Reviewerly/exHarmony huggingface dataset:
Conceptions and Perceptions of Software Industry Professionals on Team Productivity in Agile Software Development: A Comparative Study

Agile software development (ASD) has generated different benefits in organizations and in the Software Industry, mainly in improving productivity. For ASD teams this indicator plays a fundamental role since it helps determine their performance. However, evaluating productivity is a great challenge and the way in which this concept has been approached in the literature is very limited. The objective of this article is to contrast the conceptions of productivity at the team level from an ASD perspective with the perceptions that professionals in the software industry have. For the methodological design, the notions of team productivity presented in the literature were identified and compared with the perceptions of 72 professionals from the software industry collected through a survey following the protocol proposed by Kitchenham and Pfleeger. The main results show that the concept of team productivity in the literature is associated with a set of dimensions related to satisfaction, delivery of functional software, and knowledge transfer. On the part of the respondents, a perception of general productivity centered on dimensions of customer satisfaction, activity management, and early identification of the problem to be solved is evidenced. It can be concluded that the professionals' imaginaries focus on presenting productivity from a generic perspective and its dimensions do not necessarily involve teamwork.
Estimate Suitable Location of Solar Power Plants Distribution by GIS Spatial Analysis

This study proposes a model for the best investment in renewable energy plants that uses DEM, Spatial Analysis, and analysis of indicator weights by AHP to choose a suitable place to locate the solar plants, which increases their efficiency. This is because renewable energy is the most important component of future sustainability. In addition, the cities of Iraq, including Babylon, have increased the proportion of the population, which has led to high rates of urbanization and a lack of services. In particular, the need for services increased, especially electric power, which is characterized by its inefficiency and insufficiency. Yet, the governorate is a good source of solar energy and regular radiation. Therefore, the trend to use renewable energy is the optimal solution, and this manuscript proposes multiple criteria that can determine the optimal locations for building solar energy farms. So methods of analysis are the Digital Elevation Model (DEM), the slope of the earth, efficient distances from the city center, the main road networks and electricity distribution networks, and average solar brightness (hours/day) quantity. Finally, the spatial analysis of all indicators shows eight sites. By using criteria of analysis based on AHP analysis, the result is that six represent suitable sites chosen as sufficient space to locate solar plants. Consequently, the results of this manuscript for solar energy collection projects show percentages ranging between 2% and 37%, with areas starting with 10 ‎km2 and gradually rising towards the largest proposed area of 155 km2, distributed over the province so that the total proposed areas for solar energy collection projects will be about 422 km2. All that aim to achieve the best service in quality and quantity of renewable energy to establish sustainability and efficiency economic modeling in addition to increasing production efficiency. Doi: 10.28991/CEJ-2023-09-05-013 Full Text: PDF
Efficient stabilizer entropies for quantum computers

Stabilizer entropies (SEs) are measures of nonstabilizerness or `magic' that quantify the degree to which a state is described by stabilizers. SEs are especially interesting due to their connections to scrambling, localization and property testing. However, applications have been limited so far as previously known measurement protocols for SEs scale exponentially with the number of qubits. Here, we show how to efficiently measure SEs for integer index $n>1$ via Bell measurements. We provide efficient bounds of various nonstabilizerness monotones which are intractable to compute beyond a few qubits. Using the IonQ quantum computer, we measure SEs of random Clifford circuits doped with non-Clifford gates and give bounds for the stabilizer fidelity, stabilizer extent and robustness of magic. As applications, we provide efficient algorithms to measure $4n$-point out-of-time-order correlators and multifractal flatness. Our results open up the exploration of nonstabilizerness with quantum computers.
Feasibility and Benchmarking of Post-Quantum Cryptography in the Cooperative ITS Ecosystem

Localized communication between vehicles and their surrounding environment (V2X) is a key technology to enable Cooperative Intelligent Transportation Systems (C-ITS) aiming at road safety, traffic flow and driving comfort. Security services based on Elliptic Curve Cryptography (ECC) for authenticity and confidentiality (mostly application-dependent) have been chosen to meet the hard constraints of low latency safety communications and limited bandwidth radio communication in dense traffic conditions. Due to threats raised by Quantum Computers (QC), the classical asymmetric cryptographic algorithms could be broken impacting the Public Key Infrastructure (PKI)-based security solutions, with negative safety consequences on the (semi)-autonomous vehicles and road users. Our project (TAM: Trusted Autonomous Mobility) [18] is focusing on end-to-end cybersecurity and privacy for innovative services in the field of cooperative, connected and automated mobility (CCAM). One main objective is to find suitable quantum safe schemes to replace the current cryptographic standards based on ECC which are used in V2X communications. After defining the main requirements and key performance indicators for C-ITS, a benchmarking of current NIST pre-standards PQC algorithms was performed to assess the feasibility and performances in C-ITS applications and based on the results a best fit solution is selected.
The Use of Virtual Reality, Augmented Reality, and the Metaverse in Education: The Views of Preservice Biology and Mathematics Teachers

This research aims to analyse the views of preservice biology and mathematics teachers regarding the use of virtual reality (VR), augmented reality (AR), and the metaverse in education. The participants of the research consist of 24 pre-service biology and mathematics teachers. The views of preservice teachers on the use of VR, AR, and the metaverse in education were analysed on the benefits of using VR, AR, and the metaverse in education, with what subjects/modules they can be best associated, and what would be needed to make it most effective. The preservice biology and mathematics teachers demonstrate that the utility of VR, AR, and the metaverse in education is beneficial for increasing the quality of learning and improving the existing teaching methodologies. Preservice teachers believe that these technologies are effective for teaching subjects that require more visual material, such as organs, cells, ecosystems, geometry, and the geometry of space or solid bodies. Moreover, the preservice teachers draw attention to the need for infrastructure and well-informed individuals (students, preservice teachers, teachers) for VR, AR and the metaverse. The results provide value for pre-and in-service training, teacher training and designing the teaching environments for the future.
Controlling the Photon Number Coherence of Solid-state Quantum Light Sources for Quantum Cryptography

Quantum communication networks rely on quantum cryptographic protocols including quantum key distribution (QKD) using single photons. A critical element regarding the security of QKD protocols is the photon number coherence (PNC), i.e. the phase relation between the zero and one-photon Fock state, which critically depends on the excitation scheme. Thus, to obtain flying qubits with the desired properties, optimal pumping schemes for quantum emitters need to be selected. Semiconductor quantum dots generate on-demand single photons with high purity and indistinguishability. Exploiting two-photon excitation of a quantum dot combined with a stimulation pulse, we demonstrate the generation of high-quality single photons with a controllable degree of PNC. Our approach provides a viable route toward secure communication in quantum networks.
The role of private and public regulation in the case study of crypto-assets: The Italian move towards participatory regulation

With the Digital Financial package (MiCA, DLT Pilot, and DORA, later on complemented by the DAC8 proposal) the European Union seeks to establish an appropriate legal framework for crypto-assets showing a financial nature. The package represents a first attempt to regulate a complex and emerging phenomenon, characterised by significant trade-offs. Unsurprisingly, in this early stage of the law-making process several relevant aspects of the crypto environment remain unaddressed, such as pure DeFI models, DAOs, and NFTs. Such regulatory gap is to a large extent attributable to the difficulty of addressing technologically complex issues through command-and-control top-down legislation. The improvements delivered by the Better Regulation Agenda are not enough to solve this conundrum. In this context, the Communication by the Bank of Italy on Decentralised Technology in Finance and Crypto-assets and its first move, the smart-contract MoU, provide an interesting case study to discuss the potential of ‘participatory regulation.’ This experimental form of regulation tries to get the most out of co-regulation, self-regulation, and command-and-control, combining their characters with the view of reconciling the technology neutrality principle with technology-based regulation. Participatory regulation aims to bridge the public and private sector in order to strike a right balance between flexibility and legal certainty, without stifling innovation.
Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Online retailers and merchants increasingly request feedback from their clients on the products they purchase. This has led to a significant increase in the number of product reviews posted online, as more people are making purchases online. The opinions expressed in these customer reviews have a significant impact on other customers' purchase decisions, as they are influenced by other customers' recommendations or complaints. This study used Amazon, a well-known and widely used e-commerce platform, to examine sentiment categorization using several machine learning techniques while analyzing an Amazon Reviews dataset. At first, the reviews were transformed into vector representations using the Bag-of-Words approach. Word cloud was used to illustrate the text data in terms of the frequency they appear in the review. Subsequently, the machine learning methods decision trees and logistic regression were used. The two models used in this study achieved high levels of accuracy in analyzing the dataset. Specifically, the Decision Tree model outperformed the Logistic Regression one, achieving an impressive accuracy of 99% compared to the 94% of the latter.
Model Checking Omega-Regular Hyperproperties with AutoHyperQ

Hyperproperties are commonly used to define information-flow policies and other re- quirements that reason about the relationship between multiple traces in a system. We consider HyperQPTL – a temporal logic for hyperproperties that combines explicit quan- tification over traces with propositional quantification as, e.g., found in quantified proposi- tional temporal logic (QPTL). HyperQPTL therefore truly captures ω-regular relations on multiple traces within a system. As such, HyperQPTL can, e.g., express promptness prop- erties, which state that there exists a common bound on the number of steps up to which an event must have happened. While HyperQPTL has been studied and used in various prior works, thus far, no model-checking tool for it exists. This paper presents AutoHyperQ, a fully-automatic automata-based model checker for HyperQPTL that can cope with arbitrary combinations of trace and propositional quantification. We evaluate AutoHyperQ on a range of benchmarks and, e.g., use it to analyze promptness requirements in a diverse collection of reactive systems. Moreover, we demonstrate that the core of AutoHyperQ can be reused as an effective tool to translate QPTL formulas into ω-automata.
Personal considerations about creativity and artificial intelligence

It presents an original model of human creativity, as well as a series of personal considerations about creativity within Artificial Intelligence (AI). It is believed that when human creativity it will be known, it can be implemented in AI, such as a number of major challenges such as pattern recognition, process simulation, optimal control, and so on.
A novel crossover operator for genetic algorithm: Stas crossover

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.
Fuzzy support vector machine for classification of time series data: A simulation study

Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.
Determinants of behavioral intention to use big data analytics (BDA) on the information and communication technologies (ICT) SMEs in Jordan

Big Data Analytics (BDA) provides an important resource for businesses seeking to enhance their performance and gain a competitive advantage, although not all organizations are adopting BDA techniques, and small and medium-sized enterprises (SMEs) in Jordan have been slow in this regard, despite being key players in any healthy economy, and the fact that BDA adoption can be facilitated by using the Technology Acceptance Model (TAM). The purpose of this study is to investigate the drivers of behavioral intention among managerial-level employees in Jordanian ICT SMEs to adopt BDA through a quantitative correlational research approach. The TAM questionnaire was used to gather data from 271 online survey participants in Jordan using Google Forms. The target group included management level staff working in small and medium-sized ICT firms (SMEs). Confirmatory factor analysis (CFA) was used to evaluate the research instrument's reliability and validity, and structural equation modeling (SEM) was utilized to test the study's hypotheses. The findings revealed that perceived usefulness, perceived ease of use, and perceived “privacy and security” significantly influenced managerial-level employees' behavioral intention to use BDA in their organizations. The research findings also supported the application of TAM, and the results of the investigation indicated that managerial-level employees would be willing to use BDA techniques providing they were perceived to be useful, user-effortless, and posed little concern about privacy and security. Overall, the current study's results demonstrate that the suggested model had good predictive power, 51% of the variance in behavioral intention, and was therefore capable of predicting managers' intentions to use BDA.
K-Hub: A Modular Ontology to Support Document Retrieval and Knowledge Extraction in Industry 5.0

Digitalization is entering the industrial sector and different needs are emerging to support shop floor operators; in particular, they need to retrieve information to support their operations (e.g., during maintenance activities), from structured and unstructured sources, as well as from other people’s experience. Sharing knowledge and making it accessible to industrial workers is therefore a key challenge that Semantic Web technologies are able to address and solve. In this paper, we present a modular ontology that we engineered in order to support the collection, extraction and structuring of relevant information for industrial operators in a “knowledge hub” (K-Hub). In particular, our K-Hub ontology covers several aspects, from document annotation/retrieval to procedure support, from manufacturing domain concepts to company-specific information. We discuss its engineering process, extensibility and availability, as well as its current and future application scenarios to support industrial workers.
Design and Construction of Electric Wheelchair with Mecanum Wheel

This research aimed to design and construct an electric wheelchair with mecanum wheels that can move in any desired direction and speed based on the joystick controller. This represents a significant improvement over traditional electric wheelchairs, which are limited to linear movement in a single direction. The research contribution of this study is the development of an electric wheelchair with mecanum wheels that allows for improved mobility and independence for wheelchair users. The design includes a joystick controller and the use of an average filter to improve the processing of the joystick. This represents a significant improvement over traditional electric wheelchairs, which are limited to linear movement in a single direction. The design and construction of the electric wheelchair followed the ISO 2570-2555 guidelines and utilized Arduino DUE as the main processor for controlling the rotation of the wheels. The gain of speed and angle of the analog joystick were determined using the technique of finding the resultant vector to control the direction and speed of the wheels. The resulting electric wheelchair had a standard structure and was able to move in the desired direction and speed based on the movement of the joystick controller, demonstrating the success of the design and construction in achieving its objective. In conclusion, the development of joystick control for electric wheelchairs is important and allows for the creation of significantly novel and improved designs such as the electric wheelchair with mecanum wheels presented in this research.
Comparing the hardness of MAX 2-SAT problem instances for quantum and classical algorithms

An algorithm for a particular problem may find some instances of the problem easier and others harder to solve, even for a fixed input size. We numerically analyze the relative hardness of MAX 2-SAT problem instances for various continuous-time quantum algorithms and a comparable classical algorithm. This has two motivations: To investigate whether small-sized problem instances, which are commonly used in numerical simulations of quantum algorithms for benchmarking purposes, are a good representation of larger instances in terms of their hardness to solve, and to determine the applicability of continuous-time quantum algorithms in a portfolio approach, where we take advantage of the variation in the hardness of instances between different algorithms by running them in parallel. We find that, while there are correlations in instance hardness between all of the algorithms considered, they appear weak enough that a portfolio approach would likely be desirable in practice. Our results also show a widening range of hardness of randomly generated instances as the problem size is increased, which demonstrates both the difference in the distribution of hardness at small sizes and the value of a portfolio approach that can reduce the number of extremely hard instances. We identify specific weaknesses of these quantum algorithms that can be overcome with a portfolio approach, such their inability to efficiently solve satisfiable instances (which is easy classically).
Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Regulatory and technological changes have recently transformed the digital footprint of credit card transactions, providing at least ten times the amount of data available for fraud detection practices that were previously available for analysis.This newly enhanced dataset challenges the scalability of traditional rule-based fraud detection methods and creates an opportunity for wider adoption of artificial intelligence (AI) techniques.However, the opacity of AI models, combined with the high stakes involved in the finance industry, means practitioners have been slow to adapt.In response, this paper argues for more researchers to engage with investigations into the use of Explainable Artificial Intelligence (XAI) techniques for credit card fraud detection.Firstly, it sheds light on recent regulatory changes which are pivotal in driving the adoption of new machine learning (ML) techniques.Secondly, it examines the operating environment for credit card transactions, an understanding of which is crucial for the ability to operationalise solutions.Finally, it proposes a research agenda comprised of four key areas of investigation for XAI, arguing that further work would contribute towards a step-change in fraud detection practices.