Reviewerly/exHarmony huggingface dataset:
Exploring 3D Interactions for Number Entry and Menu Selection in Virtual Reality Environment

Graphical user interfaces (GUI) on mobiles involve user interaction of touch input on a two-dimensional surface. With advances in virtual reality (VR), possibilities of three-dimensional 3D GUIs will emerge. Current study on designing interfaces for VR deals with incorporating rules of 2D GUIs. The question here is that whether such an approach is optimal for a VR environment? Designing interactions and interfaces for VR deals with issues that are different from flat 2D screens like field of view, orientation of the user, having no defined reference plane and depth-related issues. In order to solve such ambiguities in the design of VR interfaces, an experiment was conducted with 30 users comparing three different keypad interfaces in the virtual world via head-gaze interaction. Subsequently, inferences drawn from this study were used to design and prototype two more interfaces where hand gesture (HG) and natural object interaction (NOI) based techniques were explored using a single layout of dial pads and menu items. A research experiment comparing the two interaction techniques for an online money transaction scenario was designed and conducted with 20 different users. The paper discusses different interaction techniques and compares 2D dial pad interface with a 3D interface. The paper also discusses insights on the task performance, the usability of such interfaces and cognitive workload while using them.
Research on stitching metrology of large aperture continuous spiral phase plate

The fully continuous spiral phase plate (SPP) can produce high quality vortex beams, which has important application value in the field of high-power lasers. For the stitching test of large aperture SPP with large gradient characteristics, the error factors that affect the accuracy of stitching are analyzed, and the corresponding error correction methods are proposed. On the basis, the stitching test of 235 mm&times;175 mm aperture SPP is achieved. Multiple test results indicate that the RMS repeatability of stitching is up to 0.0016 &lambda; and the RMS accuracy can reach 4.52 nm.
Competency Profile of Enterprise Mentor as a Basis for Corporate "Buddy Systems"

The research features professional and pedagogical competencies of a mentor in the field of talent management with the aim of securing young specialists at enterprises. The authors analyze the most relevant typologies of mentors' competences according to modern challenges and requirements. They introduce an authentic typology of competency profile of the mentor at an enterprise. It includes four blocks: 1) the organization and planning of training in the workplace in order to improve the level of qualifications of the instructors; 2) the guidance and assessment of the work of the trainee; 3) rationing and assessment of the instructed; 4) raising one's own psychological and pedagogical competence as a mentor. The paper contains a methodology for self-diagnosis of the development of the psycho-pedagogical competences of the mentor at the enterprise. The results of the study are recommended for review and implementation in industrial plants. The present research was carried out within the framework of a comprehensive scientific and methodological development for the Temporary Research Team "Golden Standard for Child Development 2.0" of the Kazan Open University of Talents 2.0 on the topic "Formation of professional and pedagogical competences of mentors of enterprises in the field of talent management with the aim of securing young professionals at the enterprises of the Republic of Tatarstan".
Adaptive Hybrid Blood Cell Image Segmentation

Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions.
State Actor Model for Cloud-Based Online Auction

In recent decades, internet auctions have become the most significant e-commerce business model worldwide. With the rapid rise of cloud computing over the last few years, the legacy online auction platform is gradually being replaced using service-oriented cloud computing in real time. This chapter describes the design and implementation of a state and high-performance online auction system over cloud and proposes the methodology to provide persistent state records during the auction process so that we are able to ensure the reliability of submitted bid price and guarantee the security of price message in the delivery process. The authors employ actor-based applications to achieve stateful, parallel, and distributed architecture. Meanwhile, utilizing distributed databases provides secure and efficient data storage. To the best of the authors' knowledge, this is the first time that the actor framework has been applied to the online auction. The preliminary result is for implementation of high-performance and real-time bidding online auction.
Thresholding Techniques for Dental Radiographic Images

Imaging techniques play a major role in improving the early detection and diagnostic process that helps dentists to make accurate diagnosis. One of the most useful medical images used by dentists is radiographic image, which is used for the treatment of various dental disorders. Segmentation is a fundamental step as it involves separation of an image into regions corresponding to the objects. A simple and natural way to segment such regions is through thresholding. In this chapter, various thresholding techniques such as Otsu's method for global thresholding and Niblack's, Bersen's, and Sauvola's techniques for local thresholding are extensively explained with the help of dental radiographic images.
Vegetable Technology Information Visual Service System Based on Knowledge Map

To the demand that the vegetable technology information visualization and comprehensive push of related information, through the vegetable instance and attribution recognition, and vegetable technology knowledge map constitution, the vegetable technology information visual service system is developed. The related varieties, cultivation technology, prevention and control of diseases, frequent question and answer the specific vegetable species have retrieved to user systematically and orderly. It helps to vegetable technology information discovery effectively.
Discrete event chain description of power system transient dynamic simulations for efficient cluster analysis

The results of an electromechanical transient simulation can be viewed as a time series data set. Storing these data requires a sizeable space, and the considerable data size renders comparative analysis difficult. For mitigating these problems, a method for analysing and describing a discrete-event chain in the dynamic process of a power system is proposed. Based on the symbolic description, the continuous dynamic process is represented as a discrete-event chain, resulting from the change in state variables beyond the given threshold. Therefore, the event-driven electromechanical transient simulation is developed, and the continuous dynamics and discrete-event chain simulation results are obtained. Subsequently, the event relation matrix is obtained, normalised, and then converted into a bitmap. Finally, the k-means method is used for the cluster analysis on the bitmap of the simulation result. Two test cases involving an IEEE 39-bus system and a practical system are considered. Case studies demonstrate the effectiveness of the proposed method.
Structured Methods of Representation of the Knowledge

This paper describes same of the structured methods of representation of the knowledge that we introduced as an alternative to the procedures of representation more formal. After a brief mention to the essential characteristics that we have to take into account to any structured method of representation of the knowledge, classified into declarative methods and procedural methods. Declarative methods give more importance to the facts and entities of the domain that to the mechanisms of manipulation of the same. By contrary, the procedural methods, although they operate on facts and entities of the domain of speech, gives greater attention to the mechanisms of relation between entities. The declarative methods studied in this paper are the semantic network in which the knowledge is represented like a collection of joined nodes among them by means of labeled arches, the frames that can be defined as complex semantic network that treat the problem of the representation from the optics of the reasoning for likeness. Moreover, we described the formalism of the production rules, putting special attention in their structure and their form of cooperating with some declarative of representation (for example frames).
Sentiment Correlation Discovery From Social Media to Share Market

Social media data analytics have been successfully applied in many real applications such as product recommendation, target advertisement. In recent years, it also attracted lots of attention from the financial researchers to analyse the financial trending or stock marketing prediction. In this paper, our goal is to investigate the meaningful way of uncovering the correlation between the stock share price change and the social media data usage. In this work, we first provide a mechanism to collect Twitter data, use Latent Dirichlet Allocation for topic modelling, then perform the sentiment analysis based on topics, and finally discover the correlation between social media and share price. Based on our empirical results, we find that the correlation could be impacted by the popularity of discussion as well as the valence of community, which represents the happiness to the target companies to be analysed and predicted. This could be built up by exploring the market and crisis resolution. The influence of online social users also plays a significant role in the correlation, which is a factor of manipulation that the influential users should be considered by measuring the responsibility of their social media account.
Keystroke Dynamics in E-Learning

Enhanced authentication is the need of the hour in today's technology. Commonly used login and password are not enough as they may be guessed by imposters. Most of the websites adopt the traditional authentication as login and password. But they don't verify whether the same person is accessing their information continuously in the current session. This is of great concern in distance-based e-learning systems. The institutes offering the e-courses must verify whether it is the same student who enrolled, is accessing their materials, doing the assignments themselves, and completing the examination without any cheating. In this case, one of the techniques, behavioral biometrics-keystroke dynamics, plays a very important role. Along with other authentication methods, keystroke dynamics can be combined to provide a more secured system for the students in e-learning environments. In this chapter, the basics of keystroke dynamics and some of the applications that use them are discussed.
A Combination of 3D Faster R-CNN and Laser Scan for a Robust Distance Measurement

Measuring the distance of a specific object accurately is a challenging task. Recently, in mobile robot community, there are several works combining an image-based object detection method and the distance measurement using laser scanner to achieve such challenge. However, the performance of existing methods tend to degrade due to the influence of occlusion and walls behind the target object. To tackle the vulnerability in the existing methods, in this paper, we propose Belief Weighted Max Cluster Average Detection (BWMCAD) method which utilizes the result of 3D Faster R-CNN as the belief of laser scan data and clusters the belief weighted data to extract the distance to the target object. We demonstrate its effectiveness through distance measurement tasks using Tsukuba challenge 2017 data and in-house data in comparison with existing methods.
Brains Are Not Computers: Celebrating the Achievements of Robert Kozma

In this article, we provide an overview of Dr. Robert Kozma's accomplishments, based on essays by leading experts in various areas related to his research, covering mathematical, computational, and cognitive aspects of his neurodynamics modeling and applications in biologically inspired autonomous systems and robotics. Specific contributions are given by Dr. Miklós Ruszinkó (the Rényi Institute of Mathematics, Hungarian Academy of Sciences) on mathematical and graph theoretical aspects of brain modeling, detailing Dr. Kozma's work, which is now known as neuropercolation. This is followed by recollections from Dr. Roman Ilin [U.S. Air Force Research Laboratory, Wright Patterson Air Force Base (WPAFB)], a former student of Dr. Kozma's, on computational models of spatiotemporal neurodynamics, and the work Dr. Kozma has made jointly with the late, world-renowned Prof. Walter J. Freeman [University of California (UC), Berkeley] and his students in a field now called Freeman K Katchalsky-sets. Dr. Edward Tunstel [the United Technologies Robotics Center (UTRC), East Hartford, Connecticut] provides an overview of biologically inspired robotics and several unique applications Dr. Kozma has made in this field together with colleagues from NASA Robotics. Dr. Bernard Baars (Society of Brain-Mind Sciences, La Jolla, California) contributes a cognitive scientist?s perspective on Dr. Kozma's work with an emphasis on implications of understanding and modeling consciousness. We believe this multiperspective overview provides an insight into the truly interdisciplinary character of Dr. Kozma's remarkable research achievements.
Secure Data Integrity Protocol for Fog Computing Environment

Fog computing is a distributed infrastructure in which certain application processes or services are managed at the edge of the network by a smart device. Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the fog platform highly suitable for time and location-sensitive applications. For example, internet of things (IoT) devices are required to quickly process a large amount of data. The significance of enterprise data and increased access rates from low-resource terminal devices demand reliable and low-cost authentication protocols. Lots of researchers have proposed authentication protocols with varied efficiencies. As a part of this chapter, the authors propose a secure authentication protocol that is strongly secure and best suited for the fog computing environment.
Teaching methods for microbiology in Saudi Arabia

Microbiology as an academic discipline that does not have a long history, but the knowledge accumulated over the past three centuries is rather considerable, and courses in microbiology are commonly taught in colleges and universities throughout the world. In this article, many of the methods that can be used for teaching microbiology in a classroom and laboratory are discussed. In brief, these methods are limited only by the basic technical facilities to which one has access as well as one's own imagination and creativity. The problems, the academic teaching is facing in Saudi Arabia are discussed; most important of them are the use of new technology and avoiding plagiarism. © 2019 International Union of Biochemistry and Molecular Biology, 47(2): 175-178, 2019.
Securing the new wave of non-human users

It is no secret that the advent of the digital age has spurred significant progress in businesses – particularly with the rising deployment of bots and artificial intelligence (AI) to boost efficiency in the workplace. But, as with most opportunities, there are challenges. And, unfortunately for businesses, fraudsters are not lagging behind when it comes to adopting these technological innovations to support their criminal efforts. As companies increasingly embed artificially intelligent ‘workers’ to help support operations, they are unwittingly exposing themselves to further risk if not properly secured, creating additional pathways for fraudsters to infiltrate.
Cumulative frequency distributions of daily clearness index for temperate and high latitudes

Cumulative distributions of daily clearness index, which is based on actinometrical observations from meteorological stations and NASA POWER data, have been constructed for the Russian Federation territory, which spans a wide range of latitudes and covers more than half of the high-latitude Northern Hemisphere. Possible applications of NASA POWER data for the simulation of solar power systems at temperate and high latitude areas have been shown. Daily clearness index distributions were approximated by equations derived from data in tropical climate conditions, with modified dimensionless variables. These equations were found to be adequate for high latitudes, which provides greater confidence that they are universal. It has also been shown here that the selection of the extra-terrestrial radiation calculation method has an influence on the calculated clearness index value, especially for high latitudes. The difference between Cooper's equation for declination and calculations conducted with NASA POWER data at high latitudes is more than 35%.
Using an Input Data Segregation Algorithm to Minimise the Error of the Fuzzy Controller in the Metrological Correction System of Electric Energy Meters

The authors of this paper presented the possibility of using a fuzzy controller in the conversion factor correction system associated with the energy meter’s current channel. The accuracy of non-adaptive fuzzy controllers is significantly affected by the relevant expert knowledge in the form of rules stored in the database. In order to increase fuzzy controller accuracy, the k-means clustering method was used to group the input data of the controller (peak value of the output signal of the energy meter’s current transducer and its derivative). This analysis can be conducted to extract central points that represent particular input data groups. Based on computer testing of fuzzy controller output signals performed by the authors, the assignment of membership functions to the central points of the input data groups should be done by the expert at the beginning while designing the rules. Additionally, this paper presents the possibilities of tuning the fuzzy controller by changing its parameters.
Exploration and impact of blockchain-enabled adaptive non-binary trust models

Distributed identity-, attribute- and reputation- management constitutes a major benefit that a properly designed permissioned blockchain system can provide. As a complementary built-in feature set, finely-granulated and time-window-constrained auditability mechanisms aid blockchain performance and scalability by eliminating the need to front-load core transaction processing with onerous communications and computational complexity, while still meeting the requirements of effective governance, risk & compliance management and containment against compromised entities. A prevalent aspect of this methodology is the capability to determine not only what entities should be considered trustworthy, but to what extent and with which relevant functionalities, where completion of tasks entails distributing communications across multiple components as means of addressing corroboration of claimed suitability in order to choose the most trustworthy available solution components matched against specific sub-task requirements.
Learning from Low Training Data using Classifiers with Derivative Constraints

Availability of low training data presents a challenge in several learning scenarios, primarily since generalization is dependent on training using a large number of samples. However, there are several practical scenarios when limited data is available for training a classifier. In this paper, we present an approach for learning with few data samples, involving additional constraints based on computing derivatives of the decision boundary at the location of the training samples. Based on the kernel Support Vector Machine (SVM) formulation in Empirical Feature Space (EFS), we add an additional constraint which stipulates that the derivative of the separating hyperplane corresponds to either a maxima or a minima at the locations of the training samples. This is done by setting the derivative of the conventional SVM constraint to zero. We call our approach the SVM-Derivative (SVM-D). We also present a variant of this approach based on the Minimal Complexity Machine (MCM), called the MCM-Derivative (MCM-D), which allows us to realize sparser models in terms of fewer number of support vectors. We illustrate that the proposed approaches perform much better than conventional SVMs when the training is done with very few samples (20% of the dataset samples are used for training) for various datasets from the UCI Machine Learning repository.
User Interfaces for Safety Inspections in Underground Mining

Technology has made inroads in various domains of the economic industry and the Mining Industry is one such realm which has undergone a substantial transformation in a bid to attuning to the demands of modernity. Digitization of Mines is a relatively new aspect of mining, wherein the use of technology holds the potential power to revolutionize the mining experience. This has led to the emergence of new vocabularies that is in juxtaposition with the old while creating challenges that were unforeseen before the foray of technology. This, however, presents a new perspective towards the creation of user-centred designs that would require design specifications unique to the users, i.e. the mining operators. The extremely hazardous work environments of underground mines make it imperative that safety comes first. Thus safety and compliance go hand in hand and are of utmost importance. The task of a mining operator starts with an inspection of their mining equipment underground as per safety regulations of the mine. The task is carried out in the dark underground environment via a paper-based checklist. This however leaves room for error while making the task laborious and tedious. With digitization, this process has undergone a tremendous change by enhancing the task of inspection without complying safety. There are however no frameworks for interface design specific to underground dark environments that these mining operators work in. This created an opportunity to come up with guidelines for designing UI for underground mining equipment checklists. Through extensive focus group studies, user interviews and user journey mapping, design guidelines were formulated which focused on UI elements, colour contrast and eye adaptation for such settings. These guidelines were further tested and evaluated with the user groups. This paper will discuss the findings and implications of this research.