Reviewerly/exHarmony huggingface dataset:
When frontline robots emerge: the double-edged-sword effect of anticipated trust on intention to switch brands after service failure

Purpose This research aims to investigate the influence of consumers' anticipated trust in service providers on brand switching intention and its underlying psychological mechanism. More importantly, this study explores the moderating role of type of service providers (human staff/humanoid robots/nonhumanoid robots). Design/methodology/approach This study adopted two single-factor between-subjects experimental designs and tested the hypotheses in two typical service failure scenarios: Study 1, a hotel scenario (N = 403); and Study 2, a restaurant scenario (N = 323). Findings The results suggest that there is an inverted U-shaped relationship between consumers' anticipated trust and tolerance of service failure and that such tolerance has a mediating effect on the relationship between anticipated trust and brand switching intention. Moreover, when service failure is caused by a humanoid service robot, a moderate anticipated trust level of consumers is most conducive to increasing tolerance, which in turn reduces their propensity to switch brands. Originality/value This study examines the nature of the relationship between anticipated trust and tolerance in a service failure context, revealing an inverted U-shaped relationship. More importantly, the boundary conditions under which different service provides have an influence on this relationship are incorporated. Finally, this study explores the influence of service failure tolerance on brand switching intentions in a technological context, enriching consumer–brand relationship research.
Integrate and scale: a source of spectrally separable photon pairs

Integrated photonics is a powerful contender in the race for a fault-tolerant quantum computer, claiming to be a platform capable of scaling to the necessary number of qubits. This necessitates the use of high-quality quantum states, which we create here using an all-around high-performing photon source on an integrated photonics platform. We use a photonic molecule architecture and broadband directional couplers to protect against fabrication tolerances and ensure reliable operation. As a result, we simultaneously measure a spectral purity of 99.1±0.1%, a pair generation rate of 4.4±0.1MHzmW −2 , and an intrinsic source heralding efficiency of 94.0±2.9%. We also see a maximum coincidence-to-accidental ratio of 1644±263. We claim over an order of magnitude improvement in the trivariate trade-off among source heralding efficiency, purity, and brightness. Future implementations of the source could achieve in excess of 99% purity and heralding efficiency using the lowest reported propagation losses.
“And then I check to see if it looks legit” – digital critical competence in teacher education

This study investigates pre-service teachers’ understanding and use of their own digital critical competence. In the Norwegian teacher training programmes, pre-service teachers at all levels are required to develop critical reflection skills and learn basic digital skills. They have to be able to communicate digitally and at the same time be able to reflect on how developments in technology entail a growing need for critical assessment of digital media. Through a qualitative approach, we interviewed 17 social studies pre-service teachers at UiT The Arctic University of Norway and asked how they understand and use digital critical competence. Our study shows that the students’ understanding of digital critical competence as a concept in the early stage of their education is mainly linked to source criticism and can largely be classified as a procedural understanding. A procedural approach means acting without any consideration of the underlying intentions; it requires little thought, and cognitive operations are ignored. More experienced pre-service teachers can link digital critical competence more clearly to the teaching profession and the school context and can reflect on didactic perspectives. They have a more norm-critical approach and question how information and knowledge are established. We find that they have developed a more critical and reflexive approach. The implication for teacher education is that digital critical competence should have a space in all subjects, not just social studies. This will help improve the quality of education and equip the pre-service teachers for everyday life as critical and reflexive teachers.
A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation

The stochastic approximation (SA) method stands as the foundational mathematical tool for modern large-scale optimization and machine learning. Therefore, gaining a theoretical understanding of SA algorithms is of fundamental interest. In their paper titled “A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation,” Chen et al. present a unified Lyapunov framework for the finite-sample analysis of a Markovian SA algorithm under a contractive operator with respect to an arbitrary norm. The key novelty lies in the construction of a smooth Lyapunov function called the generalized Moreau envelope. The authors demonstrate the effectiveness of their SA results in the context of reinforcement learning (RL), specifically through popular algorithms such as variants of temporal difference (TD) learning and Q-learning. As byproducts, the results provide theoretical insights into the efficiency of bootstrapping in TD learning with eligibility traces and the bias-variance tradeoff in off-policy learning.
A rural teacher’s transformational journey implementing digital technology

ABSTRACTTeacher learning is a non-linear process that takes place in the context of their practice. As teachers have different roles, moments of learning are created when a deficiency on the part of the teacher or the context is experienced as inhibiting effective teaching and learning. This paper presents the processes of learning when a teacher is confronted by a challenge that affects him and the learners. In rural schools in South Africa, there is a lack of teaching and learning materials, and the impact of such on learning outcomes has been adverse. However, one teacher overcame this situation by using computer simulations. As a result of using this digital teaching and learning tool, an Aha! moment was recognised. The study revealed that computer simulations are curriculum materials that can be used as an interactive source of content and a medium to engage learners and communicate the content. CS presents an opportunity for learners to author their own descriptions of phenomena- an epistemic practice that supports English language learners. Through reflexivity, the teacher adopted a subjectivist view which questions the independent existence of reality and the researcher’s role in researching it. The insights gained in this study have implications for the practice of teachers in rural areas. Moments of learning are created in the context of the practice of teachers. Teachers need to embrace Aha! moments as opportunities for learning and developing the contextual knowledge of teaching to address local concerns.KEYWORDS: Reflectionruralteachinglearningtechnology Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. South African’s government schools are divided into five quintiles based on the socio-economic status of the area where they are situated. Schools in quintiles 1–3 (low quintile) provide free access to primary and secondary education. Schools in quintiles 1–2 are usually located in urban areas, and parents have to pay school fees.2. A pacesetter is document that set and pace how the content is to be taught and the time/period to cover that content.Additional informationFundingThis work was self-supported.Notes on contributorsMaxwell TsokaMaxwell Tsoka (D.Ed.) is a science teacher and educational researcher with research interests in science education, rural education, and the use of digital technology in rural schools. He was a high school science teacher for twenty years before moving to the University where he teaches science methodology courses in the Department of Curriculum and Professional Studies at the University of Venda
Practice-oriented education as a response to the challenges of modern world

The modern world is a world that requires us to have a certain way of thinking, a non-standard approach to solving problems that arise in connection with digitalization and dynamic technological changes. The in-demand intelligence of today combines proven methods and strategies with new ways to apply them and creative ways to build them. In order for education to ensure the formation of such qualities in students, it is necessary to look for training formats that would focus on adaptability, flexibility of thinking, creativity, problem-centeredness, etc. Within the framework of the proposed article, a model of practice-oriented education is described, implemented at the D. Serikbaev East Kazakhstan Technical University (hereinafter EKTU). The purpose of the article is to highlight and justify the importance of the process of modernization of vocational education through the introduction of a practiceoriented concept of education. Practice-oriented education is interpreted as the interaction of the university and its partners for the maximum development of professional competencies and practical experience. Network cooperation with enterprises of the region and the country, on the one hand, and with leading universities and research centers, on the other, makes it possible to strengthen the activities of educational programs. One of the ways to introduce such a format of education at the university is highlighted. The level system of its implementation is also described, which consists in the phased formation of practical competencies among graduate school students and students throughout their studies at the university. The creation of a digital profile of a student’s competencies, as a result of the acquired and demonstrated competencies, is part of the implementation of such a level education. The paper also considers the concepts of SPOD-, VUCAand BANI worlds, the challenges and requirements of which determine the reformatting of the educational paradigm.
Stochastic Localization Methods for Convex Discrete Optimization via Simulation

Solving Convex Discrete Optimization via Simulation via Stochastic Localization Algorithms Many decision-making problems in operations research and management science require the optimization of large-scale complex stochastic systems. For a number of applications, the objective function exhibits convexity in the discrete decision variables or the problem can be transformed into a convex one. In “Stochastic Localization Methods for Convex Discrete Optimization via Simulation,” Zhang, Zheng, and Lavaei propose provably efficient simulation-optimization algorithms for general large-scale convex discrete optimization via simulation problems. By utilizing the convex structure and the idea of localization and cutting-plane methods, the developed stochastic localization algorithms demonstrate a polynomial dependence on the dimension and scale of the decision space. In addition, the simulation cost is upper bounded by a value that is independent of the objective function. The stochastic localization methods also exhibit a superior numerical performance compared with existing algorithms.
Performance of Genetic Algorithms in the Context of Software Model Refactoring

Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., $$\texttt{PESA2}$$ seems to be the fastest one, while $$\texttt{NSGAII}$$ shows the least memory usage).
Workflow Characterization of a Big Data System Model for Healthcare Through Multiformalism

The development of technologies such as cloud computing, IoT, and social networks caused the amount of data generated daily to grow at an incredible rate, giving birth to the trend of Big Data. Big data has emerged in the healthcare field, thanks to the introduction of new tools producing massive amounts of structured and unstructured data. For this reason, medical institutions are moving towards a data-based healthcare, with the goal of leveraging this data to support clinical decision-making through suitable information systems. This comes with the need to evaluate their performance. One of the techniques commonly used is modeling, which consists in performing an evaluation of a model of the system under analysis, without actually implementing it. However, to make an adequate performance assessment of Big Data systems, we need a diversity of volumes and speeds that, due to the sensitivity of data concerning healthcare, is not available. While in other fields this problem is usually solved through the use of synthetic data generators, in healthcare these are few and not specialized in performance evaluation. Therefore, this work focuses on the creation of a synthetic data generator for evaluating the performance of a Big Data system model for healthcare. The dataset used as a reference for creating the generator is MIMIC-III, which contains the digital health records of thousands of patients collected over a time span of multiple years. First, we perform an analysis of the dataset, adopting multiple distribution fitting techniques (e.g., phase-type fitting) to model the temporal distribution of the data. Then, we develop a generator structured as a multi-module library to allow the customization of each component, specifically we propose a multiformalism model to reproduce the patient behavior inside the hospital. Finally, we test the generator by evaluating the performance in different scenarios. Through these experiments, we show the granular control that the generator offers over the synthetic data produced, and the simplicity with which it can be adapted to different uses.
Execution Time Prediction Model that Considers Dynamic Allocation of Spark Executors

We propose a deterministic analytical model that considers dynamic allocation of spark executors while predicting execution time of spark applications. Our new model uses idle time and backlog time metrics to determine whether to add or remove executors. Following the update of executors, this model traverses every stage of a direct acyclic graph using a graph traversal algorithm. We repeat this process until the total execution time of the spark application is calculated. We validate our model against the measured execution time for Query-52 and K-Means workloads that reveal error rates of 4.96% and 4.74%, respectively. A comparison of our model to four classic machine learning models indicates that it is more effective than linear regression, neural networks, decision trees, and random forest. To the best of our knowledge, this is the first deterministic analytical model that accounts for dynamic allocation of executors.
MONCHi: MONitoring for Cloud-native Hyperconnected Islands

Network performance monitoring is a crucial aspect in order to maintain reliable and efficient communications between different hosts and clusters. This is becoming more relevant as companies are progressively moving towards cloud-native environments, where hyperconnected islands are deployed. While monitoring for individual clusters and components is widely deployed, inter-domain metrics have not been yet added to present solutions. In this paper, we present MONCHi, an open-source based custom monitoring tool designed to collect and analyse traditional and custom metrics for network links within and among Kubernetes clusters. MONCHi consists on the flexible design and implementation on top of a conventional monitoring tool –Prometheus in this case– of several custom scripts that run in separate containers within a single pod and continuously collect and store metrics. These metrics are then exposed and visualised in an analysis tool, allowing for easy monitoring of network performance in clusters and for scalable multi-domain deployments based on multiple Prometheus instances. We also discuss the modification of Prometheus configuration to support the new endpoint for MONCHi. Finally, we present the results of several performance tests, focusing mainly on Round-Trip Time (RTT) and bandwidth, conducted to validate the effectiveness of MONCHi to accurately collect and analyse network performance metrics. Overall, MONCHi provides an effective and easy to customise solution for monitoring cloud-native multi-domain environments.
Choropleth maps can convey absolute magnitude through the range of the accompanying colour legend

Data visualisation software provides the ability to create highly customisable choropleth maps. This presents an abundance of design choices. The colour legend, one particular aspect of choropleth map design, has the potential to effectively convey data points’ absolute magnitudes (how large or small they are). Colour legends present the mapping between a specific range of colours and a specific range of numerical values. In this experiment, we demonstrate that manipulating this range affects interpretations of the plotted values’ absolute magnitudes. Participants (N = 100) judged the urgency of addressing pollution levels as greater when the colour legend’s upper bound was equal to the maximum plotted value, compared to when it was significantly larger than the maximum plotted value. This provides insight into the cognitive processing of plotted data in choropleth maps that are designed to promote inferences about overall magnitude.
A State-Size Inclusive Approach to Optimizing Stream Processing Applications

In stream processing applications, accurately measuring a system’s processing capacity is critical for ensuring optimal performance and meeting Service Level Objectives (SLOs). Traditionally, operator throughput has been used as a proxy for the application’s state size, but this approach can be misleading when dealing with window-based applications. In this paper, we explore the impact of window selectivity on the performance of streaming applications, demonstrating how a growing application state can artificially decrease the operators’ throughput, resulting in false positives that could trigger premature scaling-down decisions. To address this problem, we conduct empirical evaluations to assess the relationship between operators’ throughput and state size, showcasing the state size pattern typically does not correspond to the operator’s processing rate in window-based applications. Our findings highlight the importance of considering the state size of the application in performance monitoring and decision-making, particularly in the context of window-based applications.
MODERN PERSPECTIVES OF APPLYING THE CONCEPT OF ZERO TRUST IN BUILDING A CORPORATE INFORMATION SECURITY POLICY

Modern businesses have undergone significant changes as a result of digital advances and the recent COVID-19 pandemic. In particular, there has been an increase in the number of employees working remotely, using personal digital devices alongside corporate devices, and the enterprise itself moving business processes to the cloud or using hybrid environments that combine both cloud and on-premises services. Taken together, this leads to increased interaction between devices and services over open networks, creating new risks of cyber-attack. It is this situation that has led to the relevance and direction of this research. The paper analyzes the current state of effectiveness of the application of enterprise information security policy, in particular, identifies the main limitations associated with the difficulty, and sometimes impossibility, to control the behavioral aspects of enterprise employees to comply with the basic provisions of security policy and general information security. The basic principles of the Zero Trust conceptual approach are analyzed and the main advantages of its application in the formation of the security policy as a strategic approach to ensuring the information security of the enterprise in the conditions of dynamic growth of new threats and transformation of modern business are determined. At the same time, it is established that one of the key components of the Zero Trust architecture is the access control system. As a result, forming the prospects of applying the concept of Zero Trust in the construction and implementation of the information security policy, the necessity of conducting an accompanying study of the effectiveness of modern mechanisms of identification/authentication of access subjects was determined.
ELLIPTIC CURVE CRYPTOGRAPHY AND ITS PRACTICAL APPLICATION

Elliptic curves are one of the most promising tools for constructing modern cryptographic algorithms. The security of elliptic curve cryptography is based on the complexity of solving the discrete logarithm problem in the group of points of the elliptic curve over a finite field. Elliptic curve cryptography enables two parties communicating over public channel using elliptic curve encryption and signing algorithms. Elliptic curves allow to achieve the same level of security with small key sizes than other asymmetric cryptographic algorithms. The article describes the mathematical apparatus of elliptic curves used for cryptographic purposes, the basic operations in the group of points of elliptic curves, such as addition of points, doubling of a point, and scalar multiplication of a point by a number are given. The steps and principles of the Diffie-Hellman key exchange algorithm (ECDH) and the digital signature scheme (ECDSA) on elliptic curves are considered. An overview of standards establishing recommendations and requirements for the use of elliptic curves in cryptographic systems is provided. The advantages of elliptic curve cryptography compared to traditional asymmetric algorithms, such as smaller key sizes, computational speed, and efficient use of resources, are analyzed. Potential threats and vulnerabilities of cryptographic algorithms based on elliptic curves are discussed. The main practical application areas of cryptographic algorithms on elliptic curves, including network security, cryptocurrency operations, message exchange, the Internet of Things, and government institutions are investigated. Examples of popular standardized curves (Curve25519, Curve448, secp256k1) that have been tested and recommended by specialized organizations such as NIST are given.