Reviewerly/exHarmony huggingface dataset:
Design principles of control systems for self-driving unmanned ground vehicles

The self-driving unmanned ground vehicles (UGV) design problem is analyzed in this paper. Two methodological problems of UGV design are formulated. The first problem is the difference between the purpose of creating a self-driving automobile from UGV, which leads to fundamentally different systemic solutions. The second methodological problem is associated with the need for sufficiently deep knowledge and the ability to work with a number of technologies that are used in the complex to create a control system for self-driving. The joint analysis of these problems allowed us to put forward a number of control systems design principles for self-driving UGV, including the principle of upcycling, the principle of using a discrete-event model for the logical synchronization of the operation of modules and the principle of ensuring the functional safety of the UGV during autonomous movement. The principle of using the discrete-event model for synchronizing the operation of software modules is explained by the example of a Petri net. A general solution to the problem of quantitative evaluation of the safety of autonomous movement of UGV is presented. The considered principles were used during development of prototypes of self-driving control systems for various purposes.
Some features of regional organized groups or the criminal community of mеgapolis: Socio-criminological aspect

The presented article is devoted to the study of the peculiarities of regional organized groups or the criminal community of megapolis (St. Petersburg and Leningrad region), the debatable nature of their socio-criminological characteristics. The article indicates possible ways to improve the effectiveness of countering and preventing crime in general, as well as crimes committed using information and telecommunication technologies, in particular. Attention is drawn to the problem of remote fraud, including Internet fraud, in terms of obtaining remote access to customers’ bank cards. It is emphasized that the effectiveness of law enforcement activities in preventing the activities of organized crime is associated with the vulnerability of existing formulations of organized groups or criminal community. The success and effectiveness of countering the activities of organized crime is largely determined at the level of early prevention.
Influence of information technologies on the formation of professional and value orientations of students: Opportunities and risks

The article examines the problems of the influence of mass media, primarily the Internet, on the value-semantic sphere of the personality of a representative of student youth. The characteristic of the role of formed professional-value orientations in professional activity is given. The key patterns of formation of professional value orientations through media consumption are analyzed. The positive and negative factors of the influence of the Internet space on the development of the value-semantic sphere of the individual are highlighted. There are five main factors of negative influence; the mechanism of functioning of each of them is described. Methods for reducing the considered risks for optimizing the process of forming the axiological sphere of a student’s personality are proposed. The role of the scientific community and public authorities in the development and implementation of appropriate social technologies is characterized.
Summary of Artificial Neural Network Effectiveness Evaluation Technology

In the field of modern military struggle, research and preparation for war issues are becoming more and more important. The evaluation of the effectiveness of platforms and systems affects the research direction of military issues and even military operations directly or indirectly. However, the development of our high-tech evaluation system is relatively slow, and it has not reached the level of promoting the development of platforms and systems. This article starts with the evaluation of the operational effectiveness of artificial neural networks, the research draws on many materials in related fields, summarizes some methods used in the evaluation, and puts forward some thoughts, in order to provide reference value for the development of subsequent evaluation work.
Features of education in the age of digitalization

The article analyzes the problems of education in the conditions of modern technological progress. It is shown that modern digital technologies introduced into the educational process, including in the form of distance learning, have good prospects as they allow to eliminate spatial and social inequality and make available a wide variety of scientific databases in the form of digital libraries. However, digital technologies cannot provide additional opportunities without a good methodological base, appropriate targets, and additional training for teaching staff.
Scientific research methods: levels, forms, methods

The author demonstrates that in order for the creative search to be successful and for intuitive insight to come to the rescue, the following conditions play an important role: 1) reliable, valuable and diverse information regarding the problem under study; 2) possession of effective "cognitive technologies" - methods of information processing, a significant part of which has turned into automatic skills; 3) great perseverance in comprehending the truth. The article may be useful in the framework of training in the disciplines "Science History and Methodology" and "Concepts of Modern Natural Science".
Factor Responsible for Poor Blood Management System

Blood donation and transfusion has been an ever-serious issue and the shortage of blood throughout the world has caused many people to lose their life. An automated system is required to manage the centers and to showcase the information to the interested parties. We have developed a website that singlehandedly solves all these issues related to blood donation and reception.
Generative Adversarial Network for Generating Different Types of Data

A generative adversarial network (GAN) is one of the popular deep generative models. It generates new data similar to the data of a dataset but is not intended to generate different data from them. In this paper, we propose a GAN that generates such different types of data, which a user desires to obtain. In the proposed method, some data of the dataset are iteratively exchanged for ones generated by the generator if the generated data are more helpful in generating the user's desirable ones. The performance of the proposed method is evaluated by comparing it with some other GANs.
Pendular

Pendular is an interactive musical system that explores the physical motion of hanging objects as an interface for creating music. Performers use wind and light to control the environment to which these objects react, and as they swing and spin through the air, their movements are interpreted as sonic transformations ranging from calm and predictable to chaotic and random.
The Effect of Augmented Reality-Based Teaching During Free Play on Changes in Mean length of utterance and Communication Function of Children with Moderate Physical Disabilities

The purpose of this study is to investigate the effect Augmented Reality-based teaching in free play has on both the average length of spontaneous speech and changes in communication function regarding children with moderate physical disabilities. The subject of this study was a five-year-old child with moderate physical disabilities. The research method was designed as a baseline, intervention, generalization, and maintenance phase by designing a medium-to-multiple intermittent baseline between situations. The intervention strategy was mediated by AR based teaching that applied sensory information, action induction, context maintenance, and recognition. The results of this study are as follows: First, the average utterance length of children with severe physical disabilities increased through AR-based instruction in the free play scenario; and second the communication function of children with severe physical disabilities has been diversified through AR-based teaching in the free play scenario. This study is meaningful in that it had a positive effect on the development of spontaneous speech and communication skills in children with moderate physical disabilities; all accomplished through AR-based teaching with in a play situation.
Transmission and Control of the Image Data from Remote Sensing Satellite to the Ground Station

The image data of remote sensing satellite are widely applied all over China and play an important role in economic and social development and ecological civilization construction. Based on the insufficient established ground station of China, the big data of remote sensing satellites should be processed suitably in orbit, compression and distribution are necessary, and be transmitted to the ground rightly and efficiently. The data quantity and the mode of the data transmission are analyzed. The composition and the mechanism of the data transmission system are introduced. The pointing control of antenna determines the rightness and efficiency of the transmission. Requirement of antenna control is analyzed and the way to weaken the disturbance from antenna is proposed. Meanwhile, the pointing control logic of antenna is described and the angle curve of antenna in orbit is given and analyzed.
Factors that affect learning outcomes in online learning

Online learning is an alternative to learning during a pandemic. This learning is done by utilizing technology and information. Constraints in online learning are taken into consideration so that the expected competencies are achieved. These constraints are also referred to as factors. The purpose of this study is to determine the factors that contribute to learning achievement during online learning during the pandemic. This research was conducted at SMAN 1 Kalibawang and SMA N 2 Ngaglik, with 90 respondents as the subject of the research trial taken randomly. The instrument used is a questionnaire and the results of math test scores. This research uses the Partial Least Squares-Structural Equation Model (PLS-SEM). The purpose of PLS-SEM is to confirm how well the variables that have been measured can represent the formed factors. The steps taken in conducting analysis using PLS-SEM include: (1) designing an analytical model by making path diagrams; (2) identifying the model; (3) estimating parameters; (4) drawing conclusions. Parameter estimation is divided into two parts, namely the outer model (measurement model) and the inner model (structural model). The results showed that learning media when online learning had a significant effect on learning achievement. The effect given is 83,3% which has a high effect.
Development of Blockchain-Based Cryptocurrency

The first-ever cryptocurrency invented was bitcoin (BTC) in 2008. With this, the first-ever blockchain network was launched all over the globe. The greatest advantage that Blockchain backed cryptocurrencies offer is the security and non-dependency on a third-party organization for trust issues. It allows independent verifiability of transactions while keeping the transactions anonymous but recognized by a virtual ID. Blockchains can also process transactions at a much lower cost than banks and other financial companies. In May 2016, when the Blockchain Revolution went to print, the entire crypto asset market had a value of $9 billion. Fewer than two years later, the crypto asset market was $420 billion in size. The development of cryptocurrency faces legal challenges as well. The currency developed has to develop trust among the parties involved in transactions. The blockchain technology is continuously evolving as new consensus algorithms are being developed, which may prove to be better than the currently used algorithms. Many different ways have been introduced to modify the modern-day currency into digital currency such as BTC. However, the trust remains an important factor to overcome. As the government of any country cannot monitor the use of cryptocurrencies, they are hesitant to legalizing them as a regular mode of exchange. Cryptocurrencies are heavily affected by 342mobilization amongst its users. In case the demand of cryptocurrency comes down to level zero, then the value of it is also zero.
Nature and Provenance of Heavy Mineral Distribution at Krishna River Delta, East Coast of India

The present study deals with four sedimentary cores viz. Turumella (120m), Inturu (160m), Ponnapalli (110m) and Nizampatnam (150m) which were recovered from the Krishna delta. Total heavy minerals wt% varies from 0.32 to 7.00 (av. 2.44%) in the study area. The major heavy minerals were noticed in four studied cores i.e. opaques (Ilmenite + magnetite), pyriboles (pyroxenes + amphiboles), garnet, zircon, monazite, rutile and other heavy minerals (epidote, kyanite, tourmaline, etc.). Heavy mineral substance in Krishna river sediments forced by basement rocks, drainage basin and weathering conditions of the river environment. The occurrence of different lithological units in the drainage basins is contributing sediments, mainly opaques (Ilmenite + magnetite) and pyriboles (Amphibole + pyroxenes) from Deccan traps as well as Archean provenance which covered more than 55% of drainage basin. The red garnets (almandine), prismatic sillimanites and rounded zircons are derived from khondalites (metapeletic rocks) and pink garnets, elongated zircons and ortho- pyroxenes are derived from charnockites of Eastern Ghat Granulite Belt (EGGB). The fresh appearance of heavy minerals indicates short residence time in depositional environment without any chemical dissolution effect. The rounded and sub rounded grains of magnetite and ilmenite indicate long distance of transportation and/or reworked nature. The sub angular grains of magnetite and ilmenite indicate that they might have been derived from nearby sources, i.e. mainly an Eastern Ghat Group of rocks. The prismatic characteristic of the sillimanite mineral grains also suggests that their derivation is from khondalitic rocks. Opaques are from Deccan traps as well as Archean provenance. Garnets, sillimanites and zircons are derived from khondalites (metapeletic rocks) and charnockites of Eastern Ghat Granulite Belt.
An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach

A large amount of data is generated in the fields of medicine, as healthcare is a major component of a tech-oriented lifestyle. Even now, the pandemic situation in 2020 led to a huge burden as well as medical emergencies. A need for a detailed understanding of various healthcare electronic clinical data (HECD) using Natural Language Processing (NLP) techniques has been recognized. High protection for clinical data that are being analyzed, the patient processed information generated from diagnosis using various methods, such as in ICU (intensive care unit), mortality levels, and resource allocation are performed instantly. Experts from the medical field use a patient&apos;s information from the laboratory results, such as X-ray, MRI, CTC scan, etc., to diagnose the disease using various inputs. The known vital parameters from clinical results, such as blood pressure (BP) and heart rate, always fluctuate and are inconsistent as well as the mismatch of information based on age, name, etc., are to be focused on to resolve the major challenges. Here we used the ICU clinical data as a sample to consider the features based on multiclass word phrase classification from unstructured data using a long short-term approach (LSTM). We categorize the data according to classes and group them into valid information wherein records can be minimal and help reduce the text. Also, valuable information can be protected and sensitive data can be kept confidential by phenomenal support, which is automated using NLP approaches. There are typical steps for treatment based on the critical level of the patients, in such stage time-series is mostly handled as development variables for mortality extraction from ICU data. A supervised structured data from clinical processing involve a bi-directional LSTM approach for processing the samples and brings efficient results from the records to make a reliable model from multiclass prediction.
The Effects of Student-Directed Individualized Transition Plan on the ITP Knowledge, Meeting Skills, and Self-determination of High School Students with Disabilities

The purpose of this study is to examine the effects of the student-directed individualized transition plan on the ITP knowledge, ITP meeting skills and self-determination of high school students with disabilities. The participants in this study consisted of 20 high school students with disabilities(10 in the experimental group and 10 in the control group). To evaluate the effects of the program both the experimental and control groups were evaluated for ITP knowledge, ITP meeting skills and their self-determination skills. The results of this research are as follows: First, the changes in the Participant`s ITP knowledge was significantly larger for the experimental group when compared to that of the control group. Second, ITP meeting skills of the experimental group that participated in the program were found to be significantly improved after the intervention. Third, the changes in the self-determination skills of the experimental group that participated in the program was significantly larger when compared to that of the control group. These findings show that the program was effective towards enhancing the ITP knowledge, ITP meeting skills and self-determination of high school students with disabilities. This research has contributed to addressing the needs of the special education field by providing a program that assists students with disabilities to participate in ITP. Moreover, this study comes equipped with practical implications in terms of providing both effective instructional strategies and materials to promote the active participation of students with disabilities in the ITP process.
HDR Fusion Algorithm Based on PCNN and over Exposure Correction

For traditional HDR fusion algorithm based on mapping functions, the artificial determination of the over exposure region often ignores the spatial distribution and the relation of pixels of the image brightness. In this paper, a stimulated HDR fusion algorithm based on pulse coupled neural network is proposed. For the proposed algorithm, first obtain the external stimuli according to the maximum image brightness, calculate whether the pixel “fires” or not and determine the over exposure region. Then alter the value of external stimuli to perform iteration and construct judgment matrix to make sure that every pixel in the image “fires”. The image irradiance is linear with the illuminance and exposure time. Obtain the brightness mapping function using linear fitting, and synthetize HDR image with over exposure correction algorithm to eliminate artifacts. The algorithm mainly has the following advantages: 1. It fully considers the spatial distribution of the image brightness. 2. For the imaging of different scenes, it self-adapts to separate exposure regions. 3. It performs mapping according to the imaging principles and obtains the mapping function without the exposure time. 4. It reduces the quantitative error of synthetizing image, and the artifacts in the transition region. The algorithm proposed in this paper is experimented under multiple scenes, and the image entropy and spatial frequency are improved greatly.
Decentralized and Secured Applications of Blockchain in the Biomedical Domain

In this book chapter, the decentralized and secured applications of block-chain in the biomedical domain have been discussed to offer visibility and privacy to the biomedical domain. After extensive review of recent work, it has been explained how the blockchain features can be used in biomedical filed. The results show the proposed decentralized electronic-medical block-chain (DEMB) based system and its feature. Also, its future applications, challenges and solutions are presented.
WHAT HOPE FOR DIGITAL BUSINESSES DEVELOPMENT IN NIGERIA?: AN ASSESSMENT OF ONLINE NEWSPAPER COVERAGE OF TWITTER’S SUSPENSION

A recent World Bank report reveals that poverty has been decreasing in all regions of the world with the exception of sub-Saharan Africa (SSA) as more than 45% of countries in the sub-region are off-track from achieving the Sustainable Development Goal (SDG) extreme poverty target. Asanga, Boateng &amp; Akamavi (2016). Hoofmann (TRT News, 2021) echoed similar view when he said that Nigeria has the largest number of people in poverty in the world, with the unemployment rate among 15-34-year-old around 35 percent in 2020; adding that: “Twitter is a digital platform that has been a crucial link in the operations of millions of small and medium-sized enterprises; it allows the kind of business that gives consumers options which leads to innovation and better products and services. It was estimated that the nation stands to lose a whopping sum of N650 million monthly if Twitter suspension remains in place. This paper thus explores the suspension of Twitter operations and reactions by Nigerians and the international community; using the content analysis and case study research designs; while agenda-setting and innovation theories served as the study theoretical foundation to examine how innovative communication will foster the pace of economic development which Nigerians genuinely crave for. Study found that all online news content analysed were foreign based newspapers/ magazines; 6 representing 66.7% of the entire stories dwelt on abuse of freedom of the press; freedom of expression and human rights issue, while economic and political headlines on the ban were two and one stories representing 22.2% and 11.1% respectively.
Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset

The increasing adoption of electronic health record (EHR) systems has brought tremendous opportunities in medicine while enabling more personalized prognostic models. Predicting the risk of chronic diseases using EHRs has attracted considerable attention in recent years. Diabetes is a chronic disease, and using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. However, most work to date has investigated the binary classification problem for predicting the onset of diabetes, but little attention has been given to assessing the risk of developing comorbidities that are major causes of morbidity and mortality. Leveraging massive EHRs brings tremendous promise to advancing clinical and precision medicine informatics research. However, it is very challenging to directly work with multifaceted patient information encoded in EHR data. Deriving effective representations of patient EHRs is a crucial step to bridge raw EHR information and the endpoint analytical tasks, such as risk prediction or chronic disease subtyping. To overcome these problems two new approaches, Heterogeneous Recurrent Convolution Neural Network (HRCC) and Multi Level Spatial Coherence Optimization Approach (MLSCO), are developed in this work. To understand the predictive performance of this approach several performance metrics are used. Compared with traditional machine learning models, the deep learning-based approach achieves superior performance on risk prediction tasks. The experimental results show that this method provides a superior predictive effect than other traditional machine learning models.