Reviewerly/exHarmony huggingface dataset:
The Application of Augmented Reality to the Education of Chemistry – Take the Course of Nature Science in Junior High School as an Example

In the modern society featuring developed information technology, the integration of technology into various aspects of life has been normal. Studies regarding the integration of technology into educational scenarios have also flourished. This study will integrate the augmented reality (AR) technology into the teaching of natural science for the eighth grade of middle school. This technology assists learners in learning about chemical units, such as “mole number”, “atomic weight” and “molecular weight”, to understand the composition and characteristics of matter. The concepts of this unit are relatively abstract, so the characteristics of AR combined with learners’ life experience can present the world of microscopic particles, which are invisible to the naked eye and complex and abstract, enabling learners to establish correct rules and imagine the microscopic particle world properly and paving a smooth way for future chemistry learning.
The Differential Evolution Algorithm with a Fuzzy Logic Approach for Dynamic Parameter Adjustment Using Benchmark Functions

In this paper the main idea is to state that the use of fuzzy logic helps in the improvement of results in different optimization problems. For this particular paper we propose using the methodology of combining fuzzy logic with the differential evolution algorithm to perform experiments with a set of functions of the CEC2015, since these functions are more complicated than traditional benchmark functions.
Design of Interval Type-2 Fuzzy Systems for Classification of Blood Pressure Load

In this work we will design the interval type-2 fuzzy system (FS), which will be based on the definitions and classifications of the blood pressure loads, which are elaborated by cardiology experts and based on table of ranges, the use of an interval type-2 fuzzy system and the reliability of the FOU (footprint of uncertainty) can allows achieving a 100% effective classification, it should be noted that in previous works experiments with type-1 fuzzy systems have been carried out, the goal is to have a good classification of the blood pressure load which is very important nowadays for a cardiologist, since based on this determine a cardiovascular event, the blood pressure load which indicates that the daytime blood pressure load (% of diurnal readings ≥ 135/85 mmHg) and the nocturnal blood pressure load (% of nocturnal readings ≥ 120/70 mmHg).
Neural Network Based Traffic Sign Recognition for Autonomous Driving

Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer, time saving and to bring in environmental benefits. The road infrastructure still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car. This paper deals with a new Traffic Sign Recognitions system based on a pretrained neural network for general objects and retrained for a large number of traffic signs collected in real environment under different metrorological conditions. The proposed Traffic Sign Recognition system is able to classify signs in new scenarios with more than 96% accuracy.
A Comparative Study of Famous Classification Techniques and Data Mining Tools

Data mining is the procedure or technique of drawing out the facts and patterns hidden in huge sum of data and converts it into a readable and understandable form. Data mining has four main modules like classification, association rule analysis, and clustering and sequence analysis. The classification is the major module and is used in many different areas for classification problems. Classification process gives a summary of data investigation which may be utilized to develop models or structures, telling different classes or predict future data trends for improved understanding of the data at maximum. In this survey, various data mining classification techniques and some important data mining tools along with their advantages and disadvantages are presented. Data classification techniques are classified into three categories namely, Eager learners, Lazy learners, and other Classification techniques. Decision tree, Bayesian classification, Rule based classification, Support Vector Machines (SVM), Association rule mining and backpropagation (Neural Networks) are eager learners. The K-Nearest Neighbor (KNN) classification and Case Based Reasoning (CRT) are lazy learners. Other classification techniques include genetic algorithms, fuzzy logic and Rough Set Approach. Here six important data mining tools, basic Eager learner, Lazy learner and other classification techniques for data classification are discussed. The aim of this article is to provide a survey of six famous data mining tools and famous different data mining classification techniques.
Visual analytics of team performance in online gaming

We present a visual analytics approach to explore how successful teams collaborate, attack, and accumulate objectives. We illustrate our contributions with the Multiplayer Online Battle Arena: League of Legends. We developed two visualizations: the first shows collaboration and attack interactions, and how the players' roles are relevant to comprehending these interactions and the accumulation of power (gold) in this context; The second visual illustrates the objectives reached and the game evolution in terms of targets, helping to identify patterns in the sequence of goals. The two visuals designs were produced for each match and were used to be compared in a sample of matches, leading to pattern detection of successful practices. This approach enriches the comprehension of teams' performance in a competition environment and the identification of successful practices in team competition.
Towards an Integration Methodology for Multi-Cloud Application Management Platforms

Most organisations follow an open-source strategy when developing software products like cloud platforms. However, integrating various software pieces in a wrong way can lead to generating platforms that do not feature suitable non-functional properties and easily scale to handle an increase in customer base. Such platforms would thus not properly face the fierce competition in the cloud world. This paper proposes a novel methodology for selecting the best possible integration method and strategy for cloud platforms which enables addressing the previous challenges and producing platforms that can go beyond current competition. This methodology relies on investigating the current open-source platform components to be integrated and selecting the right integration method and strategy that leads to the best possible integration result by still keeping the integration effort to the minimum possible. This paper demonstrates its application on the MELODIC multi-cloud management platform, currently uptaken in the cloud market.
A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data

This paper proposes a new generic method to detect anomalies (i.e., statistical outliers) which can be used with a generative topic model. In this paper, we specify this method in the context of the Mixture-of-unigrams model, which is widely used in text mining. It is possible to detect anomalies with a topic model by applying a threshold to the likelihood. However, it is challenging to choose the threshold since the choice needs to consider both the similarities of the topics and the length of documents. This paper describes a new intuitive method to detect anomalies which simply manipulates the output of the trained model. Such an approach is anticipated to have parameters that are more intuitive to define for a given problem. To assess the utility of the proposed approach, we also present a use case involving identifying ships misreporting their ship-type using geo-location data from the Automatic Identification System (AIS) messages. We show that, if we train a model using data for one type of ship, it is possible to identify ships of another type as anomalous.
Type-2 Fuzzy Logic for Dynamic Parameter Adaptation in the Imperialist Competitive Algorithm

In this paper we propose the utilization of type-2 fuzzy systems for the dynamic adjustment of parameters in the imperialist competitive algorithm (ICA), a type-1 fuzzy system was used as a basis, with decades as the input variable and the beta parameter as the output variable, then it was extended to interval type-2 fuzzy systems, and three variants with triangular, Gaussian and trapezoidal membership functions were performed. The imperialist competitive algorithm is based on the concept of imperialism, where the strongest countries absorb the weakest and make then their colonies. To measure the performance of the proposed method 10 mathematical functions with different number of decades are used and finally, a comparison was made between our variants and the results obtained with the type-1 fuzzy system to observe their behavior in the face of optimization problems.
The Use of Virtual Reality in Marketing: Exploring the Need for Technology and Language Adaptation to Create High Quality Immersive Experiences

This paper seeks to provide an overview of the current practitioners and scientific knowledge with regard to the use of Virtual Reality in marketing. A literature review was employed to conduct a systematic analysis of 50 sources to comprehensively capture the state of research and implementation of Virtual Reality. Findings reveal, that the majority of articles focus on either area of application or the effectiveness of Virtual Reality versus other media technologies. This paper critiques the current range of VR application and performance measures by proposing an alternate perspective, provided by narrative theory, as derived from the animation and filmmaking industry.
Aperture

One of the most powerful ways to explore data is to visualize it. Visualizations underpin data wrangling, feature space explorations, and understanding the dynamics of phenomena. Here, we explore interactive visualizations of voluminous, spatiotemporal datasets. Our system, Aperture, makes novel use of data sketches to reconcile I/O overheads, in particular the speed differential across the memory hierarchy, and data volumes. Queries underpin several aspects of our methodology. This includes support for a diversity of queries that are aligned with the construction of visual artifacts, facilitating their effective evaluation over the server (distributed) backend, and generating speculative queries based on a user's exploration trajectory. Aperture includes support for different visual artifacts, animations, and multilinked views via scalable brushing-and-linking. Finally, we also explore issues in effective containerization to support visualization workloads. Our empirical benchmarks profile several aspects of visualization performance and demonstrate the suitability of our methodology.
A Scale‐Aware Parameterization for Estimating Subgrid Variability of Downward Solar Radiation Using High‐Resolution Digital Elevation Model Data

Abstract Subgrid variability of solar downward radiation at the surface can be important in estimating subgrid variability of other radiation‐driven variables, such as snowmelt and soil temperature. However, this information is ignored in current hydrological and weather prediction models as only the mean downward solar radiation of model grid is used. In this study, a parameterization for estimating subgrid variability of downward solar radiation from the model grid mean using high‐resolution digital elevation model (DEM) data is proposed. This scheme considers aspect and slope effects on the subgrid variability. The advantage of this scheme is that computations are performed at the same resolution as the considered hydrological or weather prediction model, and subgrid topographic properties derived from high‐resolution DEM data are used as static inputs. This proposed scheme has been verified in mountainous and flat areas, respectively. It is found that the scheme can well estimate the subgrid variability of downward solar radiation. Also, effects of the DEM resolution on the calculated subgrid variability and the spatial correlation of downward solar radiation are studied. Results show that modeled subgrid variability highly depends on the resolution of the DEM, while the spatial correlation is negligibly time dependent. The proposed scheme can be used in any hydrological and weather prediction model to estimate subgrid variability of downward solar radiation. For example, it is planned to be tested in future NOAA regional and global weather models to account for the effects of the subgrid variability of downward solar radiation on the snow model of the land‐surface component.
SAC-Net: Stroke-Aware Copy Network for Chinese Neural Question Generation

Question Generation aims to create various questions from a given passage, which can provide education material, improve the training of question answering, and help chat bots have cold-to-start or continue to talk to people. Chinese Question Generation is a new research area, which mainly uses a rule-based model to convert declarative sentences into questions. We propose a Stroke-Aware Copy Network (SAC-Net), which is a sequence-to-sequence model. It can enhance the performance of Chinese question generation by using various specific components and techniques for Chinese characteristics. In view of the fact that the morphology of Chinese characters is related to its meaning, this research introduces the information of strokes in Chinese and obtain the relationship among strokes in Chinese characters. This model introduces the direction information of the questions, which helps us generate more diverse questions. For the out-of-vocabulary (OOV) word problem that often occurs in Chinese, this research has improved a Self-attention Copy mechanism. Extensive evaluations confirm that this model improves the performance of Chinese problem generation significantly and represents a state-of-the-art neural Chinese problem generation model.
Development of cross-border education in regional University

The purpose of the article is to substantiate the relevance of the development of cross-border education in regional university as an integral part of the innovative development of these universities and compliance with the trends of global globalization, including in higher education. The author’s position is based on the results of monitoring of the teaching staff of the Don State Technical University (Rostov on Don), which confirmed that the University teachers positively assess the situation in this University, its professional, technical and communication resources for the development of cross-border education, but are not ready to fully integrate into this process due to the lack of qualification and motivated focus of the University on the effective development of international education. The article discusses different points of view on the trends in cross-border education in the Don State Technical University, reveals the reasons for the low penetration of cross-border education in regional universities.
Graphical Authentication Passcode Memorability: Context, Length, and Number

Authentication verifies users’ identities to protect against costly attacks. Graphical authentication schemes utilize pictures as passcodes rather than strings of characters, numbers, and symbols. Pictures are more memorable than alphanumeric passwords. Two studies investigated the memorability of graphical passcodes when users need to remember multiple passcodes or longer passcodes. Longer passcodes provide advantages to security by protecting against brute force attacks, and multiple passcodes need to be investigated as users need to authenticate for numerous accounts. It was found that participants have strong item retention for passcodes of up to eight images and for up to eight accounts. Also, these studies examined whether context can facilitate memorability. The impact of context was not found as assumed in the literature. Overall, memorability was still higher for all the number and length passcode conditions compared with alphanumeric passwords. These studies take steps toward understanding the readiness of graphical schemes as an authentication option.
A Practical Guide to Functional Magnetic Resonance Imaging with Simultaneous Eye Tracking for Cognitive Neuroimaging Research

The simultaneous acquisition of functional magnetic resonance imaging (fMRI) with in-scanner eye tracking promises to combine the advantages of full-brain coverage of brain activity measurements with a fast and unobtrusive capture of eye movement behavior and attentional deployment. Despite its applicability to a wide variety of research questions, ranging from investigations of gaze control and attention guidance to the use of eye movement events as a response modality for gaze-contingent fMRI experiments, only few studies employ this kind of data acquisition. In this chapter we identify technical challenges, describe all necessary components and procedures for conducting such a study, and give practical advice on how these can be integrated in a common MRI laboratory setup. The chapter concludes with notes on the analysis of such datasets and summarizes key data properties and their implications of a joint analysis of fMRI and eye tracking data.
Considerations for the Usability and Implementation of Augmented Reality in Production Environments

Implementation of augmented reality (AR) devices in production floors is becoming increasingly popular for its creative potential in facilitating training and development. From a human factors perspective, there is thus a need to further examine the usability issues and design guidelines of these emerging augmented reality technologies especially with regard to various manufacturing industries. Such assessment efforts will serve to ensure that the fit between human and machine allows for user self-actualization and a safe and productive work environment. Despite the widespread interest in augmented reality, there is no clear set of principles yet established. Many usability models are based on traditional desktop-based computer applications which means unique augmented reality characters are overlooked including the physical ergonomics, expanded field of view, and device mobility features. Here, we review the fit of user-centered usability models to propose a set of criteria to serve as a guideline to implement augmented reality technologies efficiently in the production environment.
MIMO Model-Free Adaptive Control Color Background Image Extraction to Video

The current video background image extraction methods mainly obtain the gray background image, and the gray image processing is very sensitive to the interference noise, which brings great difficulty in detecting and tracking of moving target accurately. In order to overcome the above problems, a novel color background image extraction method is proposed using the idea of model free adaptive control method. The method introduces the pseudo-Jacobian matrix of the system and combines RGB three-channel historical data of pixels to establish the color background image extraction and update. The proposed method that under different video conditions is compared with the traditional gray background image extraction methods. The results show that the method can extract the color background image intuitively, and the separated foreground is closer to the ground truth of the video target.
A Blockchain Smart ContractApplication Framework

Blockchain is an emerging technology which gained popularity with Bitcoin, which is the most famous application of that technology. After Bitcoin became popular, new usage areas of Blockchain was started to being investigating because of the advantage of the technology such as security, agentless transaction, and transparency. HAVELSAN is a large scale software company, investigates on new generation technologies and works on to adapt it. In this manner, Blockchain is one of these new generation technologies that brings impressive advantages. Blockchain is a decentralized transactional database technology in which all transactions are encrypted on blocks, allowing the buyer and seller to make a secure exchange between themselves directly. In this transactional database technology, all transactions are recorded in a structure called “Distributed (Open) Ledger”, where transactions are held and open to all users. In the traditional approach (central database), when the database is controlled by a third party, the copy of the database in the Blockchain approach is available to all participants. This prevents the data from being corrupted and destroyed. In order to be able to modify this distributed structure, the relevant changes must be recorded on all computers in the system. The success of any type of cyber attack becomes impossible because of the necessity of accepting changes in the vast majority of the network. Smart Contract is a key concept of Blockchain in order to gather all these advantages. Smart Contracts not only define the rules and penalties around an agreement in the same way that a traditional contract does, but also automatically enforce those obligations. HAVELSAN is a large scale software company that has wide range of operation field, so there could be different applications of Blockchain with regard to company operation fields. Combining this variety with the importance of Smart Contract concept, which could be counted as the basis for most Blockchain applications, it is beneficial to establish a strong based Smart Contract frame before starting to build our own applications with Blockchain. Therefore, we aimed to build our customized Smart Contract structure before developing miscellaneous applications. In this article, HAVELSAN Blockchain Smart Contract framework establishment; which infrastructures were used, the problems encountered during the development and the most applicable way of use this framework will be explained.
Numerical Modeling of Intelligent Heating Systems to Improve their Energetic Efficiency

Actual context of permanent prices increasing of natural gas or raw materials (coal, wood) imposes a permanent challenging to reduce unnecessary energy consumption in buildings, as a major objective for households, large industrial consumers or SME-s. In the present paper is modeled the behavior of a building, divided into different thermal zones with intermittent occupancy. A separate pre-processing program based on the building structure, material's characteristics, and geographic position is firstly executed. It is presented the mathematical model used in the optimization scheme for the windows, walls, floor, and ceiling and part of the numerical results for the heating systems, ventilation, and air conditioning for a personal building. As the main step for the input data is selected the functioning plan, scheduled based on the owner program, a residential building. The management is finally dedicated to optimize the building energetic consumption. Finally, some conclusions and references are presented.