Reviewerly/exHarmony huggingface dataset:
Using Mixed Reality in Intralogistics - Are we ready yet?

Abstract The Industry 4.0 vision proposes a seamless integration of several modern concepts and technologies, such as the Internet of Things (IoT), artificial intelligence, or robotics, to capture and contextualize data to improve the manufacturing processes. In this respect, Industry 4.0 is all about information and connectivity. Mixed reality (MR) takes the data collected by IoT systems and helps workers by visualizing contextualized data in real time. Human-Computer Interaction research has shown that MR can be advantageous in various application scenarios. However, there is a gap between research and practice when it comes to real world scenarios. So the question arises whether MR is already suitable for efficiently supporting employees in their daily work in industrial settings. To answer this question, we examined two maintenance scenarios in the field of intralogistics: (i) the maintenance of roller conveyors and (ii) the alignments of containers in shuttle warehouses using the Microsoft HoloLens. This paper (i) describes the scenarios and the specific challenges they pose, (ii) presents the prototypes that have been developed, and (iii) discusses the results of user studies that have been conducted to evaluate the practical applicability by presenting inhibiting and facilitating factors. In summary, although MR technology itself seems to be very promising in the scenarios presented, further research on hardware ergonomics and intuitive interaction design is needed.
Building openssl GOST engine for XAMPP web-server

The author describes openssl GOST extension for web-applications designed using Russian unified identification and authentication system (ESIA) with apache web-server including php support for OS Windows.
The Body Sensor Suit with Mixed Reality Interactive Games

With the progress of the technology, the Virtual Reality (VR) that only provides viewing becomes more difficultly to provide users the better VR experience. In order to enhance VR interaction, there are various technologies to improve better VR experiences, such as users could control and move by their hand and feet. However, the most of the VR equipment only operate by controller’s direction and the triggers on the controller button, it leads that users cannot use their own body to have an intuitive control.In order to improve this issue, this study focuses on maximizing the interactivity of VR of long-range moving, thus smartphone is chosen as our development platform. Based on Google ARCore library, the values of accelerometer, magnetism sensor and gyroscope from 9-axis sensor can be got. Furthermore, the Inertial Measurement Unit (IMU) is used to calculate information and motion tracking of body. Therefore, the smartphone could detect the body node on the user’s three-dimensional movement accurately, and feedback the control information into mixed reality interactive games.
On the sensitivity of acoustic distance measures to different parameterizations of mel-frequency cepstral coefficients and temporal alignment algorithms

The increasing role of large speech databases in phonetic and psycholinguistic research has placed a premium on the efficiency and objectivity with which continuous acoustic signals are encoded in discrete parameters that may then be used in recognition models. One simple and common way speech has been encoded and compared in this work is by (1) converting the acoustic signal into a matrix of MFCCs, and (2) measuring the distance between two or more matrices following the application of a temporal alignment algorithm (e.g., dynamic time warping, cross-correlation) to account for differences in utterance rate. The specification of both the MFCCs and the alignment algorithm requires control over a wide array of variables, many of which have both theoretical and experimental implications, and yet most of these variables are set to default values specified in toolkits like the HTK and the Phonological CorpusTools software (Young et al., 2002; Hall et al., 2017). This project uses a large multispeaker database of nonword syllable production and perception (Woods et al., 2010) to test the performance of this acoustic distance procedure over the full parameter space, and evaluate the degree of accordance of such measurements with phonological theory and perceptual data.
SaDe: Learning Models that Provably Satisfy Domain Constraints

In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (for example, safety-critical constraints in autonomous driving systems). Such constraints are often handled by including them in a regularization term, while learning a model. This approach, however, does not guarantee 100% satisfaction of the constraints: it only reduces violations of the constraints on the training set rather than ensuring that the predictions by the model will always adhere to them. In this paper, we present a framework for learning models that provably fulfill the constraints under all circumstances (i.e., also on unseen data). To achieve this, we cast learning as a maximum satisfiability problem, and solve it using a novel SaDe algorithm that combines constraint satisfaction with gradient descent. We compare our method against regularization based baselines on linear models and show that our method is capable of enforcing different types of domain constraints effectively on unseen data, without sacrificing predictive performance.
Analysis The Use Of E-Ddc In Classification Of Library Materials In Environmental Library Of The Lancang Kuning University

This study aims to determine the level of suitability of the classification number of library materials in the Lancang Kuning University Environmental Library based on the use of e-DDC. This research method is descriptive quantitative. The sample in this study is the number of general collections in several libraries at Lancang Kuning University. The results of this study indicate that at the UPT Unilak Library with a sample of 99 collections, the appropriate classification number is 80 collections belonging to very high criteria (81%) while the unsuitable classification number is 19 collections belonging to very low criteria (19%). Furthermore, the Library of the Faculty of Cultural Sciences with a sample of 98 collections, the appropriate classification number is 86 collections belonging to very high criteria (82%) while the unsuitable classification number is 12 collections belonging to very low criteria (12%). Then the Law Faculty Library with a sample of 98 collections, the appropriate classification number is 71 collections belonging to the high criteria (72%), while the unsuitable classification number is 27 collections belonging to the low criteria (28%). Then the Library of the Faculty of Economics with a sample of 97 collections, the appropriate classification number is 75 collections belonging to the high criteria (77%) while the unsuitable classification number is 22 collections belonging to the low criteria (23%). Then the Library of the Faculty of Administration with a sample of 96 collections, the appropriate classification number is 77 collections belonging to very high criteria (82%) while the inappropriate classification number is 17 collections belonging to very low criteria (18%). And the Forestry Faculty Library with a sample of 95 collections, the appropriate classification number is 59 collections belonging to the high criteria (62%) while the unsuitable classification number is 36 collections belonging to the low criteria (38%).
ON NUMERICAL MODELING OF THE INVERSE EPIDEMIOLOGY PROBLEM

The article examines a mathematical compartment model of the spread of the COVID-19 coronavirus. The article consists of six paragraphs. In this study, epidemic outbreaks are studied from an interdisciplinary point of view using an extension of the susceptible-infected-recovered-deceased (SEIRD) model, which is a mathematical chamber model based on the average behavior of the studied population group. Many infectious diseases are characterized by an incubation period between exposure and the appearance of clinical symptoms. Subjects exposed to infection are much more dangerous to the public compared to subjects who have clinical symptoms. A nonlinear system of SEIRD differential equations is considered. For the numerical solution of a direct problem, the fourth-order Runge-Kutta method is considered. In modeling, the relationship is derived based on the laws of the subject area and allows you to determine the nature of changes in the framework of the work, depending on its parameters. The inverse problem is posed to determine the coefficients of this system. The inverse problem was solved by the method of a genetic algorithm. The method of the genetic algorithm is described. Numerical results are obtained and a comparative analysis with accurate data is made.
Faults in Oceanic Crust Contribute to Slow Seismic Waves

New high-sampling rate measurements of fluid pressures in oceanic crust reveal unresolved fractures and pathways for fluid flow.
Attitudes Al-Qadisiyah University students towards e-learning

The research aims to know the attitudes of Al-Qadisiyah University students towards e-learning, and in order to achieve the objectives of the research, the researchers adopted the descriptive approach by informing them of the literature and studies related to the subject of the research, and the research identified the students of Al-Qadisiyah University for the academic year 2020-2021. To achieve the objectives of the research, the researchers prepared a scale of Al-Qadisiyah University students’ attitudes towards e-learning, which consisted of (30) five-paragraph alternatives distributed over three areas. Data processing statistically, if the researchers used the T_TEST test equation For one sample to verify the university students’ attitudes towards e-learning, and the results showed that university students have positive attitudes towards e-learning, there are statistically significant differences between the gender variable (males, females) in favor of males, and based on those results that the current research came out with, it was reached To a set of conclusions, the researchers also recommended some recommendations and suggestions
Proposal on Personal Authentication Method Cooperated with Surrounding Friends

Personal authentication is one of the most important technology to enhance the security of IT system. Although the existing methods such as knowledge authentication like password authentication and biometric authentication utilize the information or characteristics of individual users, we consider new authentication method that focuses on personal sociality. The proposed method is based on the fact that the person to be authenticated must be the right person himself or herself if there are many cooperators such as friends or family around the person. The authentication is achieved by collecting evidences of which there are cooperators near the person from the surrounding smartphones.
Artificial Neural Network for Identification of Infant Feeding Tracking Using the Smart Bottle System

In this work, we present the results of a comparison of simple artificial neural network (FFNN) designs intended to identify infant bottle-feeding events and appropriate feeding volume recording intervals using accelerometer data recorded from a custom designed "Smart Bottle" system. To properly identify and distinguish these events with an accuracy of 99.8%, while accommodating the constraints of the deployment environment, two concurrent FFNNs were implemented.
Technology Leadership In Malaysian Schools- The Way Forward To Education 4.0

Education 4.0 is the answer to the global needs for the advanced integration of humans and technology. Leading school’s technology utilization can be the way forward to support education 4.0 realization. This study aims to investigate the effects and roles of principals’ technology leadership towards teachers’ ICT utilization and students’ academic performance in secondary schools in Selangor, Malaysia. This empirical study uses a set of questionnaires to gather information from respondents who are in the teaching profession. A total of 310 questionnaires were completed and analyzed. The findings have shown significant positive impacts between the effects of the technology leadership roles of principals on teachers’ effective ICT utilization and students’ academic performance. The integration of ICT and technological tools in schools has a great challenge towards the new era of the Education 4.0 system. This suggests that principals who embrace technology will effectively lead their schools to acquire educational resources to enhance student engagement and learning.
Determination of Effective Radio Frequency Monitoring Locations Using Fuzzy-Analytical Hierarchy Process

Determining the optimum location to monitoring the radio frequency spectrum requires a complex consideration. Several considerations including topography, accessibility, population density, residential areas, distribution of radiofrequency spectrum users, high voltage power lines, and Transmitter power level. This study evaluates all consideration parameters using a Fuzzy Analytical Hierarchy Process (F-AHP). The optimum locations which were founded based on the F_AHP analytical are shown on the map using the Web Geographic Information System (WebGIS). The research was conducted in the Bali Province area. The display via WebGIS indicates three categories of suitability, namely: "high", "medium", and "low". Meanwhile, based on the analysis of the effect of consideration for determining the location, it was found that the distribution of radiofrequency spectrum users had the highest weight of 49.1%, then followed by the accessibility of 30.1%, residential area of 9.7%, the topography of 6.5%, and the population 4.6%.
Research on load balancing of parallel component programs based on quantum particle swarm optimization algorithm

Abstract Parallel component applications are often deployed on heterogeneous clusters. Load balancing is very important for their performance requirement. Existing load balancing methods have high performance cost and poor balance effect. Based on the analysis of structures of parallel component applications, we established the mathematical model of load balancing for parallel components on heterogeneous clusters. We use the quantum particle swarm optimization algorithm to search the optimal solution of the proposed mathematical model and determine the best load balancing scheme. Comparing with the methods based on real-time detection and other swarm intelligence optimization algorithms, our method has lower balance cost, less number of iterations and better performance.
On the communicative nature of electronic culture

The Internet, as the global communication channel, is a factor systemically transforming all social reality. Digitalization of almost all spheres of life, communications virtualization and the expansion of network interactions lead to deep changes in the social structure, social institutions, sociocultural mechanisms for the functioning of society and revising ethical and legal norms. Transformation changes of a large-scale sociocultural context generate a new type of culture. The article gives the theoretical and methodological substantiation of the phenomenon of modern electronic culture within the communication approach. It is concluded that the formation of digital world culture is conditioned by all types of communication, allowing for the social and cultural interactions of individuals and social communities.
Calculation of Mahjong Score using AI

In recent years, declining birth rates and aging populations had become more serious in developed countries such as Europe, the United States, and Asian countries, including Japan. Therefore, in this research, we focused on mahjong, which was effective in preventing dementia. However, it was difficult for beginners and even intermediate players to proceed with the game instantly because mahjong has complicated yaku (hand), and the calculation of scores using them is even more complicated. Therefore, we were developing a mahjong progression support system using computer vision aiming at helping players with dementia who have difficulty in calculating mahjong scores. Since simple learning did not produce results that could be used in the proposed system, we achieved significant results by changing the size of the mahjong tiles in the images to be learned, and the system is now able to calculate mahjong scores from the images of mahjong tiles.
SMART Technologies for Student Mobility in the Open Education System and their Application Criteria

The publication presents the criteria for the application of Smart-technologies for student mobility in the system of open education. The novelty of the research is in the fact that the working prototype of the Smart-system for optimal decision-making, where students can independently choose the parameters and resources of the planned educational journey is developed as an integral IT-system with a single interface. The practical significance is in the fact that the system of open education is greatly enhanced by a unique possibility of organizing mobile education with justification of application criteria.
IoT Security and Privacy Concern A systematic Mapping Study

The extend of clever gadgets has accelerated touchy statistics trade on the Internet the usage of most of the time unsecured channels. Since a large use of RFID (Radio-frequency Identification) tags in the transportation and development industries from 1980 to 1990, with the multiplied use of the Internet with 2G/3G or 4G when you consider that 2000, we are witnessing a new generation of related objects. . This paper aims to understand and additionally grant continuing doe’s research topic, challenge, and Future Direction related to Iot security. A systematic mapping finds out about (SMS) is thus utilized on the way to organize the chosen Articles into the following classification: contribution type, Type of Research, Iot Security, and their approach. We take out an overall of twenty-four Articles in support of this systematic discover out about also they categorize the following described criterion. The findings of this SMS are mentioned and the researcher was once given hints on the possible route for future research
Modeling Overview

Any consideration of reaction processes in multicomponent chemical systems begins with a conceptual model of the setting of interest. This chapter describes how to develop a conceptual basis for constructing a geochemical reaction model and discusses the uncertainties inherent in evaluating such a model.
Attention-Based Graph Convolution Networks for Event Detection

Event detection is an important subtask in the field of event extraction. Most of the current state-of-the-art(SOTA) methods introduce rich external information to improve the performance of detection. The graph convolution networks (GCNs) method has been proved to be an effective way to enhance the semantic representation of each word, in which an adjacency matrix is constructed by analyzing the dependencies among the words in the corpus. Syntactic structure and typed dependency label information has shown its positive effects on performance enhancement of event detection. However, few works consider multi-order distance and edge representation update of dependency graph at the same time. Therefore, we design a new way to combine edge representation update method based on attention weight and multi-order distance edge labels to enhance the semantic impact of long range dependency. The individual and composite effects of these two enhancements have been analyzed. Experiments on the ACE2005 dataset show a performance improvement of 0.4% over SOTA GCNs baseline method.
Preface

We introduce a new geometric spanner, δ-Greedy, whose construction is based on a generalization of the known Path-Greedy and Gap-Greedy spanners. The δ-Greedy spanner combines the most desirable properties of geometric spanners both in theory and in practice. More specifically, it has the same theoretical and practical properties as the Path-Greedy spanner: a natural definition, small degree, linear number of edges, low weight, and strong (1+ε)-spanner, for every ε>0. The δ-Greedy algorithm is an improvement over the Path-Greedy algorithm with respect to the number of shortest path queries and hence with respect to its construction time. We show how to construct such a spanner for a set of n points in the plane in O(n2log⁡n) time.The δ-Greedy spanner has an additional parameter δ, which indicates how close it is to the Path-Greedy spanner on the account of the number of shortest path queries. For δ=t, the output spanner is identical to the Path-Greedy spanner, while the number of shortest path queries is, in practice, linear.Finally, we show that, for a set of n points placed independently at random in a unit square, the expected construction time of the δ-Greedy algorithm is O(nlog⁡n). Our analysis indicates that the δ-Greedy spanner gives the best results among the known spanners of expected O(nlog⁡n) time for random point sets. Moreover, analysis implies that by setting δ=t, the δ-Greedy algorithm provides a spanner identical to the Path-Greedy spanner in expected O(nlog⁡n) time.
Unsupervised Learning for 2D Image Texture Enhancement

One disadvantage of supervised learning is the capability of the neural network to only recognize objects labeled during the training phase. In order to identify objects outside of the training dataset, unsupervised learning is needed. In this paper, autoencoder is used as the unsupervised neural network and it also functions as a 2D image texture enhancer. Autoencoder is capable of clustering and dimensional reduction, where reducing dimensions of images can be categorized as feature selection and feature extraction. The role of autoencoder as an unsupervised network is to train different High-Resolution (HR) images and preserve texture features that can be used later to enhance Low-Resolution (LR) input images and process them into an enhanced 2D image (E2D). Low-resolution images due to degradation or blurring can be enhanced with the proposed algorithm under 2 parts of autoencoder: encoder and decoder. The encoder stage downsizes the HR dataset while maintaining the extracted features during the training phase. The decoder stage makes use of the extracted feature and denoises the LR input image to produce an E2D image. 100 LR images were tested with a 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> success rate of E2D enhancement.
Improved Collaborative Filtering Recommendation Algorithm Adapted to User Interest Change

Collaborative filtering algorithm is the most widely used algorithm in the recommendation system. Its core is to recommend the target according to the neighbor users or similar items. When the traditional collaborative filtering algorithm is faced with sparse data, the computational similarity accuracy is low, which indirectly leads to the quality decline of the recommendation system. To alleviate the influence of data sparsity on similarity calculation and improve recommendation quality, an improved collaborative filtering recommendation algorithm adapted to the change of user interest is proposed. Firstly, considering the user preference similarity, the traditional cosine similarity is combined with the penalty factor of popular items, while the correction factor for the user rating score difference degree and the user rating tendency similarity are introduced; then, for the user interest decay problem, the Ebbinghaus forgetting function is incorporated into the Pearson correlation coefficient and modified using the common scoring weighting function. Finally, the correlations between user preferences and user interest changes are obtained by weighted fusion, to calculate more accurate user similarity. The experimental results show that the algorithm of this paper effectively improves the prediction accuracy of the recommendation system.