Reviewerly/exHarmony huggingface dataset:
Computation of Multinomial and Factorial Theorems for Cryptography and Machine Learning

Combinatorial techniques with binomial coefficients, factorials and multinomial computation are used as computing algorithm or cryptographic algorithm for the programs development to apply in artificial intelligence and cybersecurity. Methodological advances in combinatorics and mathematics play a vital role in machine learning and cryptology for data analysis and artificial intelligence-based cybersecurity for protection of the computing systems, devices, networks, programs and data from cyber-attacks. In this article, combinatorial geometric series is discussed more for the application of machine learning and cybersecurity.
CTE 2019 – When cloud technologies ruled the education

This is an introductory text to a collection of papers from the CTE 2019: The 7th Workshop on Cloud Technologies in Education, which was held in Kryvyi Rih, Ukraine, on the December 20, 2019. It consists of short introduction and some observations about the event and its future.
MOTIF-Driven Contrastive Learning of Graph Representations

We propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a self-supervised manner so that it can automatically mine motifs from large graph datasets. Our framework achieves state-of-the-art results on various graph-level downstream tasks with few labels, like molecular property prediction.
Towards User-Centric Guidelines for Chatbot Conversational Design

The conversational nature of chatbots poses challenges to designers since their development is different from other software and requires investigating new practices in the context of human-AI interaction and their impact on user experience. Therefore, this work aims to unveil text-based chatbot conversational practices alongside their impacts on users to build a web guide to support designers while conceiving conversations for chatbots. We have carried out a Systematic Literature Review (SLR) to identify linguistic, visual, and interactive elements used in text-based chatbot conversational design. The SLR resulted in 40 selected studies that were reviewed and coded into a set of conversational guidelines that were evaluated through a survey. Respondents strongly agreed that applying the proposed guidelines in chatbot development would produce greater user satisfaction and user engagement and that the guide is usable, flexible, clear, and understandable, making it a great ally in building text-based chatbots with an improved user experience.
Integrating mineralogy, geochemistry and aeromagnetic data for detecting Fe–Ti ore deposits bearing layered mafic intrusion, Akab El-Negum, Eastern Desert, Egypt

This study delineated the Fe-Ti oxide deposit concurrencies on the layered mafic intrusion in Gabal Akab El-Negum (GAN), South Eastern Desert, Egypt, using aeromagnetic mapping and chemical analysis of the hosted mafic rocks and mineralogical studies. Aeromagnetic data was improved using the enhanced horizontal gradient amplitudeto detect the primary structures (edges/contacts/faults) that control the distribution of Fe-Ti ore deposit. GAN layered gabbros are differentiated into troctolite, olivine-, pyroxene-, and hornblende-gabbros. These mafic rocks primarily comprise plagioclase, olivine, pyroxene, and hornblende with Fe-Ti ores (magnetite and ilmenite). The significant variation in Mg# of clinopyroxene between 0.70 and 0.82 indicates the importance of fractional crystallization in developing layered mafic intrusion. Clinopyroxene and plagioclase thermometry yielded low temperatures similar to the fractionated primary basaltic magma. The pairs of magnetite-ilmenite minerals in gabbros provide equilibrium temperatures of 539.44-815.56, and high fO2, reflecting various cooling and subsolidus reequilibration phases of minerals. The enrichment of GAN gabbros in light rare-earth elements relative to heavy rare-earth elements indicates the interaction between the Fe-Ti rich mantle and the fractionated tholeiitic magmas in the back-arc setting, generating Fe-Ti oxide ores.
Continuance intention to use artificial intelligence personal assistant: type, gender, and use experience

This study aims to identify the key predictors that affect the continuance intention of artificial intelligence personal assistant (AIPA). It proposes the theoretical framework which employs utilitarian value, hedonic value, perceived ease of use, perceived usefulness, novelty value, perceived enjoyment, and parasocial interaction. Data was collected from 257 users of artificial intelligence personal assistants. This study used partial least squares structural equation modeling (PLS-SEM) to analyze the empirical data. The results show that utilitarian value and hedonic value are significantly correlated with continuance intention. The findings reveal that perceived ease of use, perceived usefulness, novelty value have a significant effect on utilitarian value. The analysis results indicate that novelty value, perceived enjoyment, and parasocial interaction are significantly associated with hedonic value. The current study conducted multi-group analysis according to the AIPA type, gender, and use experience. The results of this study will be a useful guideline for research and business on AIPA.
Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.
Impact of virtual reality use on the teaching and learning of vectors

The use of virtual reality in education has enabled the possibility of representing abstract concepts and virtually manipulating them, providing a suitable platform for understanding mathematical concepts and their relation with the physical world. In this contribution, we present a study that aims to evaluate the students’ experience using a virtual reality (VR) tool and their learning of three-dimensional vectors in an introductory physics university course. We followed an experimental research design, with a control and an experimental group, for measuring students’ performance in a pre-post 3D vectors questionnaire. We surveyed the experimental group about their perception of VR use regarding their learning objectives, their experience using VR as a learning tool during the sessions, and the value of using VR in class. We found that on the items in which visualization was important, students in the experimental group outperformed the students in the control group. Students evaluated the VR tool as having a positive impact on their course contents learning and as a valuable tool to enhance their learning experience. We identified four hierarchical categories in which students perceived the use of virtual reality helped them learn the course contents: Visualization, 3D Visualization, Identification, and Understanding. Overall, this study’s findings contribute to the knowledge of using virtual reality for education at the university level. We encourage university instructors to think about incorporating VR in their classes.
Teaching Online: Lessons Learned about Methodological Strategies in Postgraduate Studies

The objective of the present study was to analyze the methodological strategies used by postgraduate faculty from four international education faculties during the transition from face-to-face classes to virtual ones. Through the use of a mixed methodology, a questionnaire with Likert-type, multiple, alternative, and open-ended responses was designed. The results showed that during the transition from face-to-face to virtual classes, the most-utilized teaching activities were collaborative work, followed by lectures through videoconferences. Almost all the faculty modified their instruction methods, highlighting teaching activities, followed by evaluation activities. The study concludes by pointing out the lessons learned during the transition from face-to-face teaching to virtual teaching. We believe that universities must take advantage of the push towards virtual teaching made possible by the pandemic, to continue opting for the new technologies in Higher Education to face future emergency situations with resilience.
Underwater image dehazing using global color features

Accurate underwater imaging is a challenging task. Unlike regular photography, visibility underwater is drastically hazed by water molecules and suspended particles distorting light rays and causing differential absorption of different wavelengths of color. Existing research produces well-adjusted but not perfect results. In this work, we present a novel method that dehazed underwater images by estimating global background light based on the optimal eigenvalues of a matrix constructed from three components: (1) gradient of the pairwise wavelength of color channels (blue–red (br), blue–green (bg), and green–red (gr)) (2) gradient of the wavelength of color channels, and (3) the color channels themselves. We estimate transmission maps via scene depth by exploiting the difference in absorption of the different color channel wavelengths. The proposed technique is executed by augmenting UWCNN (Under-Water CNN) with graph-cut theory. The resultant dehazed images successfully show greatly improved color. The proposed results outperform the existing methods in terms of entropy, UIQMnorm, UICM, UISM, and UCIQE. These improvements will provide stronger imaging tools to domains like submarine search and rescue, navigation, oceanography, and mapping. There is room for future research because our process slightly darkens images which leads to marginally lower UIConM values. Future study can also further evaluate the effect of color channels while assuming pixels with the highest intensity are used as the global ambient light.
A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis

Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature.
Decentralized Finance (DeFi) Projects: A Study of Key Performance Indicators in Terms of DeFi Protocols’ Valuations

Decentralized finance (DeFi) protocols use blockchain-based tools to mimic banking, investment and trading solutions and provide a viable framework that creates incentives and conditions for the development of an alternative financial services market. In this respect, they can be seen as alternative financial vehicles that mitigate portfolio risk, which is particularly important at a time of increasing uncertainty in financial markets. In particular, some DeFi protocols offer an automated, low-risk way to generate returns through a “delta-neutral” trading strategy that reduces volatility. The main financial operations of DeFi protocols are implemented using appropriate algorithms, but unlike traditional finance, where issues of value and valuation are commonplace, DeFis lack a similar value-based analysis. The aim of this study is to evaluate relevant DeFi performance metrics related to the valuations of these protocols through a thorough analysis based on various scientific methods and to show what influences the valuations of these protocols. More specifically, the study identifies how DeFi protocol valuations depend on the total value locked and other performance variables, such as protocol revenue, total revenue, gross merchandise volume and inflation factor, and assesses these relationships. The study analyzes the valuations of 30 selected protocols representing three different classes of DeFi (i.e., decentralized exchanges, lending protocols and asset management) in relation to their respective performance measures. The analysis presented in the article is quantitative in nature and relies on Granger causality tests as well as the results of a fixed effects panel regression model. The results show that the valuations of DeFi protocols depend to some extent on the performance measures of these protocols under study, although the magnitude of the relationships and their directions differ for the different variables. The Granger causality test could not confirm that future DeFi protocol valuations can be effectively predicted by the TVLs of these protocols, while other directions of causality (one-way and two-way) were confirmed, e.g., a two-way causal relationship between DeFi protocol valuations and gross merchandise volume, which turned out to be the only variable that Granger-causes future DeFi protocol valuations.
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data

How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple trials or subjects. For a behavioral assessment with multiple ratings, one may want to identify an aggregate score that is reliably reproduced across raters. Correlated Components Analysis (CorrCA) addresses this problem by identifying components that are maximally correlated between repetitions (e.g. trials, subjects, raters). Here we formalize this as the maximization of the ratio of between-repetition to within-repetition covariance. We show that this criterion maximizes repeat-reliability, defined as mean over variance across repeats, and that it leads to CorrCA or to multi-set Canonical Correlation Analysis, depending on the constraints. Surprisingly, we also find that CorrCA is equivalent to Linear Discriminant Analysis for zero-mean signals, which provides an unexpected link between classic concepts of multivariate analysis. We present an exact parametric test of statistical significance based on the F-statistic for normally distributed independent samples, and present and validate shuffle statistics for the case of dependent samples. Regularization and extension to non-linear mappings using kernels are also presented. The algorithms are demonstrated on a series of data analysis applications, and we provide all code and data required to reproduce the results.
Exponential concentration and untrainability in quantum kernel methods

Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model's parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from a quantum hardware. In this work we study the trainability of quantum kernels from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value, leading to an exponential scaling of the number of measurements required for successful training. We identify four sources that can lead to concentration including: the expressibility of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation and the trainability of quantum kernel methods.
Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach

Background: This study presents a comprehensive review of blockchain technology with a sustainability orientation in supply chains and logistics. Methods: The publications are extracted from the Scopus and Web of Science databases, comprising 552 publications between 2017 and 2022. Several bibliometric laws and techniques, namely three-field analysis, Bradford’s Law, Lotka’s Law, and thematic maps, are applied in R with the bibliometrix package. Content analysis is also carried out based on 185 publications to appreciate the industry-based view of the field. Results: The bibliometric results indicate that this field is on the rise. Authors, sources, affiliations, countries, keywords, and their relationships are also addressed. The findings of the content analysis and thematic maps reveal that some of the most highlighted themes in the literature include traceability, COVID-19, the internet of things, and Industry 4.0. The most popular industry in this field is discovered to be food and agriculture. Conclusions: This paper contributes to the still relatively scarce literature on how blockchain technology fosters sustainable supply chains and logistics, providing a closer look at blockchain use, methodologies, and future directions for different industries concerning food, agriculture, fashion, textile and apparel, manufacturing, automotive, maritime and shipping, healthcare and pharmaceutical, mining and mineral, and energy.
Current and Prospects of Waste Heat Utilization and Cooling Technology in Data Centers

As the COVID-19 pandemic and interest in the virtual world rapidly increase, the data usage in the current society is rapidly increasing to an unprecedented level, and the need for expansion of data centers to handle it is also rapidly increasing. Data centers operate continuously and consume a lot of power in operation. As electricity consumption increases, the increase in greenhouse gas emissions adversely affects the environment, so the increase in data centers inevitably affects the climate environment. Typical technologies that can reduce power consumption in data centers are waste heat energy utilization and outdoor air conditioning. By analyzing the cases of major countries for the two technologies, the technological direction beneficial to Korea was reviewed. As Korea's outdoor air conditioning standards are relaxed, the number of outdoor air conditioning facilities in Korea is increasing. However, the Korean government's practical support for waste heat energy utilization is extremely limited. By analyzing policy cases for waste heat energy utilization in data centers in other countries, policies for revitalizing waste heat energy utilization in Korean data centers were proposed. Furthermore, several ways to reduce the amount of power in the data center were explored and proposed.
Novel Predictive Control and Monitoring System based on IoT for Evaluating Industrial Safety Measures

In this paper, the Accident Reduction Model (ARM) technique has been used to analyze different critical criteria in various industries. This ARM technique is used to determine the conclusions of the decision-making process. Valid data is obtained in the structure of the IoT with proper and consistent and useful information. The network address utility allows efficient sensor data. The necessary configuration procedure effectively monitors relevant sensor boundary values. Finally, we have ensured that the system will be able to provide dynamic performance in an IoT-based use of low-cost estimates and lower execution time.
A novel whale optimisation algorithm with filtering disturbance and nonlinear step

As a recent addition of population-based metaheuristic algorithms, whale optimisation algorithm (WOA) has attracted a lot of attention recently. However, WOA still has room for improvement in the accuracy and reliability of solution. In this study, a novel WOA with filtering disturbance and nonlinear step (FDNS-WOA) is proposed. Firstly, to enhance the population diversity and the global search ability, we design a weighted Cauchy mutation equation to perturb solution space. Secondly, the excessive disturbance may cause the imbalance between exploration and exploitation, and the quality of solutions will be decreased. Thus, before the disturbance, we propose a filtering mechanism to dynamically select different individuals for disturbance at different evolutionary stages. Thirdly, a weighted local walk mechanism is designed to improve the local exploitation capability. Different from original linear step with large fluctuation, we propose a nonlinear step to reduce blind spots in the local exploitation process. Finally, the proposed FDNS-WOA is tested on benchmark functions and applied in a real world problem. The experimental results show that the proposed FDNS-WOA not only outperforms other recent metaheuristic algorithms in term of accuracy and reliability for most benchmark functions, but also achieves satisfactory results in real world problem.