pronunciation level from acoustic information

The main purpose of this research is to test the use of deep learning for automatically classifying an English learner’s pronunciation proficiency, a step in the construction of a system that supports second language learners. Our deep learning dataset consists of 28 speakers – ranging in proficiency from native to beginner non-native – reading the same 216-word English story. In the supervised deep learning training process, we first label the English proficiency level of the data, but this is a complicated task because there are a number of different ways to determine someone’s speech proficiency. In this research, we focus on three elements: foreign accent, speech fluency (as measured by total number of pauses, total length of pauses, and speed of speech) and pronunciation (as measured by speech intelligibility). We use Long Short-Term Memory (LSTM) layers for deep learning, train a computer on differently labeled data, test a computer on separate data, and present the results. Features used from audio data are calculated by Mel-Frequency Cepstrum Coefficients (MFCCs) and pitch. We try several combinations of parameters for deep learning to find out what settings are best for our database. We also try changing the labeling method, changing the length of each audio sample, and changing the method of cross-validation. As a result, we conclude that labeling by speech fluency instead of by speech intelligibility tends to get better deep learning test accuracy.
Classification of Natural Images Using Machine Learning Classifiers on Graph-Based Approaches

Local descriptors have become more and more popular and are gaining a high recognition in recent years in texture analysis and other domains of image processing and computer vision due to their capability in identifying unique and distinct features which are robust to various conditions. Most of the local descriptors like local binary pattern (LBP), textons and Peano scan motif considered the neighborhood as a simple window and extracted the features. This paper derives a graph structure on a local grid. The local features are derived based on transitions between adjacent vertices. This paper derives a dual graph function using the neighborhood property that exists between a vertex V and two of its neighbors V1 and V2 which are connected with vertex V. This paper initially divides the texture image into 2 × 2 non-overlapped grids and derives dual transition function and derives a dual graph unit (DGU) and replaces the 2 × 2 grid with DGU. The co-occurrence matrix derived on DGU indexed image represents dual graph texture matrix (DGTM). The gray level co-occurrence matrix (GLCM) features are derived on DGTM, and these feature vectors are given as inputs to the machine learning classifiers for classification. The proposed local DGTM is compared with state-of-the art local-based approaches, and the results on five popular databases exhibit the efficacy of the proposed DGTM over the existing local-based methods.
Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement

Collaborative filtering (CF) algorithm uses the preferences expressed by previous users of items being studied and is widely applied to build recommender systems. A collaborative filter predicts items that a user will like based on the vote similar users gave to that item. In this study, we use CF to estimate how much the knowledge of the presence or absence of one software feature can contribute to the correct prediction of the presence or absence of each of the possible remaining features. Completed software project documentations from the Master in Information Technology programs of selected Northern Luzon higher education institutions were first collected. An analysis of these documents revealed 26 unique software features and yielded a binary matrix indicating the presence or absence of a feature in a specific project. Leave-one-out cross-validation was performed to estimate the predictive power of each element of a given holdout vector, using the 26x26 cosine similarity matrix generated from the remaining vectors. The results show that, on average, knowing correctly the presence or absence of only 1 feature can predict with an accuracy of about 58% the presence or absence of the remaining features. This is 8% better than that of a naïve 50-50 random binary guessing algorithm, and somehow indicates the amount of information contributed by one feature value under the CF algorithm.
Study on Remote Settings Modification of Automatic-Safety Device

Remote settings modification technology is one of the key study subjects to achieve integration of dispatch and control in power grid. It deserves a further study in ensuring the security and prevention of information distortion during the delivery to Automatic-safety device after modifying and operating the settings file. Currently, there are many methods to achieve remote settings modification. After studying and analyzing two common cases, an optimized remote settings modification technology based on the five levels of security defense mechanism is first developed; Then, the foundations for remote settings modification mode that include the exclusive format, exclusive setting platform and identity verification mechanism are detail introduced. By comparing two common cases, the result shows that application of optimized remote settings modification technology has highly flexibility and can efficiently reduce the difficulty and complexity for operators. Also, the operation security is improved by applying the five levels of security defense mechanism. In summary, the application of remote settings modification technology has better prospects.
Digital Educational Resources in the Professional Activities of Educators

The relevance of the article lies in a comprehensive assessment of the use of electronic textbooks in the educational process.The article provides an overview of existing digital educational tools. It is shown that electronic resources play a dual role: they can effectively introduce the information environment into educational activities and require a complete transformation of the content, methods and organizational forms of training. The main content of the article is analysis of the types of digital educational resources used in the educational process, as well as effectiveness of their use.For an objective assessment of teachers’ work skills in using electronic textbooks, a list of digital educational resources is offered that they use in everyday and professional activities. The pedagogical tasks are determined, for the implementation of which digital educational resources are used. Features of their use by teachers are analyzed depending on the age of the teacher, length of service and qualification category. The article assesses the level of understanding by teachers of threats in the field of information security.The interpretation of the research results presented herein can serve as the basis for further research.
A Hybrid Method for Training Convolutional Neural Networks

Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the feature learning process. In the hearth of training deep neural networks, such as Convolutional Neural Networks, we find backpropagation, that by computing the gradient of the loss function with respect to the weights of the network for a given input, it allows the weights of the network to be adjusted to better perform in the given task. In this paper, we propose a hybrid method that uses both backpropagation and evolutionary strategies to train Convolutional Neural Networks, where the evolutionary strategies are used to help to avoid local minimas and fine-tune the weights, so that the network achieves higher accuracy results. We show that the proposed hybrid method is capable of improving upon regular training in the task of image classification in CIFAR-10, where a VGG16 model was used and the final test results increased 0.61%, in average, when compared to using only backpropagation.
Theoretical aspects of superconducting materials as its characteristics and applications in physics

Superconductors are demonstrated to profoundly differ in arrangement yet elusive and mysterious. To understand the Josephson Effect, it is advisable to first acquire some notion about the origin of the superconductivity. Macroscopically, superconductivity manifests itself as a lack of resistance, so that the current flows with no obstacles. The practical applications of superconductivity are consistently improving each year. However, the real utilization of superconducting devices is constrained by the way that they should be cooled to low temperatures to move toward becoming superconducting. The revelation of high-Tc superconductors broadens the feasible application of superconductors. The Josephson Effect has enabled the advancement of unique electrical devices and systems including SQUIDs, quantum computers, analog to digital (A/D) converters, Josephson voltage standard, THz emitters and detectors, single flux quantum devices, etc. In this research paper, we studied the Superconducting materials, its characteristics and applications in physics.
Evaluation of Complex Network Layout Based on User Cognition

Although evaluation metrics for complex network layouts have been studied by many authors, it is still unclear whether these metrics can effectively evaluate cognitive performance of information. Therefore, we conducted user research on the cognition of nodes information of complex networks. Based on user response time and accuracy of the information, the paper makes a multiple regression model to sieve effective metrics and evaluate the layout. The results of model show that the screening metrics can effectively evaluate the response time and accuracy of the cognitive information in complex network layout. According to the results, we designed and developed a visual tool to help us better evaluate and optimize complex network layout algorithms from the perspective of users, and improve the efficiency of users' information acquisition.
A Co-clustering Algorithm for the Domain Knowledge Model Construction of Electric Power Simulation Training System

With the rapid development of power grid, it is of great significance to develop an intelligent electric power simulation training system with adaptive learning characteristics to improve the ability of operation, maintenance, and dispatching personnel. This paper puts forward an idea for the construction of electric power domain knowledge model, which is one of the basic parts in the system, and establishes a co-clustering algorithm for the electric power knowledge units. Considering the complexity of knowledge structure in the electric power field, the algorithm adopts a quality measure method generalized to the multiple object spaces and multiple feature spaces in this paper to evaluate the clustering results. Combined with the iterative calculation of the measure value, the optimization of clustering quality is realized.
A Bigram-based Inference Model for Retrieving Abbreviated Phrases in Source Code

Expanding abbreviations in source code to their full meanings is very useful for software maintainers to comprehend the source code. The existing approaches, however, focus on expanding an abbreviation to a single word, i.e., unigram. They do not perform well when dealing with abbreviations of phrases that consist of multiple unigrams. This paper proposes a bigram-based approach for retrieving abbreviated phrases automatically. Key to this approach is a bigram-based inference model for choosing the best phrase from all candidates. It utilizes the statistical properties of unigrams and bigrams as prior knowledge and a bigram language model for estimating the likelihood of each candidate phrase of a given abbreviation. We have applied the bigram-based approach to 100 phrase abbreviations, randomly selected from eight open source projects. The experiment results show that it has correctly retrieved 78% of the abbreviations by using the unigram and bigram properties of a source code repository. This is 9% more accurate than the unigram-based approach and much better than other existing approaches. The bigram-based approach is also less biased towards specific phrase sizes than the unigram-based approach.
Private STaaS with OpenStack Cinder Volumes for Hybrid/Multi-cloud

Storage as a Service (STaaS) is in a very advanced stage and 80-90% of the users trust and use public cloud storage. However, enterprises and communities are still skeptical and are concerned with data safety and theft, and are investing to build their own private storage system, where cost of storage space is not the most important concern. This limits resource (compute and network) scale at the peak on demand in hybrid/multi-cloud environment as one cannot keep buying and upgrading computing hardware resources to support peak workloads in private cloud [9]. There are commercial solutions offered by few public cloud service providers/brokers to maintain the data in a private cloud in a hybrid environment, but this may have vendor/broker lock-in and portability/migration issues. In this paper, we explore OpenStack cloud for private Storage as a Service in hybrid/multi-cloud environment and demonstrate with our framework (middleware), which is built upon OpenStack [16], an open source cloud and by leveraging existing OpenStack functionalities.
A nonparallel support vector machine with pinball loss for pattern classification

In this paper, we propose a nonparallel support vector machine with pinball loss (Pin-NPSVM) that deals with the noise sensitivity and resampling instability of NPSVM. More specifically, we redefine a pinball loss funtion and build a pair of quantile hyper-planes. Each quantile hyper-plane is const ructed by using the new pinball loss instead of ɛ-insensitive loss, which makes the new classification model be insensitive to noise samples, especially for feature noise samples around the decision boundary. Moreover, instead of hinge loss, Pin-NPSVM also builds a pair of decision boundaries based on traditional pinball loss, which further improves the anti-nosie ability of the classification model. In a word, Pin-NPSVM not only inherits the characteristics of the nonparallel optimal hyper-planes, but also has a consistent model with Pin-SVM, which can process noise data well. Finally, numerical experimental results show that the Pin-NPSVM has more obvious advantages than other models in classification performance, especially for noise datasets.
Learning based Approximation Algorithm: A Case Study in Learning through Gaming

With new learning technologies springing up, gaming also plays a vital role in creating learning experiences. Studies show that games play an active role in defining active and creative learning activities. For effective learning through game based activities, games need to be well defined, interactive and challenging. Gamification can be applied in the context of learning using Atificial Intelligence. It is understood that game development is one of the use cases for AI. An attempt had been made to gamify learning using a supervised learning based approximation technique. The case study is done with TRON game using an alternate Voronoi heuristics, employing supervised learning. The algorithm has been tested for different game scenarios and the proposed game bot was tested for accuracy of moves, winning percentage and response time.
Scientific Map Creator: A Tool to Analyze the Author’s Affiliation and Citations by Producing Mind Maps

Citations have been considered a parameter to gauge the importance of research paper in the scientific society. Scientific societies consider citations to evaluate the authors to rank them for deserving positions such as assigning those researchers PHD supervisions, presenting them award, hiring them as a reviewer or editor in the Journal. With the help of “Scientific Map Creator”, we can provide web-based concept mapping application with a Database. Our main research focus is to extract the information from the bulk of data. The data that has been stored in several online databases for research papers such as JUCS, IEEE Explore and Scopus is intended to be managed and extracting influential author and present that meta data in the form of mind maps with the help of “Scientific Map Creator”.
MOMENT

This paper deals with the problem of temporal meta-fact generation in RDF knowledge graphs (KGs). These temporal meta-facts represent the time validity of facts, for instance, <Barack Obama, president Of, United States of America> is valid for the period [2008..2016]. We propose an approach called MOMENT that combines two methods, the first uses a set of specified rules to generate meta-facts in knowledge bases where no temporal meta-fact exist. The second method exploits existing temporal meta-facts and a set of Horn rules generated by AMIE [9] to propagate the meta-facts and thus expand the set of temporal meta-facts. An experimental evaluation has been conducted using Yago, DBpedia and Wikidata datasets. The obtained results are promising and showed the relevance of such an approach for temporal meta-fact generation in Knowledge Graphs.