Reviewerly/exHarmony huggingface dataset:
Using Grammar-Based Genetic Programming for Mining Subsumption Axioms Involving Complex Class Expressions

Ontology enrichment is a key task in the area of the Semantic Web. It allows directly to enrich the links between entities of the semantic Web and thus adding information. Our research area is part of this objective with the search for axioms using an evolutionary process. We propose an adaptation resulting from the coupling between evolutionary algorithms and the theory of possibilities in order to allow the research of axioms of subsumption composed of complex classes.
Evaluation of the Pink Luminous Breast LED-Based Technology Device as a Screening Tool for the Early Detection of Breast Abnormalities

Breast cancer is the leading cause of sex-specific female cancer deaths in the United States. Detection at earlier stages contributes to decreasing the mortality rate. The mammogram is the “Gold Standard” for breast cancer screening with an estimated sensitivity of 86.9% and a specificity of 88.9%. However, these values are negatively affected by the breast density considered a risk factor for developing breast cancer. Herein, we validate the novel LED-based medical device Pink Luminous Breast (PLB) by comparison with the mammogram using a double blinded approach. The PLB works by emitting a LED red light with a harmless spectrum of 640–800 nanometers. This allows the observation of abnormalities represented by dark or shadow areas. In this study, we evaluated the sensitivity and specificity of the PLB device as a screening tool for the early detection of breast abnormalities. Our results show that the PLB device has a high sensitivity (89.6%) and specificity (96.4%) for detecting breast abnormalities comparable to the adjusted mammogram values: 86.3 and 68.9%, respectively. The percentage of presence of breast density was 78.2% using PLB vs. 72.9% with the mammogram. Even with higher findings of breast density, the PLB is still capable of detecting 9.4% of calcifications compared to 6.2% in mammogram results and the reported findings for cysts, masses, or tumor-like abnormalities was higher using the PLB (6.5%) than the mammogram (5.6%). A 100% of the participants felt comfortable using the device without feeling pain or discomfort during the examination with 100% acceptability. The PLB positive validation shows its potential for routine breast screening at non-clinical settings. The PLB provides a rapid, non-invasive, portable, and easy-to-use tool for breast screening that can complement the home-based breast self-examination technique or the clinical breast examination. In addition, the PLB can be conveniently used for screening breasts with surgical implants. PLB provides an accessible and painless breast cancer screening tool. The PLB use is not intended to replace the mammogram for breast screening but rather to use it as an adjunct or complemental tool as part of more efficient earlier detection strategies contributing to decrease mortality rates.
Multi-heuristic scheduling methods for workflow in credit cloud

Abstract In the credit cloud, credit services are sold to applications for credit computing, credit fusion and credit risk estimates. Plenty of services with different performance for the same task may have different execution time and charged by various ways. The users have specific requirements for the workflow completion time or cost. Hence, to meet the user’s satisfaction is an important challenge. In this paper, we propose heuristic scheduling methods for credit workflow with total cost minimization, and the deadline should be satisfied. The problem can be divided into two sub-problems, task-mode mapping and task tabling on renting service instances. For the task-mode mapping problem, a recursive heuristic method is constructed to select appropriate service for each task of the workflow. Then another heuristic algorithm based is established to get a final schema with deadline constraint. We discussed the service instance rented in shareable manner and compared with un-shareable manner. Three renting strategies are discussed in detail. Experimental results show the effectiveness and efficiency of the proposed algorithm.
Modeling and Computing Available Rights for Algorithmic Licensing of Movies based on Blockchain

We consider the challenge of determining which rights are available for licensing within the context of algorithmic licensing of movies using blockchain technology. We model the space of rights using set theory. We define algorithms within our modelling context that implement basic transactions for our use case, and argue for their correctness. We evaluate our algorithms through experiments within a multi-node Hyperledger Fabric network, and establish the feasibility of our algorithms for the intended use case.
Data Science: Concern for Credit Card Scam with Artificial Intelligence

In nowadays time we are on the specific education to a cashless society. Similarly, the use of credit card in developing international locations has grown to be a not unusual scenario. The world goes to be digital by means of making the availability of services to peoples together with credit score playing cards, ATM, Internet banking, and mobile banking services. With the rapid increase of the e-trade or online business the use of credit card is so common as it makes the payment easier and saves a time. There are lots of blessings of using the credit card system such as ease of purchase, preserve purchaser credit score history, protection of Purchases, and so on. As we recognize each coin has two faces so the usage of credit card has hazards too inclusive of credit score card fraud in which the hacker theft the credit card info and the misuse the credit score card by means of stealing the details of the card together with credit score card range, CVV, expiry date, etc. Fraud is intentional hypocrisy for the motive of obtaining economic profits or inflicting losses by means of implicit or express deception. However, Fraud is the violation of public law wherein the scammer profits an unauthorized advantage or causes unlawful damage.
Is It Real? – Learners’ Perceptions on Tele-Immersive 3D Video Technology and Its Further Use in K-12 Education

Current immersive technology applications, such as virtual and augmented reality (VR/AR), are still limited in providing an immersive experience in which a user is perceived in 3 dimensions (3D) as their real self. Furthermore, research on such immersive environments applied to educational contexts is scarce. To investigate the unexplored opportunities of immersive technologies in education, we carried out a trial of our tele-immersive platform (TIP) with elementary school 6th grade learners (12–13-year-olds) working on an environmental study lesson. During our investigations, we applied qualitative analysis of the learners’ focus group interview, researchers’ field notes, and recordings of test sessions, alongside descriptive statistics of questionnaires. Learners expressed feelings of positive surprise and excitement, and felt the technology enabled their participation throughout the lesson. Learners recounted feelings of ‘being there’ inside the environment, and in the company of the teacher. Learners agreed that their sense of physical proximity and presence inside the immersive environment is similar to that of face-to-face interaction with the teacher. With our investigations, the opportunities of this emerging technology towards boosting online educational activities for learners to develop their 21C skills are highlighted. Our findings indicate that the introduced 3D TIP technology has the critical potential to overcome psychological strains due to physical distance in online education.
Conversion of a conventional to a digital lecture under the conditions of Covid19 – an example from a bachelor lecture on finishing, dyeing &amp; printing

Due to the Covid-19 pandemic situation during the years 2020 and 2021, the necessity occurred to convert conventional university lectures into digital ones, to avoid personal contacts between people and by this minimizing the spreading of the disease. Beside lectures, also exams have to be transferred to minimize personal contacts. This paper reports on a lecture in a bachelor course with more than 100 students and its conversion to digital format. The lecture is related to textile finishing processes – especially to dyeing and printing. The experience get during the digital lecture are compared with the results gained during the last decade with the same lecture in conventional style. Main tools of the digital format are video conferences and digital worksheets. Challenging are the contact to students by e-mail and the time management of the students especially during the final digital homework. In conclusion, digital lecture and exam are valuable tools to increase the number of participating students and their success in the exam. These tools could be also used successfully in post-corona times especially for lectures given for experienced students in higher semester.
Pengambilan Keputusan Dalam Menentukan Media Pembelajaran Online Pada Masa Pandemi Menggunakan Metode AHP dan TOPSIS

STIKOM Tunas Bangsa is one of the computer science colleges located in Pematangsiantar City. Due to the Covid-19 Pandemic Period, the teaching and learning process is carried out online at their respective homes by students and lecturers. Sources of data obtained by interview and direct observation to students. The author's goal in this study is to determine the best learning media in the Information Systems Study Program at STIKOM Tunas Bangsa by using a combination of TOPSIS and AHP methods. With the given material variables, What'sApp variables, Classroom variables, and E-Learning variables, Google Meet variables and Zoom variables. The result of this study is the increase in the learning process and understanding of students during the Covid 19 Pandemic
Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning

Transfer learning (TL) techniques can enable effective learning in data scarce domains by allowing one to re-purpose data or scientific knowledge available in relevant source domains for predictive tasks in a target domain of interest. In this Data in Brief article, we present a synthetic dataset for binary classification in the context of Bayesian transfer learning, which can be used for the design and evaluation of TL-based classifiers. For this purpose, we consider numerous combinations of classification settings, based on which we simulate a diverse set of feature-label distributions with varying learning complexity. For each set of model parameters, we provide a pair of target and source datasets that have been jointly sampled from the underlying feature-label distributions in the target and source domains, respectively. For both target and source domains, the data in a given class and domain are normally distributed, where the distributions across domains are related to each other through a joint prior. To ensure the consistency of the classification complexity across the provided datasets, we have controlled the Bayes error such that it is maintained within a range of predefined values that mimic realistic classification scenarios across different relatedness levels. The provided datasets may serve as useful resources for designing and benchmarking transfer learning schemes for binary classification as well as the estimation of classification error.
Digitization of the Serbian folk proverbs compiled by Vuk S. Karadžić

This paper aims to present the digitization process of a very important piece of Serbian intangible cultural heritage, Српске народне пословице и друге различне као оне у обичај узете ријечи (Engl. Serbian folk proverbs and other common expressions and phrases), compiled by Vuk Stefanović Karadžić during the first half of the 19th century. In the paper, we discuss the necessary steps in the digitization process, the challenges we had to deal with as well as the solutions we came up with. The goal of this process is to have a fully digitized, user-friendly version of Serbian folk proverbs, that will also easily integrate and be compatible with other digitized resources and/or multi-dictionary portals.
Do Information Security Breach and Its Factors Have a Long-Run Competitive Effect on Breached Firms’ Equity Risk?

A breach in information security (infosec) can materially impact a firm’s long-term competitiveness. For publicly listed firms, an infosec breach can have a long-lasting effect on their competitive stock performance, including their equity risk. Despite its significance, past research has focused primarily on examining the short-term effect of infosec breaches while ignoring its long-term effect on the firm’s equity risk. Therefore, in this research, we examined the long-run effect of 276 infosec breaches at publicly traded firms on equity risk from 2009 to 2018. We analyzed each firm’s equity risk compared to its competitive control firms of similar sizes and performances for three years, from one year before to two years after the breach, using a one-to-one matching methodology. The univariate analysis of infosec breaches on equity risk indicated that breach firms have a 7% higher equity risk than competitive control firms. Additionally, the quantile regression analysis of the effect of infosec breach factors on long-run equity risk showed that the rise in equity risk is higher if the breach involves the compromise of confidential information and is a repeat breach for the same firm. The findings provide a valuable resource for investors, managers, and researchers interested in understanding the long-term relationship between infosec breaches and a firm’s stock competitiveness.
Generic logical encoding for argumentation

Abstract Argumentation has become an essential tool in AI, allowing the representation of knowledge with only a set of arguments and a binary relation between arguments, the attack relation. Since the seminal work presented in 1995 by Dung, many extensions have been proposed in order to enrich this very simple framework by the addition of a second type of interaction, the support relation, and by the use of higher-order or collective interactions. In parallel, several links have been exhibited and studied between Logics and Abstract Argumentation, showing that these two domains are closely interrelated. Among these works, one can find several logical encodings of abstract argumentation frameworks. Nevertheless, these encodings are very often built ad hoc and only for some given frameworks. The aim of this paper is to propose a generic logical encoding covering several families of abstract argumentation frameworks, from Dung’s framework to the most enriched framework using higher-order and collective evidential supports and attacks.
THE EFFECTS OF VOCABULARY AND GRAMMAR MASTERY ON STUDENTS’ WRITING SKILL IN DESCRIPTIVE TEXT (Surveys at Private Junior High Schools in Serang Banten)

This research is aimed to get emperical data and analyze the effects of vocabulary and grammar mastery on writing skill in descriptive text at private junior high schools in Serang Banten. This research is done by Kolmogorov-Smirnov method, ANOVA Table, Proportional Correct for testing the item difficulties, by biserial coefficient, and by Kuder Richarsson testing. The research was held at Privates Junior High Schools in Serang Banten. Data collective done by giving test for writing skill (make a composition or an essay about an object description), test for vocabulary mastery (multiplechoice/ 35 items), and test for grammar mastery (multiplechoice/ 35 items) given to class at MTs Al Ma’arif, MTs Nurul Basmallah and MTs Nurul Huda. The result of study identifies there are effects between Vocabulary and Grammar Mastery towards Student’s Writing Skill in Descriptive Text (Fo = 18.748 and Sig = 0,000 &lt; 0,05; coefficient of 0.666 and the coefficient determination of 81.5%). There is effect of Vocabulary Mastery towards Student’s Writing Skill in Descriptive Text (Sig = 0.026 &lt; 0,05 and ttest = 2,301; while ttable = 1,88). There is effect of Grammar Mastery towards writing skill in Descriptive Text (Sig = 0.002 &lt; 0,05 and ttest = 3,656 while ttable = 1,88).
NMR Hamiltonian as an effective Hamiltonian to generate Schrödinger’s cat states

This report experimentally demonstrates that the theoretical background of the atom–field scenario points out that the NMR quadrupolar Hamiltonian works as an effective Hamiltonian to generate Schrödinger’s cat states in a $$2I+1$$ low-dimensional Hilbert space. The versatility of this nuclear spin setup is verified by monitoring the $$^{23}$$ Na nucleus of a lyotropic liquid crystal sample at the nematic phase. The quantum state tomography and the Wigner quasiprobability distribution function are performed to characterize the accuracy of the experimental implementation.
The Smart Sailing Robot for Navigational Investigation is Used to Explore all the Details on the Zone of the Water Pura

Ocean Exploration and Navigational Research is driving undertakings by supporting undertakings with PC vision frameworks have shown potential for Sailboat robots made to make assessments at the surface. The marine environment presents an in each commonsense sense, ideal showing ground for the assessment and improvement of robotized progressions. Robot cruising is a tricky task in both turn of events and controlling the boat consequently it joins a wide degree of orders. The cruising robot researches in comprehension of video film, the ID of cruising features, human-robot correspondence, vehicle control, position assessment and mechanical course of action. Key applications for this vessel are the appraisal of marine living spaces and complex moves. An idea presented has been with a Robotic vehicle what starts normally and truly control the moving thing in the water the robot will get and sends the information to the PC which uses advanced picture managing improvement and investigates appropriate pictures by seeing cut down features which will follow the article present in the outside of ocean. The DC motors are used to turn the arms of the robot to get living spaces.
Understanding the Challenges Faced by Hospitality Managers during COVID-19 Pandemic in Malaysia

The COVID-19 pandemic severely hit the hospitality industry in Malaysia. The Malaysian government implemented restriction orders and preventative measures, which had impacted business operations. This study aims to explore the challenges faced by hospitality managers during the early phase of the COVID-19 in Malaysia. Using qualitative purposive sampling, researchers interviewed 12 managers from the hotel and restaurant sectors. Based on the content analysis, the respondents reported several challenges, including generating revenues, controlling costs, and managing the workforce. Findings contributed significantly to various stakeholders such as the Malaysian government and the hospitality providers. Limitations and future recommendations are discussed. Keywords: Hospitality; COVID-19; Malaysia; Pandemic eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i19.3213
Real-time road object segmentation using improved light-weight convolutional neural network based on 3D LiDAR point cloud

It is critical that autonomous navigation systems can segment the objects captured by their sensors (cameras or LiDAR scanners) in real-time. A convolutional neural networks (CNN) is proposed for real-time semantic segmentation of road objects (pedestrians, cars, cyclists) in this paper. The proposed network structure is based on the light-weight network SqueezeNet, which is small enough to be stored directly in the embedded deployment of an autonomous vehicle. The input of the proposed CNN is the transformed 3D LiDAR point cloud, and the domain transform (DT) makes the segmentation object precisely align its boundary, which results in the preferable point-wise label map as the output. In addition to comparing our segmentation results with the pipelines based on deep learning, the visual comparison with the traditional 3D point cloud segmentation pipelines is also made. Experiments show that the proposed CNN can achieve faster running time (6.2 ms per frame) and realise real-time semantic segmentation for the objects in autonomous driving scenes while ensuring the comparable segmentation accuracy.
Deep Graph Mutual Learning for Cross-domain Recommendation

Cross-domain recommender systems have been increasingly important for helping users find satisfying items from different domains. However, existing approaches mostly share/map user features among different domains to transfer the knowledge. In fact, user-item interactions can be formulated as a bipartite graph and knowledge transferring through the graph is a more explicit way. Meanwhile, these approaches mostly focus on capturing users’ common interests, overlooking domain-specific preferences. In this paper, we propose a novel Deep Graph Mutual Learning framework (DGML) for cross-domain recommendation. In particular, we first separately construct domain-shared and domain-specific interaction graphs, and develop a parallel graph neural network to extract user preference in corresponding graph. Then the mutual learning procedure uses extracted preferences to form a more comprehensive user preference. Our extensive experiments on two real-world datasets demonstrate significant improvements over state-of-the-art approaches.
Two-stream attention network with local and non-local dependence for referring relationships

Referring relationships is a task aiming to retrieve entities in the image conditioned on the given structured relationship text. Previous works utilise convolution-network-base model to learn image features conditioned on the text and classify the pixels in the image for retrieval. However, convolution network concentrates on the local dependence in the image without considering correlations between pixels far apart. In this paper, a two-stream attention network (TSAN) is proposed to address the above issue. Firstly, cross-modal feature maps are initialised by adding text information to each pixel in the visual feature maps. Then, TSAN designs attention mechanism for the local and non-local dependence in the cross-modal features to obtain two probability maps. Finally, the network integrates the two probability maps to predict the localisation probability. Experiments are conducted on three datasets and the results demonstrate that the proposed TSAN achieves better performance compared with several powerful models.