Reviewerly/exHarmony huggingface dataset:
Development of an Application to Manage Hygiene Measures in the Grocery Retail Sector

Managing the hygiene measures in the grocery retail sector has gained more and more importance during the COVID-19 pandemic. The sector faces a great challenge to keeping further spread as low as possible while not restricting people in their daily routines. Research focuses on what restrictions have been taken into action. However, less attention has been paid on the effects of the shopping experience of customers. The author develops an application that allows the reduction of the number of people in a shop while at the same time avoiding long queues and loss of time, exploiting the possibilities of digital technologies. Problems that have not yet been resolved despite hygiene measures and their effects are discussed. The developed application provides a basis for further development and conception of prototypes using digital technologies.
Feedforward Neural Networks

The feedforward neural network is the most fundamental type of neural networks from historical viewpoint and its wide applicability. This chapter discusses several aspects of this type of neural network in detail. Section 2.1 describes its fundamental structure and algorithm, Sect. 2.2 various types of layers, Sect. 2.3 some techniques for regularization, Sect. 2.4 the acceleration techniques for training, Sect. 2.5 the methods for weight initialization, and finally Sect. 2.6 the model averaging technique and the Dropout.
Eye Controlled Rover for Mapping the Environment

This work is focused on implementing various robotic systems to fulfill the objective of creating a prototype rover which is controlled with the eye ball movement of the user through webcam and this rover is used to map the surrounding environment. It is installed with the first person view camera system which will act as the eye of the rover. Hence, the user can deploy the rover in the desired pathway. Mapping circuitry consists of a time-of-flight sensor which measures the distance of the surrounding objects. The required output graph will be displayed in our sketchpad (processing software). The rover is controlled by an autonomous eye tracking system which will help the user to control the rover from remote places even if he cannot reach the required area. Rover and the user will have their own eyes to observe the required necessities distantly. NodeMCU that is mounted on the rover receives the data that feeds the data to a motor driver which is again connected to the motor driver mounted on the top of the automation. Motor driver accordingly drives the motor and hence the wheels are rotated either clockwise or anticlockwise according to our command. It will either move forward, left or right and stop
GATE (Gregorian Archives Texts Editing): An Online Resource of Angelo Secchi Correspondence

Starting by reflecting on the current opportunities for research about Angelo Secchi, we analyse a possible theoretical approach that can be used to study the Secchi materials preserved in the Historical Archives of the Pontifical Gregorian University (APUG). We describe a collaborative research project using the material hosted on the Gregorian Archives Texts Editing (GATE) platform, outlining its functionalities and features and describing the actions available for users who would like to contribute to the project, in particular a collaboration to produce an edition of Angelo Secchi correspondence and an enhanced bibliography of his work.
Energy Consumption Forecasting Using a Stacked Nonparametric Bayesian Approach

In this paper, the process of forecasting household energy consumption is studied within the framework of the nonparametric Gaussian Process (GP), using multiple short time series data. As we begin to use smart meter data to paint a clearer picture of residential electricity use, it becomes increasingly apparent that we must also construct a detailed picture and understanding of consumer's complex relationship with gas consumption. Both electricity and gas consumption patterns are highly dependent on various factors, and the intricate interplay of these factors is sophisticated. Moreover, since typical gas consumption data is low granularity with very few time points, naive application of conventional time-series forecasting techniques can lead to severe over-fitting. Given these considerations, we construct a stacked GP method where the predictive posteriors of each GP applied to each task are used in the prior and likelihood of the next level GP. We apply our model to a real-world dataset to forecast energy consumption in Australian households across several states. We compare intuitively appealing results against other commonly used machine learning techniques. Overall, the results indicate that the proposed stacked GP model outperforms other forecasting techniques that we tested, especially when we have a multiple short time-series instances.
Formation of Economic Consciousness among Students of an Agrarian University on the Example of the Oktyomsky Branch of Federal State Budgetary Educational Institution of Higher Education “Arctic State Agrotechnological University” (ASAU)

Abstract The article gives an attempt to form economic consciousness among agro-university students. The results of pedagogical activity are reflected here. The model of formation of economic consciousness in students with 2- 4 courses is proposed. They study according to the main professional educational program for preparing bachelors of 35.03.04 Agronomy. The goal of research is to determine the importance of the human factor in activating the role at the optimal level of the systematic development of economic consciousness among students of an agrarian university using the example of students and graduates of the Oktyomsky branch of the Yakut State Agrotechnological University. As a result of the formation of economic consciousness, students have a motivation and creative need for the implementation of planned economic projects. These projects were introduced into the agricultural production of agro-industrial complex of Yakutia. Thus, the project of Suzdalova A.A. on the cultivation of potatoes in the Arctic region in open ground was implemented. Graduates actively participate in competitive projects. So Ivanov D.A. organized the scientific circle “Dennatory life”. He became the winner of many competitions at both the local and regional levels. And the graduate of Illarionov I.D. works as individual entrepreneur on the production of seed production of grain crops. At the same time he also took part in the forum “Graduate-2018: For a new village!” Thus, an attempt to create a model for the formation of economic consciousness was implemented in the Oktemsky branch of the Arctic SAU.
A Comparative Study of Korean Feature Granularity Based on Hybrid Neural Network

In natural language processing, the selection of the token is a very important step. The original text should segment into some granularity, then subsequent processing and analysis work can be carried out. The influence of select different segmentation granularity on the Korean text classification task is discussed. Due to the Korean language composition characteristics, combined with the linguistic knowledge the text was divided into six different levels: phoneme, syllable, subword, word, space writing without suffix, and space writing. Then build text semantic expressions in the vector space model. According to the different granularity performance in five classic classifiers and six deep learning models, Korean text feature representations were analyzed and compared in six different granularities. The Korean scientific literature was classified, and the results show that the spacing without suffix level performs best in seven classifiers, and the highest classification accuracy rate reaches 91.94%.
Internal Software Quality Evaluation of Self-adaptive Systems Using Metrics, Patterns, and Smells

Quality has a key role in the functioning, maintenance, and longevity of software. To evaluate the software quality, different points of view and mechanisms may be adopted, e.g., quality attributes, runtime performances. In this paper, we are interested in the internal quality of self-adaptive systems (SAS). SAS are more complex than non-self-adaptive systems (NSAS) because they implement also the mechanisms to monitor the execution environment, to analyze the gathered data about the environment, to plan adaptation strategies and to execute necessary adaptations required by the current state of the system. The available evaluation approaches for SAS focus mainly on the runtime performances achieved through the self-adaptive mechanisms. We consider that also the internal quality of SAS is equally important for their evaluation as for any other software. Therefore, we analyze 20 SAS using 4 different quality evaluation mechanisms: software metrics, design patterns, code and architectural smells. To discuss the quality of SAS, in our analysis we have considered 20 NSAS as a quality reference. Hence, we compare the quality of SAS with the quality of NSAS, and discuss the possible reasons behind the identified quality issues.
The Road Traffic Data

The data are analysed concerning the vehicle traffic on a national road. These data are the numbers of vehicles passing by a measurement station during one hour, and the data items (“objects” or “observations”) are individual days, characterised, therefore, by 24 numbers each. These observations have been classified according to the days of the week, since such daily traffic intensity profiles are deemed to primarily depend upon the day of the week. Reverse clustering is applied to verify this classification, with a robust, but also somewhat surprising effect: a different classification appears to be proposed by the results from the reverse clustering.
Power Towers Knowledge Base Model Construction and Inference Analysis Based on Ontology

Abstract Due to the knowledge limitations in current traditional manual inspection of power towers and the difficulty of knowledge sharing and management, this paper would discuss the components of power towers with the characteristics and connections of different components based on the analysis on manual inspection. And then, an ontology-based knowledge model construction would be established for power towers. Finally, the potential association relationships in the model would be analysed according to the ontology inference rules, and the hidden information was excavated with several examples. The establishment of this knowledge base provides technical support for the path planning of UAV’s automatic inspection and helps with the fault diagnosis and identification so that it improves the sharing and reusability of knowledge in the field of power towers.
Design of four-rotor autonomous target-dropping aircraft based on image processing

Abstract The aim of this study is to design and produce a quad-rotor autonomous target shooting aircraft based on STM32F407 microprocessor and OV7725 image acquisition system. Herein, the quaternion is defined by using Euler’s theorem, navigation coordinate system and carrier coordinate system. Taking STM32F407 master controller as the core, the control system of the aircraft is composed of, distance measuring and height setting module, six-axis motion sensor measurement module, three-axis magnetometer measurement module and camera unit trajectory tracking module. Then, four circuits of the flight control system were designed, including the main control circuit, attitude measurement circuit, throwing rake control circuit and image processing circuit. In addition, three algorithms related to aircraft were prepared, including attitude control cascade PID algorithm, height control algorithm and attitude solution algorithm, and an image processing software was designed. On this basis, the aircraft was built, and finally its flight attitude, height, fixed point, and hover were tested. The results showed that all the algorithms are correct, and the aircraft can successfully pitch, roll, yaw, recognize image trajectory, and automatically drop materials and cruise.
Designing the Information Educational Environment of the Studying Course for the Educational Process Management Using Cloud Services

The authoritative International Data Corporation (IDC) estimates that by 2023, global spending on public cloud services and infrastructure will be more than double, mainly due to the introduction of digital transformations. Globally, the cloud market is becoming a field of increasingly fierce competition between the world's leading IT corporations. Modern education is rapidly evolving due to teachers who in recent years have introduced cloud services in their professional activities. There was an urgent question about the formation of information and educational environment for cooperation, collaboration, communication and learning mobility. This study is devoted to the analysis of the methods peculiarities of designing the information and educational environment of the discipline with the help of GoogleServices. The authors reviewed Google Services for designing open and closed information and educational environments, which are free, so they can be actively used in the educational process. The advantages of open and closed information-educational environment of the discipline as environments for the organization and management of the educational process are described. In the study, the authors identified the following characteristics of the information and educational environment: openness (support for modern standards, Internet / Intranet technology); scalability (increasing the number of services provided in the environment; the amount of information that can be processed); expansion of databases and knowledge (does not lead to the need to reconfigure the system); integration (the ability to organize a single educational environment to expand the tasks associated with the development and design of educational and methodological support of the educational process); adaptability (dynamic adjustment to the needs of both a particular educational institution and an individual user).
Pythonpad

We propose Pythonpad, an open-source JavaScript library that supports web-based Python programming exercises. Unlike other standalone web-based programming tools, Pythonpad can be easily integrated into other websites. Although it runs learners' Python code in client-side web browsers, Pythonpad supports a file system, building and importing external modules, and many essential built-in Python libraries to teach basic programming concepts in CS1 classes.
Word Boundary Estimation of isiZulu Continuous Speech

Typical automatic speech recognition algorithms assume that spoken words are completely isolated; this is not always the case in real-world applications. Consequently, there is a need for intra-sentence segmentation of words uttered in a continuous speech. Word boundary estimation is a method that was proposed for the separating of words. There has been extensive research on western languages, especially English. However, limited investigations have been done for indigenous languages. IsiZulu is the most spoken indigenous language in South Africa. This paper proposes the use of prosodic features and higher-order statistic (HOS) features to classify IsiZulu speech segments i.e. noise, voice and boundary. For automatic classification, supervised learning was used.
Engines of order: A mechanology of algorithmic techniques. BernhardRieder. Amsterdam: Amsterdam University Press, 2020, 352 pp., € 115,00 (hardcover). (ISBN 9789462986190). € 0,00 (PDF). (ISBN 9789048537419).

Journal of the Association for Information Science and TechnologyVolume 72, Issue 8 p. 1092-1095 BOOK REVIEW Engines of order: A mechanology of algorithmic techniques. Rieder, Bernhard. Amsterdam: Amsterdam University Press, 2020, 352 pp., € 115,00 (hardcover). (ISBN 9789462986190). € 0,00 (PDF). (ISBN 9789048537419). Ryan Shaw, Corresponding Author ryanshaw@unc.edu orcid.org/0000-0002-5486-159X School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Correspondence Ryan Shaw, School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Email: ryanshaw@unc.eduSearch for more papers by this author Ryan Shaw, Corresponding Author ryanshaw@unc.edu orcid.org/0000-0002-5486-159X School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Correspondence Ryan Shaw, School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Email: ryanshaw@unc.eduSearch for more papers by this author First published: 01 March 2021 https://doi.org/10.1002/asi.24462Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume72, Issue8August 2021Pages 1092-1095 RelatedInformation
Progressive low/high-resolution Space Attention Fusion Network for Single Image Super-Resolution

Abstract The general single image super-resolution methods mainly extract features from the high-resolution (HR) space by the pre-upscaling step at the beginning of the network or from the low-resolution (LR) space before the post-upscaling step at the end of the network. The former way requires high computation as well as misleading the network by wrong artificial priors. The latter way cannot learn mapping well by only conducting simple operations in HR space. In this paper, we aim to utilize the features from LR and HR space more efficiently and propose the novel network, which applies a frequency-slicing mechanism to divide features into LR and HR space, a direction-aware fusion residual group to extract distinctive features in LR space and an attention fusion module to recalibrate features in HR space. The experimental results demonstrate that our model is superior to the state-of-the-art methods upon quantitative metrics and visual quality.
Multihop Concurrent Big Data Sharing via Multithreading using Blockchain on a Decentralized Network

This study aims to provide a novel concept of multihop concurrent communication between blockchain network peers to facilitate big data sharing via threads in a multithreaded environment to reduce effective transmission time required for sharing a large file. In a general client-server model, the entire load of sharing and synchronization of files is split among a service provider and a client. A three-tier model too provides the same rate of speed irrespective of the number of nodes a current node is connected with, provided there is no network congestion. This paper focuses on how decentralization might help to effectively overcome such problems. Decentralization would increase the number of workers that will redirect data from a server node to a client node and blockchain would maintain security when data is being shared. The proposed model can be used to transfer big files in a relatively less span of time. Moreover, the study elaborates on design implications and considerations a user must understand to effectively use this model for achieving the holy grail to acquire better results when implemented on his/her systems.
Efficient Deep Learning Approach to Address Low-Resolution Person Re-Identification

Person re-identification is a vital part of the computer vision and plays an important role, especially for surveillance applications, e.g., intelligent video analysis, forensic, behavior analysis, and robotics. Mostly it is assumed that gallery and target images have the same resolution. However, this assumption fails in real-world re-identification because images taken by security cameras are mostly low-resolution and have pose variations, background clutters, and occlusions. So it makes the re-identification a challenging task. We proposed an efficient deep learning approach to address the low-resolution problem that regenerates low-resolution images to uniform high-resolution images. Afterward, these high-resolution images are passed to the re-identification network to get the final output. We employ a feedback network to convert low-resolution images to high-resolution. Our network's performance is tested on two datasets, MLR-VIPeR, MLR-DukeMTMC-ReID, and our model achieved superior results compared to the other algorithms.
Medical Image Registration via Similarity Measure based on Convolutional Neural Network

Registration, which is exploited to establish the corresponding relationship between a group of images, is of importance for medical applications. Within the image processing process, a similarity measure is an essential stage. To note that the effectiveness of similarity measure is to evaluate the discrepancy between a set of image slices, which greatly affects the performance of registration. Most of the previous algorithms can be categorized in model-based methods, which rely on their suitability to the images. Meanwhile, these similarity measures can not satisfy the requirements of efficiency and accuracy in medical image registration. To address the above-mentioned problems, one novel similarity measure is presented with a convolutional neural network. Experiments were conducted to evaluate the proposed similarity measure with two public DIARETDB1 and RIRE. The numerical and visual outcome both support our work.
Generating Adversarial Examples Based on Subarea Noise Texture for Efficient Black-Box Attacks

Nowadays, machine learning algorithms play a vital role in the field of artificial intelligence. However, it has been proved that deep convolutional networks (DCNs) are vulnerable to interference from adversarial examples. In this paper, we innovatively simulate natural textures by adding continuous noise to image subareas to generate adversarial examples, which can achieve up to 90% fooling rate on the object detection tasks (YOLOv3/Inceptionv3). The experimental results show that DCNs based on ImageNet dataset training relies too much on the feature aggregation of lower subareas in the classification task. It is instructive that when training DCNs, we need to consider not only the pursuit of accuracy but also the nature of model feature learning.
Beyond Word Embeddings: Temporal Representations of Words using Google Trends

The main essence of word representation in a vector space is to preserve the similarity between the words. Traditional measures of word similarity retain the contextual or semantic affinity among the words. In this study, we propose an alternative word embedding scheme which considers the temporal relationships among the words. We employ the Google Trends search queries along with the respective time series information to represent words in a vector space. Our experiments show that the proposed representation is capable of incorporating temporal context that is otherwise unavailable in conventional word representations.