Reviewerly/exHarmony huggingface dataset:
Development of Standard Operating Procedure (SOP) Training Model Using Disaster Safety Communication Network Based on Public Safety Long Term Evolution (PS-LTE)

This study provides information for developing the domestic standard operating procedure (SOP) to CMMI level 3 or higher by presenting the SOP education and training model development process that systematically utilize the PS-LTE-based disaster safety communication network. The survey was conducted with 113 domestic SOP experts. &amp;#x00052;esults revealed that four strategies can minimize the damage to people's lives and property in a national disaster and develop the domestic SOP level to CMMI level 3 or higher-establishment of governance for the SOPs for disaster safety communication networks; training on SOP once a year; establishment of SOP according to the guidelines; and improvement in the technical field. In the future, if SOP develops to CMMI level 3 or higher, it will contribute to the protection of public safety and property from disasters.
Hypergraph based Multi-Agents Representation Learning for Similarity Analysis

As a lot of agents with diverse sensing capabilities are expected to collaborate in the near future, the needs to process a huge number of multi-modal data are emerging to recognize global situations, events or environment. In this paper, we propose a hypergraph based multi-agents representation learning (HMARL) to obtain agent embedding vectors, which can be used to classify agents in the same region and correlate the collected data of similar properties. To this end, the proposed HMARL transforms the multi-modal data into the same graph structure with nodes and their relations. Then, a hypergraph is constructed to integrate local graphs and a hypergraph random walk is applied to obtain the sequence of adjacent agents, which is used to train the agent embedding vectors. Experiments on public datasets are provided for similarity analysis on agents and their collected data.
Afterword: Shifting the Terms of the Debate

The afterword discusses how this special issue’s articles work from different angles to unsettle the precepts of “attentional sovereignty” — the socially, politically, and economically valorized virtue that anchors most discussions over attention in its contemporary technological predicament. Whether the attentional sovereign appears in its liberal humanist or its neoliberal behavioral economic guise, sovereignty is valorized and considered under threat. By revealing the contemporary and historical backstories to our investment in this notion, these articles shift the terms of the debate around the attention crisis and clear space for thinking anew about the possibilities and limits of attention today.
PROFESSIONAL SOCIALIZATION OF STUDENTS IN THE STUDY OF HUMANITIES AT A TECHNICAL UNIVERSITY

This article substantiates the importance of professional socialization of students of a technical university. It is the student age, due to its age-related mental and personal characteristics, that all types of socialization occur intensively. Consequently, students are considered as the most favorable period for professional socialization. In addition, the article analyzes the scientific sources of literature in terms of the definition of the concept of "professional socialization". A huge role in the training of qualified personnel is played by higher education, which lays the basic foundation for the personal and professional development of the younger generation, in whose hands is the future of society and the country as a whole. It should be noted that thanks to the study of humanities at a technical university, it is possible to implement such methods of working with students that will ensure successful professional socialization of junior students at the training stage, form personal qualities and increase the level of professional training. Using the example of specific humanities disciplines, such as "Communication in professional activity", "Business communication" and "Psychology of professional activity", the article presents work with junior students, which forms the social formation of a student's personality and is part of the process of professional socialization.
Design and implementation of an Embodied face recognition and tracking algorithm

This paper mainly designs an algorithm for face recognition and tracking and implements the algorithm using ROS robots. Given the high hardware requirements of the actual ROS robot in the target tracking process, the accuracy is not accurate, and the recognition and tracking speed is slow. This article tries to use a combination of Haar-like, key point tracking, and optical flow tracking to improve the above problems. Secondly, this article uses camshaft color tracking to address poor image transmission quality under the ROS framework, the large difference in light difference, inaccurate depth information, and other problems caused by the loss of recognition and tracking targets solve. Finally, the algorithm is implemented on the robot, and the accuracy and advancement of the embodied face recognition and tracking algorithm in this article are verified through experiments.
Research And Analysis of Nonlinear Identification Adaptive Control of Fitness Using MATLAB Numerical Simulation

Abstract Aiming at the problems of non-linearity in physical exercise, difficulty in predictive model identification control, and difficulty in exercise in control target. A nonlinear identification adaptive control algorithm based on physical exercise is proposed, and a nonlinear identification adaptive control system based on physical exercise is established and simulated with MATLAB. Simulation results show that the recognition ability of physical exercise is strong, which improves the requirements of physical exercise quality, and has the advantages of simple control algorithm, high control accuracy and strong anti-interference ability.
Research on building operation and maintenance management based on BIM and IoT technology

In view of the current situation of poor and inefficient cross-platform information interaction in the three-dimensional building model under the C/S architecture, in order to intuitively and efficiently manage the static data and dynamic data generated in the building operation and maintenance phase, the management method based on BIM and IoT technology is studied. Through the IFC file conversion plug-in, the BIM model data is converted into a file format suitable for Web technology, and the Three.js framework is used to realize the display of the lightweight building model based on the B/S architecture; the data is collected by the IoT devices based on the Zigbee protocol. Integrating the building dynamic data with the BIM model data to realize data management applications in the building operation and maintenance phase. The practical results verify the practicability of this method.
Architecture of a time‐sensitive provisioning system for cloud‐native software

Abstract Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud‐native business models that leverage containerized multi‐tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time‐sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of the Reserved Instance Allocation Problem (RIAP), which we first prove to be NP‐hard. We then design a heuristic‐based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic‐based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit‐to‐investment ratio for a broker who seeks to operate a commerce of virtual machines with time‐sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost‐sensitive fulfillment of bare metal provisioning requests in a time‐sensitive manner.
Enabling Exploratory Large Scale Graph Analytics through Arkouda

Exploratory graph analytics helps maximize the informational value from a graph. However, increasing graph sizes makes it impossible for existing popular exploratory data analysis tools to handle dozens of terabytes or even larger data sets in the memory of a common laptop/personal computer. Arkouda is a framework under early development that brings together the productivity of Python at the user-side with the high performance of Chapel at the server-side. In this paper, we present our initial work on overcoming the memory limit and high-performance computing coding roadblocks for high-level Python users to perform large graph analyses. Based on a simple and succinct graph data structure, a high-level Chapel-based graph algorithm, Breadth-First Search (BFS), is presented to show the scalable and parallel graph algorithm development method in a productive way through Arkouda. The reverse Cuthill–McKee (RCM) algorithm is implemented in Chapel to relabel the vertices of a graph as a preprocessing step to improve the performance of BFS and one low-level BFS algorithm is also developed to compare with the performance of high-level method. Both synthetic graphs and typical graph benchmarks are used to evaluate the performance of the provided graph algorithms. The experimental results show that, based on the proposed high-level algorithm framework, the performance of BFS can be improved significantly and easily by simply selecting suitable Chapel high-level data structures and parallel constructs. Our code is open source and available from GitHub (https://github.com/Bader-Research/arkouda).
Deep Convolutional Neural Network Diabetic Entity Relationship Extraction Model Based on Enhanced Semantic Representation

Diabetes, as the number one chronic disease in China, plagues the lives of people of different age groups and causes great distress to people. This paper extracts the semantic relationships among diabetes entities through the entity relationship extraction technique, which helps to build the knowledge graph of diabetes domain. The experimental results show that the deep convolutional neural network (ESPDCNN) based on enhanced semantic representation proposed in this paper effectively improves the accuracy of the diabetic entity relationship extraction model.
Enhanced Conditions Based Deep Convolutional Generative Adversarial Networks

Generative adversarial network (GAN) is a prevalent generative model. While it is effective, it has been shown to be very hard to train in practice. This work demonstrates how an improvement to the GAN framework can be used in a stable training, and in a conditional manner able to restrict their generation according to some alternate information such as a class label. Additionally, we explore different GAN structures, showing stable training method between images and attributes.
Interactive Explainable Case-Based Reasoning for Behavior Modelling in Videogames

Creation of believable characters is one of the most challenging problems in the video game industry. Although there are different authoring tools available for designers and programmers to create the behavior of non-player characters, it remains a complex and error prone process that requires a high level of technical knowledge. Considering how powerful Learning from Demonstration is for building intelligent agents that are able to replicate human player behaviors, we are currently using an online case-based reasoning agent that learns how to imitate real players of Ms. Pac-Man using an interactive approach in which both the human player and the computational agent take turns controlling the character. In this paper, we describe the new explanation subsystem and how it helps game designers to understand the agent’s learning process. We also present an evaluation of the system performed by five professional video game designers.
International science and education classification: The place of library science

Through the Order of the RF Ministry of Science and Higher Education of February 24, 2021, on the Nomenclature of Scientific Professions to be awarded academic degrees, the speciality 05.25.03 «Library science, bibliography, and bibliology» was excluded from this nomenclature. The concerns arouse that this speciality might be excluded from the educational programs. The post-graduate programs is to become the third level of higher education. One more attempt should be made to stand for our profession by referring to global precedents, i. e. to the International Standard Classification of Education (ISCE), adopted by the UNESCOGeneral Assembly in 2011, and to FOS (Revised field of science and technology, classification in the Frascati manual). The latter is the international classification of science and technology adopted and approved by 75% world countries to represent scientific disciplines and to unify world statistical data. In both classifications, library science is specified as an independent discipline within the social sciences unit.
A Multi-Perspective Distributed Mining Framework for Scalable Search Spell Correction

Acquiring high-quality misspelling data at large scale to train quality search speller models is key but challenging. Synthetic data generation approaches as a major focus in literature are usually linguistics dependent, challenging in domain adaptation, and centered around empirical choices based on error patterns generalized from limited annotated datasets. Mining based approaches on the other hand are not sufficiently studied and don’t ensure ground-truth corrections. Both methodologies also lack focus on other strategical considerations which matter for the final quality of the data and model. We introduce a novel, comprehensive and production-proved distributed mining framework which is able to generate large-scale quality data to train search speller models. The enabling method eliminates dependency on human judged data, and fully scales exploring, training and deploying high-quality speller models with maximal efficiency. The work has been demonstrated by production launches of spell correction to worldwide markets for Apple Maps search. Our approach should also facilitate the general synthetic data generation approaches in applicable domains to get rid of the human annotation dependency.
Comparison of NNs-ARIMAX and NNs-GSTARIMAX on Rice Price Forecasting in Indonesia

Rice supply contributes the most to the food poverty line in Indonesia since rice is the main food for Indonesian. Therefore, the ups and downs of rice price has great impact to Indonesian. In fact, the national average price of rice in 2020 had fluctuated. This fluctuation was influenced by various factors: the previous period price, the price of grain in the mill, rice stocks, harvested area, rice production, rice consumption, weather, and rice prices in neighboring areas. Therefore, forecasting rice price is carried out in this article. Based on these problems, this study offers a solution to compare methods to predict rice prices in Indonesia. The method is Hybrid NNs-ARIMAX with Hybrid NNs-GSTARIMAX. The selected provinces are West Java, Central Java, East Java, DKI Jakarta, DI Yogyakarta, and Banten. The input variable is historical data on the average price of rice in the period January 1,2008, to December 31,2019 (weekly). The output of this article is the forecast of average rice price and its accuracy performance. The best NNs-ARIMAX model for Banten province is ARIMAX (4,0,5), DKI Jakarta province is ARIMAX (4,1,5), and ARIMAX (1,1,1) for West Java, Central Java, DI Yogyakarta, and ARIMAX (3,0,2) for East Java. The best NNs-GSTARIMAX model is GSTARIMAX (1,1,0)1. This most accurate training-testing is 85:15. There was a 0.17% decrease in MAPE of ANN compared to Hybrid NNs-ARIMAX. Also, 1.09% decrease in MAPE of ANN compared to NNs-GSTARIMAX. This shows that the accuracy of NNs-GSTARIMAX is better than NNs-ARIMAX. So, it can be concluded that the NNs-GSTARIMAX method is better than the NNs-ARIMAX method for predicting rice prices in Indonesia.
Scene Recognition and Narration from Video using Deep Learning Techniques

The world becomes just a little harder if one of our senses are impaired and if that impaired sense is sight, every facet of our life becomes more challenging? Not every blind person can have a guide. This thought prompted us to work on a project called scene recognition and narration in which the application will recognize and understand the scene presented and dynamically give a verbal output. This application will help in the reduction of accidents and help make the lives of blind people just a little easier. The findings appear to provide good evidence that using widely accessible dense building blocks to approximate the predicted ideal sparse structure is a feasible technique for enhancing neural networks for computer vision. There are two more advantages to this architecture: One is that the number of units in each step is greatly increased, yet the computational complexity is kept under control. A significant number of input filters can be shielded from the final step to the next layer when dimensionality reduction is used widely. Second, visual data is processed on several scales and then aggregated so that the following phase may extract characteristics from many scales at the same time. YOLO Algorithm is used for the proposed architecture.
Comparison of Algorithms of Associative Rules Search Apriori and Fp-Growth for Investigation of Time Dependence of Their Execution on Database Parameters

The research is devoted to a comprehensive study and comparison of algorithms FP-growth and APriori. These algorithms are used in modern areas such as: contextual advertising, creation of recommendations, big data extraction, etc. That is why the study of the feasibility of using specific search algorithms for frequent sets and their comparison is an important task. In the research the substantiation of expediency of using one of the above algorithms depending on the size and other parameters of the database, which is being investigated is created. These and other results obtained in this scientific work can be used to facilitate the task of choosing the algorithm for searching for associative rules after determining the database under study.
Multiplicative knapsack injectivity as condition of effective unauthorized access protection

Abstract The mathematical modelling of data protection systems based on multiplicative knapsack is considered. In particular, development of cryptographic solution to hierarchic access control problem is represented. Previously in research the authors has formulated the requirements to knapsack in order to apply these to a hierarchic access control problem based on the requirements for similar models. Here in the article the possibility of practical implementation of the construction of such knapsacks is pointed. Based on special mathematical model for multiplicative knapsacks generalized sufficient condition of multiplicative knapsack injectivity is formulated and proved in the article. Known sufficient conditions of multiplicative knapsack injectivity are special cases of authors condition. The mathematical apparatus of additive knapsacks for building multiplicative vector is shown.
Detection of Difference in 2 Dimensional Animation Shot Using Structural Similarity Index and Otsu Thresholding

An animator plays important role in the process of making 2D animation films. During the process of making 2D animation characters that have been created by animators, errors are sometimes encountered because the making of 2D animation by drawing frame-by-frame makes the movements seem less natural. Therefore, revisions are necessary for the next steps. SSIM is usually used to detect images, while in this study SSIM is implemented in the stages of the production process of 2-dimensional animated films as quality control in detecting differences between 2 shots of animation movement before and after revisions that are difficult to find by eyes. The results of this study in detecting differences of 2-dimensional animation shot using the structural similarity index (SSIM) algorithm are values of the structural similarity index which are then computed to obtain the locations of the differences. Next, the contours of the differences are searched to get the visual difference using otsu thresholding. Based on the results of this study, it is concluded that the structural similarity index measure algorithm can be used as a method to detect differences in 2-dimensional animation shots before and after revision with good accuracy. The speed of the detection algorithm from detecting animation differences to producing visual contours differences of 593 compared frames is on average 9.08 seconds per frame.
Musical Hyperrealism: Exploring Noah Creshevsky’s compositions through Jean Baudrillard’s ideas

While a familiar term in art history, philosophy and cultural studies, ‘hyperrealism’ is rarely applied to music. This is despite Noah Creshevsky’s use of the term to describe his unique compositional process and aesthetic approach. A composer of electroacoustic music and founder of the Brooklyn College Center for Computer Music, Creshevsky has described his musical hyperrealism as a ‘language constructed from sounds that are found in our shared environment (“realism”), handled in ways that are somehow exaggerated or excessive (“hyper”)’. In this article, I summarise the ideas behind Creshevsky’s hyperreal music and compare them to philosopher Jean Baudrillard’s theorisation of the hyperreal. Numerous similarities between Creshevsky and Baudrillard’s ideas will be made evident. The first half of this article focuses on Creshevsky’s sampling of sounds as ‘simulacra’ and how the interweaving textures and melodies that Creshevsky makes out of these samples are similar to ‘simulations’. In the article’s second half, Creshevsky’s creation of disembodied ‘superperformers’ is addressed and related to Baudrillard’s transhumanism. Towards the end of the article, Creshevsky’s aesthetic more broadly and what he calls ‘hyperdrama’ are linked to Baudrillard’s ‘transaesthetics’, before a concluding note addressing Baudrillard and Creshevsky’s different dispositions towards hyperrealism.
Method of Building a Hexagonal Grid

Let there is an image with some object made up of hexagons (for example, a photo of a microchannel plate). In this paper, the algorithm that builds a hexagonal grid on a photo of an object is considered. The main principle of the algorithm is detection the contour of the object. Then with the help of special geometric construction determine the coordinates of each nodal point of the hexagonal grid. The developed algorithm consists of several stages: the detection of the object edge, the corners detection on a two-dimensional curve, geometric constructions for determining the nodal points coordinates. At the same time, the widespread problem of edge detection in this case has a certain specificity. As a result of which a special algorithm for contour selection for this particular task was developed. The algorithm was also tested in conditions of high noise and uneven illumination of the object.