Reviewerly/exHarmony huggingface dataset:
How Digitalization in Tourism Led to Moral Disengagement of Employees: Differential Impacts on X, Y, Z Generations in Egyptian Hotels

The recent global trend towards digitalization is causing a lot of economic, political and social changes to all world populations and states. From travel agencies to Online Travel Agencies (OTA’s) in Tourism and from Central Reservation Offices (CRO’s) to Global Distribution Networks (GDS) in hospitality business, this is how the world changed. Even social media is converting from Facebook to Metaverse and from Two Dimentional (2D) to Virtual Reality (VR) and Virtual Augmented Reality (VAR) besides to holograms. Many researches explored the impacts of digitalization on these perspectives. However, few if any were focused to measure the shift in moral models adopted due to this shift in life, from physical to digital. This paper targets to explore the moral disengagement caused by the aforementioned trend on employees from different generations “x, y and z” concerning total performance, reputation, present and future image of hospitality business operations and how is this perceived among them and the role of accumulated experience to mitigate these impacts. Keywords: digitalization, x, y, z generations, moral disengagement, immoral decisions, reputation, total performance, present and future image, accumulated experience
Survey on Cancer Diagnosis from Different Tests and Detection Methods with Machine and Deep Learning

Cancer is one of the most dreaded and antagonistic diseases in the world, being meticulous in excess of 9 million deaths unanimously. Many researchers established that detecting cancer disease at early stages leads to an increase in probability to recuperate from cancer disease. In this proposed literature review work, we have chosen machine learning and also deep learning methods for cancer disease diagnosis in human body through algorithm based on issues, laboratory tests, imaging tests, biopsy, bone scan, computerized tomography (CT) scan, positron emission 240tomography (PET), ultrasound images, etc. Moreover, we investigated how existing work analyzed cancer disease using recent technologies. The main objective of this state-of-art work is to evaluate performance by accuracy estimation on cancer disease prediction via machine learning. Furthermore, comparison of existing work specifies that how cancer disease were diagnosed, ways to prevent during early stage of cancer disease, or classification of disease by means of deep learning approaches were scrutinized, which makes cooperative in medical diagnosis synchronal relevance. The intention of this state of artwork is to afford investigators to choose to work in realizing machine learning as well as deep learning approaches for cancer disease identification.
Extensive Study of WBC Segmentation Using Traditional and Deep Learning Methods

According to the WHO annual report, the death rate related to blood diseases is very high in the Asian continent. The existing traditional system is prolonged and tedious and also based on the expertise’s knowledge. Therefore, the development of an automated blood-related disorder diagnostic system is very essential to make the system error-free and more effective. As per the hematologist’s opinion, most of the disease can be identified by the White blood cells (WBC) related information. Thus, the main goal of this chapter is to segment WBCs from microscopic images using different traditional and deep learning (DL) algorithms. For this work, three traditional methods, i.e., global thresholding, k-means clustering and one DL-based model, i.e., U-Net is implemented. The extensive work of experimentations has been conducted on ALL-IDB dataset. The performance of the Global thresholding, the K-means clustering technique and the U-Net method is evaluated with the help of metrics like Jaccard Index, accuracy, sensitivity, and specificity. It has observed that the U-Net method is the best performing method for the WBC segmentation.
Identification of mineral characterization and alteration types in primary tin deposits of oxide skarn type in Batubesi mining areas by petrographic analysis and grain counting analysis

Abstract The presence of primary tin ore on Belitung Island can be found in several areas with varying amounts of presence. One area that has the potential is the Batubesi TB area, Burung Mandi Village, Damar District at East Belitung Regency. This research aims to identify mineral assemblages and alteration types in oxide skarn deposits. The methods used are outcrop observation, sampling, and mineralization-alteration identification. Testing of rock samples through petrographic analysis is to identify minerals through their polarization optical properties and GCA analysis (Grain Counting Analysis) is to determine specific types of minerals that cannot be seen through petrographic analysis. Macroscopically, oxide skarn deposits have a greenish-brick red, black appearance which is dominated by ore minerals (iron oxide, iron carbonate, and iron sulfide), and in some parts chlorite mineral dissemination. Based on the observation result of the optical properties of the rock samples, metallic minerals other than those that have been directly identified were found, such as ilmenite, siderite, cassiterite, and chlorite. The results of the GCA analysis indicate the presence of cassiterite, pyrite, quartz, siderite, hematite, and limonite minerals. Based on these minerals, it can be seen that the type of alteration in the study area is skarn alteration.
Research on multi-robot formation obstacle avoidance algorithm

In the field of robotics, mobile robots have always been a hot topic of research. Aiming at the problem of mobile robot formation, multi-robot collaborative control technology is adopted, and the leader-follower algorithm is realized through the design of the leader and follower roles, and the multiple robot formation control is realized, and the dynamic window algorithm is integrated to achieve the formation to avoid obstacles. First, the leader-follower algorithm is introduced and theoretically derived. Secondly, the dynamic window algorithm added in the formation collaborative control process is introduced and theoretically derived. Finally, through the simulation of cooperative control of formations, the dynamic window algorithm and the artificial potential field algorithm were used for simulation and comparison of the formation travel process and the path trajectory traveled by the final formation, respectively.
Partial quantisation scheme for optimising the performance of hopfield network

The ideal Hopfield network would be able to remember information and recover the missing information based on what has been remembered. It is expected to have applications in areas such as associative memory, pattern recognition, optimisation computation, parallel implementation of VLSI and optical devices, but the lack of memory capacity and the tendency to generate pseudo-attractors make the network capable of handling only a very small amount of data. In order to make the network more widely used, we propose a scheme to optimise and improve its memory and resilience by introducing quantum perceptrons instead of Hebbian rules to complete its weight matrix design. Compared with the classical Hopfield network, our scheme increases the threshold of each node in the network while training the weights, and the memory space of the Hopfield network changes from being composed of the weight matrix only to being composed of the weight matrix and the threshold matrix together, resulting in a dimensional increase in the memory capacity of the network, which greatly solves the problem of the Hopfield network’s memory The problem of insufficient memory capacity and the tendency to generate pseudo-attractors was solved to a great extent. To verify the feasibility of the proposed scheme, we compare it with the classical Hopfield network in four different dimensions, namely, non-orthogonal simple matrix recovery, incomplete data recovery, memory capacity and model convergence speed. These experiments demonstrate that the improved Hopfield network with quantum perceptron has significant advantages over the classical Hopfield network in terms of memory capacity and recovery ability, which provides a possibility for practical application of the network.
An Approach to Identifying and Quantifying Bias in Biomedical Data

Data biases are a known impediment to the development of trustworthy machine learning models and their application to many biomedical problems. When biased data is suspected, the assumption that the labeled data is representative of the population must be relaxed and methods that exploit a typically representative unlabeled data must be developed. To mitigate the adverse effects of unrepresentative data, we consider a binary semi-supervised setting and focus on identifying whether the labeled data is biased and to what extent. We assume that the class-conditional distributions were generated by a family of component distributions represented at different proportions in labeled and unlabeled data. We also assume that the training data can be transformed to and subsequently modeled by a nested mixture of multivariate Gaussian distributions. We then develop a multi-sample expectation-maximization algorithm that learns all individual and shared parameters of the model from the combined data. Using these parameters, we develop a statistical test for the presence of the general form of bias in labeled data and estimate the level of this bias by computing the distance between corresponding class-conditional distributions in labeled and unlabeled data. We first study the new methods on synthetic data to understand their behavior and then apply them to real-world biomedical data to provide evidence that the bias estimation procedure is both possible and effective.
Fraud Detection in Online Market Transactions

E-Commerce transaction process involves multiple entities at different stages such as market place, merchants, payment gateways, financial institutes. Each of them can act as a vulnerability or attack point for Malicious acts. This makes online marketing systems adapt to high-level security and data handling technology solutions like machine learning, deep learning and predictive analytics which are efficient enough to deal with highly sensitive data, predict frauds and unwanted behavioral patterns in this data. Predictive analytics with machine learning is good fraud detection system helps to identify the fraud transaction accurately and should make the detection possible in real time transactions. The techniques have been used to detect whether a transaction is fraudulent or not.
Understanding VR Audiences

Becoming an audience member in virtual reality (VR) can be a surreal experience, like entering a pocket reality, like finding Narnia in your wardrobe, like popping through a chalk painting on the pavement. Quite distinct from our relationship with big and small glowing rectangles, in VR, we are now present at the heart of an experience, often with a newfound ability to move within and influence a story as it unfolds.
Water Desalination Plant Fault Detection using Artificial Neural Network

&lt;p&gt;Water famine is very cruel. The desalination plant was brought in to alleviate the water shortage. A desalination plant has been set up in many places around the world. In the view of expanding worldwide need of fresh water, the usage of desalination plant becomes more common. There are more than twenty-five thousand desalination plants around the world. Many industries require treated water for production, water treatment, and other functions. Water quality is occasionally insufficient or does not satisfy the quality criteria for manufacturing causes. So, the enterprises utilize desalination systems to purify water for their own usage. It makes the water safe to drink as well as suitable for a variety of industrial applications. The fault occurring in the desalination plant, it slows down the processing the speed and reduce the output rate. In this study, focuses on the faults like not under system control, electrical fault, pump fault, control valve fault, inaccurate signal, old data fault, derived fault and transmitter fault. The proposed artificial neural network with the single and double component fault (ANN S-DCF) is introduced to detect the faults occurring in the desalination plant. The faults are splitted into two catagories and characteristics of each fault are trained in the artificial neural network. The result of this work achieves the best accuracy comparing to the existing techniques of SVR (support vector regression), PCA (principal component analysis) and DPLS (dynamic partial least square) method. This study achieves the accuracy rate of 96%, precision rate of 93% and sensitivity rate of 95% respectively with low complexity and high operational speed.&lt;/p&gt;&#x0D;
Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.
Recent Advances in Intelligent Textbooks for Better Learning

Abstract Intelligent textbooks are a new form of digital textbooks that provides students with intelligent learning services, such as automatic question answering, adaptive navigation support, automatic linking, and personalized recommendation. Educators are investing much effort into the conversion of their paper-based textbooks and digital textbooks into intelligent textbooks. This chapter provides a brief introduction to the development of intelligent textbooks and reviews major adaptive technologies and the usage of intelligent textbooks in the past decade. We first summarize the development of intelligent textbooks and the existing authoring platforms. Then, the key intelligent technologies used in the intelligent textbooks are reviewed, including domain modeling, student modeling, and instruction technologies. Moreover, the empirical studies conducted to evaluate intelligent textbooks are summarized. This chapter would promote understanding of the current state of the technology and platform development of intelligent textbooks.
Risk assessment of geological hazards in Southern Shaanxi based on machine learning

Due to the complex geological conditions and frequent disasters in southern Shaanxi, the regional geological disaster risk assessment is of great significance for disaster prevention and control in southern Shaanxi. Taking southern Shaanxi as the research object, this paper selects 13 factors such as topography, land use, and vegetation cover as evaluation factors, and uses three machine learning models of BPNP, KLR, and LMT to evaluate the hazard of disasters in southern Shaanxi. Vulnerability is obtained based on AHP. Using the risk calculation model proposed by the United Nations, the vulnerability and hazard are superimposed to calculate the risk zoning map. The results show that the dense road networks and river valley population aggregation areas have a high vulnerability, the area of low-risk area is about 59438.931 km2, the medium-risk area is about 7915.934 km2, and the high-risk area is about 2567.653 km2, and the extremely high-risk area is about 102.456 km2.
An Effective Online Sequential Stochastic Configuration Algorithm for Neural Networks

Random Vector Functional-link (RVFL) networks, as a class of random learner models, have received careful attention from the neural network research community due to their advantages in obtaining fast learning algorithms and models, in which the hidden layer parameters are randomly generated and remain fixed during the training phase. However, its universal approximation ability may not be guaranteed if the random parameters are not properly selected in an appropriate range. Moreover, the resulting random learner’s generalization performance may seriously deteriorate once the RVFL network’s structure is not well-designed. Stochastic configuration (SC) algorithm, which incrementally constructs a universal approximator by obtaining random hidden parameters under a specified supervisory mechanism, instead of fixing the selection scope in advance and without any reference to training information, can effectively circumvent these awkward issues caused by randomness. This paper extends the SC algorithm to an online sequential version, termed as an OSSC algorithm, by means of recursive least square (RLS) technique, aiming to copy with modeling tasks where training observations are sequentially provided. Compared to the online sequential learning of RVFL networks (OS-RVFL in short), our proposed OSSC algorithm can avoid the awkward setting of certain unreasonable range for the random parameters, and can also successfully build a random learner with preferable learning and generalization capabilities. The experimental study has shown the effectiveness and advantages of our OSSC algorithm.
ACQUISITION OF DIGITAL SKILLS OF UNIVERSITY TEACHERS: A MASSIVE EXPERIENCE

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 6745-6749ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1703Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
THE ROLE OF LEADERSHIP IN PROMOTING SOCIAL RESPONSIBILITY IN HIGHER EDUCATION

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 2455-2464ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0616Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
CREATION OF VIDEOS BY STUDENTS AS A WAY TO PROMOTE LEARNING IN THE AREA OF MECHANICS: AN EXPERIENCE IN HIGHER EDUCATION

CREATION OF VIDEOS BY STUDENTS AS A WAY TO PROMOTE LEARNING IN THE AREA OF MECHANICS: AN EXPERIENCE IN HIGHER EDUCATION
SUCCESSFUL TEACHER PREPARATION PROGRAM CHARACTERISTICS

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 50 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0019Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
THINKING THE FUTURE IN THE PRESENT - THE WAY TO A MORE COMPETITIVE AND DIGITAL INDUSTRY

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 3560-3564ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0870Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
REFORMING BUAYA DI BAWAH JAMBATAN ADAB AND MORAL VALUES BOARD GAME BASED ON TEACHERS' FEEDBACK

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 8355-8360ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.2186Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
DEVELOPING AN ONLINE HOME LABORATORY COURSE WHERE STUDENTS MAKE THEIR OWN EQUIPMENT

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 4165 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1011Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
An Architectural Model of a Software Module for Piloting UAV Constellations

Technology behind unmanned aircraft vehicles (UAVs) has been rapidly advancing in the recent years. This propelled the adoption rate and widened the range of UAV uses, such as in sport activities, cartography, surveillance, transportation, and military. While using single, individually controlled UAVs is already very common, using UAV constellations is becoming increasingly popular due to advantages in flexibility, scalability and sensing power. However, the use of UAV constellations remains limited to very specific applications due to number of challenges involved. One of the challenges is the lack of generic software system that would allow controlling UAV constellations in different types of flight missions. In order to address this problem, we propose an architectural model of a software module for UAV constellation piloting.
SPECIAL CITATION INDEX FOR EVALUATION OF SCIENTIFIC WORK TOPICALITY IN THE CONTEXT OF KNOWLEDGE TRANSFER

SPECIAL CITATION INDEX FOR EVALUATION OF SCIENTIFIC WORK TOPICALITY IN THE CONTEXT OF KNOWLEDGE TRANSFER
APPLICATION OF "FLIPPED LEARNING" METHODOLOGY IN CHEMICAL ENGINEERING USING YOUTUBE AS TEACHING TOOL

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 3398-3407ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0830Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Carderock Research Projects (DOD)

Federal Grants & ContractsVolume 47, Issue 1 p. 4-4 Grants Alerts Carderock Research Projects (DOD) First published: 25 November 2022 https://doi.org/10.1002/fgc.32724Read 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 No abstract is available for this article. Volume47, Issue11 December 2022Pages 4-4 RelatedInformation
Robust image watermarking algorithm based on DWT and DCT

To address the current problem of frequent piracy infringement of digital images, this paper proposes an image watermarking algorithm based on DWT and DCT. Firstly, the binary watermark is encrypted by the logistic chaos algorithm, which produces a chaotic sequence with better uncertainty and initial value sensitivity; secondly, the Arnold transform is applied to the encrypted watermarked image to eliminate the correlation between the watermarked pixels, so that the robustness of the image information is improved; the original image is decomposed by the second-level wavelet in the embedding process, and then the low-frequency sub-band LL1 is blocked In the embedding process, the original image is decomposed by the second-level wavelet, and then the low-frequency sub-band LL1 is chunked, and the pre-processed watermark information is embedded by modifying the IF coefficients through DCT transform. In this paper, Matlab simulation experiments are conducted for this algorithm, and the experimental results have good invisibility and good robustness when subjected to various malicious attacks, which is practical for safeguarding digital image copyright.