Reviewerly/exHarmony huggingface dataset:
Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise Robot

We propose a neural learning approach for a humanoid exercise robot that can automatically analyze and correct physical exercises. Such an exercise robot should be able to train many different human partners over time and thus requires the ability for lifelong learning. To this end, we develop a modified Grow-When-Required (GWR) network with recurrent connections, episodic memory and a novel subnode mechanism for learning spatiotemporal relationships of body movements and poses. Once an exercise is successfully demonstrated, the information of pose and movement per frame is stored in the Subnode-GWR network. For every frame, the current pose and motion pair is compared against a predicted output of the GWR, allowing for feedback not only on the pose but also on the velocity of the motion. Since both the pose and motion depend on a user's body morphology, the exercise demonstration by one individual cannot easily be used as a reference for further users. We allow the GWR to grow online with each further demonstration. The subnode mechanism ensures that exercise information for individual humans is stored and retrieved correctly and is not forgotten over time. In the application scenario, a physical exercise is performed in the presence of an expert like a physiotherapist and then used as a reference for a humanoid robot like Pepper to give feedback on further executions of the same exercise. For evaluation, we developed a new synthetic exercise dataset with virtual avatars. We also test our method on real-world data recorded in an office scenario. Overall, we claim that our novel GWR-based architecture can use a learned exercise reference for different body variations through incremental online learning while preventing catastrophic forgetting, enabling an engaging long-term human-robot experience with a humanoid robot.
Ontology-Based Approach to Risk-Factor Identification to Support the Management of Provisions in Bridge Design

Requirement management is crucial in the design stage of infrastructure project delivery to ensure the designed facility satisfies the owner expectations and avoids any costly redesign, rework, and disputes. The requirements are primarily described in design codes and standards. The relevant requirements from codes are transformed into a set of in computer-readable rules required for computer-aided design and compliance verification. Prior to the construction of rules, the requirements in codes must be converted into an organized, structured format in terms of risks to support easy retrieval of the relevant applicable requirements. However, the unavailability of risk information in the requirement text makes it challenging for a designer to extract the relevant requirements of specific risks to support design tasks, including compliance checking. The manual process of requirement classification according to risk factors is time-consuming, laborious, and error-prone because the codes are mostly voluminous, including thousands of specifications. Much less attention has been paid to develop an automated framework to organize the requirements in terms of risks addressed in them. To address this need, this study has attempted to develop an ontology-based framework to identify relevant risk factors addressed in bridge design requirements to support requirement management. The nine risk factors of bridge engineering used in this study included flood, earthquake, fire, snowfall, wind, temperature, overloading, vessel collision, and blast loading. A risk ontology was developed to represent the conceptualized semantic knowledge associated with each of the nine risk factors. The algorithm was validated on a human-annotated requirement dataset based on the AASHTO bridge design specifications and state design manuals. The developed model yielded a Spearman, Kendall tau, and Pearson correlation coefficient of 0.7223, 0.6021, and 0.7222, respectively. The proposed model is expected to improve specifications retrieval for rules construction, thereby enabling ease of requirement classification for the design process.
Operation Mode and Creation of Film Media Based on the Internet Logic under the Field Theory

With the development of information technology and new media technology, the Internet has had a profound impact on the development of the film and television industry. Mainstream media, new media, Internet companies, cultural media companies, etc. are all eager to try, and the forces from the state, capital, media, and users are intertwined. The film and television field is the behavioral practice field of various stakeholders. Capital stimulates the industrial functions of the film and television industry from the perspective of commercial interests. The interests of users are being redefined in the context of the Internet, and the film and television industry actively promotes the creative process from its own interests. In order to analyze the influence and motivation of the Internet on the operation mode and creation research of the film and television media industry, this paper relied on Bourdieu’s field theory to explore the forces and interests that promote the development of the film and television media industry from different aspects. This provided development suggestions for the construction of a new pattern combining the Internet and film media. The integration of film and television and the Internet has become the new normal in the film and television industry. At present, people’s online entertainment time at home has increased significantly, which has led to a rapid increase in the number of movie viewers. The introduction of Internet technology into the film industry media operation model can improve the operation efficiency of enterprises by 6.5%.
On the Implementation of the Algorithm for Calculating the Functionals of Minkowski Three-Dimensional Digital Space

The paper is devoted to the implementation of an algorithm for calculating the functionals of the Minkowski set in a three-dimensional digital space. The algorithm is based on the calculation of the values of these functionals for various types of node neighborhoods into which a set in a digital space can be divided. The concept of Minkowski functionals appeared in the theory of convex sets in n-dimensional Euclidean space; they are coefficients in the expansion of the volume function ε of a neighborhood of a convex set in powers of ε. Subsequently, it turned out that the concept of functionals can be generalized to the case of sets with singularities, including the case of a set in a digital space. Minkowski functionals of a digital image representing a union cubic voxel intersecting along edges and vertices are statistical measures based on the Euler-Poincare characteristic of n-dimen-sional space, show sensitivity to the morphology of disordered structures, which is confirmed by applied research. They are used in calculating density measures for a number of unordered microstructural models; particle-based models, amorphous microstructures, cellular and foamlike structures. The results of calculations for various microstructures demonstrate a number of qualitative characteristics.&#x0D; The paper studies the implementation of the algorithm for finding the Minkowski functionals for a set in a threedimensional digital space.
Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture Detection

Noise reduction in medical image analysis is still an interesting hot topic, especially in the field of ultrasonic images. Actually, a big concern has been given to automatically reducing noise in human-bone ultrasonic computed tomography (USCT) images. In this chapter, a new hardware prototype, called USCT, is used but images given by this device are noisy and difficult to interpret. Our approach aims to reinforce the peak signal-to-noise ratio (PSNR) in these images to perform an automatic segmentation for bone structures and pathology detection. First, we propose to improve USCT image quality by implementing the discrete wavelet transform algorithm. Second, we focus on a hybrid algorithm combining the k-means with the Otsu method, hence improving the PSNR. Our assessment of the performance shows that the algorithmic approach is comparable with recent methods. It outperforms most of them with its ability to enhance the PSNR to detect edges and pathologies in the USCT images. Our proposed algorithm can be generalized to any medical image to carry out automatic image diagnosis due to noise reduction, and then we have to overcome classical medical image analysis by achieving a short-time process.
GALLERYIST ANNA: CHATBOT AS AN EDUCATIONAL RESOURCE

The case study focuses on a qualitative analysis of the reflection of librarians (n = 12) and students (n = 17) working with the chatbot created through dialogue frames.It focuses on how these respondents understand the interaction with a dialogue system that does not use artificial intelligence, how they are willing to accept the narrative characteristics of such an educational interaction.The discussion focuses on the importance of the design of such educational objects, on the problems and challenges that work with them brings.The case study works with her own created chatbot -Galleryist Anna, which is focused on the development of competencies related to finding information about art artefacts on the Internet.
A Framework of Smart Mobile Application for Vehicle Health Monitoring

The smart system integrates cloud computing and mobile computing, also known as mobile cloud computing. This smart system helps monitor the vehicle's health condition on any device, i.e., platform-independent. Using machine learning algorithms, the smart system helps predict vehicle health and maintain the vehicle's and the driving person's safety. The cloud computing used to deploy this smart system for monitoring the vehicle condition is the Google Cloud Platform. Google Cloud Platform provides various services like Computing and Hosting, Networking, Storage, etc., which help deploy and host web applications on Google Cloud using multiple services. One of the best securities is achieved using the Google Cloud Platform. Several layers are encrypted with specially designed algorithms for the safety of the customer data and applications. Google Cloud Platform helps provide data integrity, making it better for storing all the data. It also provides Denial of Service protection which helps real time protection of servers for hosting the data. The smart system is deployed to only authenticated users eligible to monitor the vehicle's health condition. The health of the car may be tracked in the cloud and on every device with an internet connection and communication services. The mobile application is deployed from the webserver, facilitating secure and safe data browsing. The smart system is developed for displaying vehicle conditions dynamically, Google Maps for tracking the present vehicle location, and manual testing of the vehicle health by entering the values in the portal, which helps notification of risk, medium risk, and no risk of the vehicle condition using machine learning algorithm, which runs at the backend of the application.
An Optimized System for Sentiment Analysis using Twitter Data

Progression in technology and innovation increases internet users, who post their perspectives on social media platforms regarding any product or service. It brings forth significant terms, i.e.,”feedback of users,” termed as sentiments and plays a substantial role for commercial organizations to analyze and find polarity related to their respective services. In Sentiment Analysis, the feature extraction phase is a crucial one that affects the entire process's processing. In the case of high dimensional Real- Time data, it leads to a sparse feature matrix and gives rise to steady processing. In this exploration work, we have proposed an Improved Optimized Feature Sentiment Classifier for Big Data (IOFSCBD) System, which deals with advancing the classifiers by giving improved values in each sort of dataset. Results show better execution of the Improved Optimized Feature Sentiment Classifier for Big Data system System.
Bab Al Qasr Hotel &amp; Residence: sustaining business while housing COVID-19 patients

Learning outcomes This case study aims to yield the following learning outcomes: understand the key performance indicators of successful human resource management (HRM) in the hospitality and tourism sector, especially during pandemics; identify the various concepts and strategies of HRM and recruitment over a short period; conceptualize the types of HRM practices such as safety and health, recruitment and promotion and rewards when facing pandemic conditions as well as the handling of hotel staff in strained times; evaluate the crisis management solutions used by human resources to lower the employee turnover rate; and develop a crisis management plan from a human resource standpoint in a pandemic situation. Case overview/synopsis A five-star hotel in Abu Dhabi, United Arab Emirates (UAE), Bab Al Qasr Hotel &amp; Residence (BAQHotel) opened in October 2016. In April 2020, the hotel began accepting patients with COVID-19 in conjunction with the Abu Dhabi’s Healthcare Company (SEHA), as a support to the UAE Government, through Department of Culture and Tourism Abu Dhabi (DCTAD), to meet the local needs and sustain business during these unforeseen circumstances, which heavily impacted the whole World. Samer Majari, the Human Resource Director of BAQ hotel was responsible for recruiting and supervising delegates, while ensuring the comfort of hotel staff, arranging for their transportation, catering, safety and well-being. To combat the high staff turnover, including COVID-19-positive staff, Majari reviewed the existing staff model; divided the staff into two groups for providing services to both sides of the hotel; arranged for separate accommodations, food and transportation; and retained the existing resources and recruited new workers. This study aims to provide management solutions that concern hiring of staff from the existing UAE market and highlights ways of creating a lower staff turnover rate through incentives and compensations, while also managing staff by motivating them and also safeguarding them against COVID-19. Complexity academic level This case study intends to provide a context for creative solutions to human resource challenges facing organizations during a pandemic. It also involves swift responses to crises faced by HR managers and the immediate solutions required. Therefore, this case study can be used in undergraduate level courses for students pursuing a bachelor’s or a master’s degree in HRM, hospitality management, crisis management or international business studies. Moreover, this case study can be used for corporate training and to help hospitality industry staff, mid-level human resource managers and organizational development practitioners to better understand crisis management in their hotels. Supplementary materials Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management
Wavelet expansion method

The wavelet expansion method is widely used in various fields due to its powerful ability to simulate the oscillating function. This method is applied to discretize the energy variable of neutron angular flux within the resonance energy range. Meanwhile, the conventional multigroup method is applied in fast, thermal energy ranges. This coupled method can obtain the problem-dependent, continuous-energy neutron spectrum within resonance energy range. The method of characteristics is employed as a space-variable solver in this method to keep the powerful capability of dealing with the complex geometry problems. Numerical results show that this is a valuable method to solve the resonance self-shielding calculation problem with complex geometry, and its potential is promising as applied to realistic reactor problems.
Assessment of solar energy system by probability and sampling distribution

Data science is built on the foundations of probability and statistics. Probability theory is quite useful in formulating predictions. Data science relies heavily on estimates and projections. We make estimations for further examination with the use of statistical approaches. As a result, statistical approaches are heavily reliant on probability theory and data is the foundation of all probability and statistics. This chapter describes assessment of solar energy system by probability and sampling distribution. The evaluation of solar energy parameters is done through the discrete, continuous, binomial, poisson and hypergeometric distribution process. In this chapter, different types of sampling technique are also used for the assessment of solar energy parameters. This chapter ends with the utilization of Weibull distribution in the field of solar energy system.
DEVELOPMENT OF A METHODOLOGY FOR ASSESSING THE LEVEL OF SATISFACTION WITH THE QUALITY OF UNIVERSITY GRADUATES' TRAINING

Meeting the needs of university students and employers in high quality education is the key task of a university. In order to ensure the competitiveness of the university in the educational market, it is necessary to study the requirements and the level of satisfaction with the level of competence of university graduates among trainees and employers. To assess the perceived quality of university graduates' training, a questionnaire survey methodology is proposed, which involves determining the motivation of applicants, the necessary qualities of graduates, the degree of satisfaction with the level of theoretical, practical and socio-psychological training. Basing on the formulated hypothesis, the key problems that require studying were identified. When studying the satisfaction of trainees, the satisfaction of undergraduates with the choice of their future profession, the quality of training, employment prospects, expectations in relations with employers were considered. Taking into account that employers in the region are not fully satisfied with the quality of graduates' training, the study determined the ways to fill vacancies, the level of satisfaction with various criteria for the quality of graduates' training. The assessment of employers' level of satisfaction with the quality of training at universities was carried out on the basis of determining the significance and level of satisfaction with the theoretical, practical and socio-psychological training of university graduates, as well as personal characteristics of university graduates. The study established that employers’ participation in the educational process provides enhanced training for university graduates and contributes to the growth of employers’ satisfaction.
How persuasive is AI-generated argumentation? An analysis of the quality of an argumentative text produced by the GPT-3 AI text generator

In this paper, we use a pseudo-algorithmic procedure for assessing an AI-generated text. We apply the Comprehensive Assessment Procedure for Natural Argumentation (CAPNA) in evaluating the arguments produced by an Artificial Intelligence text generator, GPT-3, in an opinion piece written for the Guardian newspaper. The CAPNA examines instances of argumentation in three aspects: their Process, Reasoning and Expression. Initial Analysis is conducted using the Argument Type Identification Procedure (ATIP) to establish, firstly, that an argument is present and, secondly, its specific type in terms of the argument classification framework of the Periodic Table of Arguments (PTA). Procedural Questions are then used to test the acceptability of the argument in each of the three aspects. The analysis shows that while the arguments put forward by the AI text generator are varied in terms of their type and follow familiar patterns of human reasoning, they contain obvious weaknesses. From this we can conclude that the automated generation of persuasive, well-reasoned argumentation is a far more difficult task than the generation of meaningful language, and that if AI systems producing arguments are to be persuasive, they require a method of checking the plausibility of their own output.
On the Effectiveness of Images in Multi-modal Text Classification: An Annotation Study

Combining different input modalities beyond text is a key challenge for natural language processing. Previous work has been inconclusive as to the true utility of images as a supplementary information source for text classification tasks, motivating this large-scale human study of labelling performance given text-only, images-only, or both text and images. To this end, we create a new dataset accompanied with a novel annotation method—Japanese Entity Labeling with Dynamic Annotation—to deepen our understanding of the effectiveness of images for multi-modal text classification. By performing careful comparative analysis of human performance and the performance of state-of-the-art multi-modal text classification models, we gain valuable insights into differences between human and model performance, and the conditions under which images are beneficial for text classification.
Introduction

The continuation of Moore’s law and resulting proliferation of cheap and powerful computational hardware in a small form factor, coupled with advances in sensors and state estimation algorithms, has dramatically increased the autonomy of small-scale flying robots in recent years.
Urban Image Geo-Localization Using Open Data on Public Spaces

In this paper, we study the problem of urban image geo-localization, where the aim is to estimate the real-world location in which an image was taken. Among the previous approaches to this task, we note three distinct categories: one only analyzes metadata; the other only analyzes the image content; and the third combines the two. However, most previous approaches require large annotated collections of images or their metadata. Instead of relying on large collections of images, we propose to use publicly available geographical (GIS) data, which contains information about urban objects in public spaces, as a backbone database to query images against. We argue that images can be effectively represented by the objects they contain, and that the spatial geometry of a scene—i.e., the positioning of these objects relative to each other—can function as a unique identifier for a particular physical location. Our experiments demonstrate the potential of using open GIS data for precise image geolocation estimation and serve as a baseline for future research in multimedia geo-localization.
A Fine Grained Quality Assessment of Video Anomaly Detection

In this paper we propose a new approach to assess the performance of video anomaly detection algorithms. Inspired by the COCO metrics we propose a quartile based quality assessment of video anomaly detection to have a detailed breakdown of algorithm performance. The proposed assessment divides the detection into five categories based on the measurement quartiles of the position, scale and motion magnitude of anomalies. A weighted precision is introduced in the average precision calculation such that the frame-level average precision reported in categories can be compared to each other regardless of the baseline of the precision-recall curve in every category.