Reviewerly/exHarmony huggingface dataset:
Distance education in higher education in Saudi Arabia in the post-COVID-19 era

COVID-19 has changed the field of education. This paper aimed to re-envision the post-pandemic higher education landscape in Saudi Arabia. This study employed descriptive-qualitative research design. Twenty postgraduates completed a journal task at a public university in the Eastern Province of Saudi Arabia. The responses revealed their first-hand experience of multi-sectorial communities caused by the COVID-19 lockdown, including students, parents and teachers. Results showed that there are seven positive and eight negative impacts of emergency distance education caused by the pandemic lockdown and various themes have emerged, including Social and Educational. To succeed in the post-pandemic era, teachers need to acquire online pedagogical content knowledge and teaching strategies, conduct effective student evaluation and engage students in both virtual and in-person labs and classrooms. This study contributes to quality access of students toward online learning in countries, where distance education is still in its infant stage, such as Saudi Arabia.&#x0D; &#x0D; Keywords: Distance education, post-secondary education, teacher training, teaching strategies, networked learning
Digital competence of a teacher involved in the implementation of a cross-border lesson for classrooms in Brazil and Chile

Purpose The purpose of this study was to examine the digital competence displayed by a primary school teacher who implemented an interdisciplinary cross-border lesson that was designed with the lesson study methodology and involved two countries: Brazil and Chile. Design/methodology/approach A qualitative methodology was adopted via the case study method through which the case of a sixth-grade Chilean teacher participating in the study was documented. The data were collected through a lesson plan, a videotape of the implemented lesson and a questionnaire. A professional digital competence framework for teachers provided an analytical perspective via data analysis. Findings The results show that the teacher displayed digital competence in a synchronous collaborative learning environment in which she had time to reflect on the educational value of technology and appropriate teaching methods involving information and communication technologies. Certain elements of the studied case contributed to this performance, namely the cross-border context, the classroom setting and the collaborative Lesson Study methodology. Practical implications The lesson study methodology facilitated the teacher's performance in the “Pedagogy and didactics” digital competence by enabling her to participate in planning and implementing a lesson that allowed all those who collaborated, including teachers and researchers, to reflect on teaching in a digital learning environment. Social implications The cross-border context, which involved co-designing and implementing a lesson in two countries, allowed the teacher to display her “School in society” and “Ethics” competencies. This was achieved through connecting two classrooms with different languages and cultures digitally and synchronously, thereby providing students the opportunity to debate and participate in a global and local problem such as a country's responsibility for energy consumption. Originality/value Modern society requires the transformation of school practices, and new teaching approaches should include the provision of collaborative spaces that incorporate digital technologies. In this sense, this paper shows that cross-border lessons involving a synchronous learning environment offer a potential alternative, as digital teaching competence enables teachers to bring together different social and cultural groups virtually, thereby contributing to the reduction of social gaps and to the promotion of positive identity among less advantaged students.
Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.
ETKDS: An efficient algorithm of Top-K high utility itemsets mining over data streams under sliding window model

The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDHSW structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets.
Analysis of Different Load Balancing Algorithms in Cloud Computing

In today's world, the IT industry is emerging day by day; therefore, the need for storage and computing is increasing multifold. Cloud computing has transformed the IT sector to much greater heights by virtualizing the systems, thereby reducing cost of the hardware to greater extent. Cloud computing is based on the pay as per use policy. Due to the exponential growth in cloud computing, users demand supplementary services and improved results which makes load balancing a major challenge. Load balancing distributes the workload across multiple nodes to optimize the performance of the system. Various load balancing algorithms exist to provide better resource utilization. This paper gives a brief analysis of load balancing algorithms and also compared these algorithms on the basis of certain metrics like average response time, processing cost, and data servicing time.
Variational State and Parameter Estimation

This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution. The approach is deterministic and results in an optimisation problem of a standard form. Due to the parametrisation of the assumed density selected first and second order derivatives are readily available which allows for efficient solutions. The proposed method is compared against state-of-the-art Hamiltonian Monte Carlo in two numerical examples.
Multipartite entanglement of billions of motional atoms heralded by single photon

Abstract Quantum theory does not prevent entanglement from being created and observed in macroscopic physical systems, in reality however, the accessible scale of entanglement is still very limited due to decoherence effects. Recently, entanglement has been observed among atoms from thousands to millions levels in extremely low-temperature and well isolated systems. Here, we create multipartite entanglement of billions of motional atoms in a quantum memory at room temperature and certify the genuine entanglement via M -separability witness associated with photon statistics. The information contained in a single photon is found strongly correlated with the excitation shared by the motional atoms, which intrinsically address the large system and therefore stimulate the multipartite entanglement. Remarkably, our heralded and quantum memory built-in entanglement generation allows us to directly observe the dynamic evolution of entanglement depth and further to reveal the effects of decoherence. Our results verify the existence of genuine multipartite entanglement among billions of motional atoms at ambient conditions, significantly extending the boundary of the accessible scale of entanglement.
"What Happened Here!?" A Taxonomy for User Interaction with Spatio-Temporal Game Data Visualization

Currently, there is no formal taxonomy for the activities that users engage in when interacting with and making meaning from spatio-temporal game data visualizations. As data visualization, especially spatio-temporal visualization, becomes more popular for game data analytics, it becomes increasingly crucial that we develop a formal understanding of how users, especially players, interact with and extract meaning from game data using these systems. However, existing taxonomies developed for InfoVis are not directly applicable due to domain differences and a lack of consensus within the literature. This paper presents the beginnings of a taxonomy for user interaction with spatio-temporal data specific to the domain of games, developed from the results of a qualitative user study (n=7) in which experienced players were tasked with using a spatio-temporal visualization system to explore and understand telemetry data from Defense of the Ancients 2 (DotA 2). The taxonomy includes seven activities organized into three categories: Data Interaction, Sense Making, and Validation. We discuss the implications of these activities on design and future research.
Assessment of University Students on Online Remote Learning during COVID-19 Pandemic in Korea: An Empirical Study

The COVID-19 pandemic is affecting business, society, and education worldwide. In particular, it is an example of a non-face-to-face approach in commerce and education. As the pandemic has lasted for nearly two years in Korea, online remote learning has been held at universities for four semesters. There are learning management systems (LMSs) and real-time Zoom lectures for the types of online remote learning widely used at Korean universities, and students and professors are adapting with some difficulties due to the unfamiliar lecture environment. Assessment of students’ online remote learning is essential in reinforcing strengths and compensating for weaknesses. This study aimed to investigate the relationship between online remote learning quality (system quality, information quality, and service quality), flow, and learner satisfaction, and the implications are presented. A survey was conducted on 182 university students, and the structural equation model of AMOS 21.0 was used to analyze the research hypothesis. The results of the empirical analysis were as follows. First, system quality, information quality, and service quality had a positive effect on flow. Second, system quality, information quality, and service quality had a positive effect on learner satisfaction. Third, flow had a positive effect on learner satisfaction. It seems that online remote learning in university is becoming a new normal. In conclusion, the implications and limitations of this study are presented.
Is blockchain ready for orthopaedics? A systematic review

The unique attributes of distributed ledger blockchain systems including robust security, immutability, transparency, and decentralisation, make them highly suitable solutions for many healthcare-related problems. This review examines the potential applications for blockchain technology in the field of orthopaedics, by taking a systematic approach to the evolving blockchain literature and mapping potential use cases against the current needs of orthopaedic practice.A literature search was performed using Pubmed, EMBASE, OVID and the Cochrane library with the primary aim of identifying detailed accounts of blockchain solutions and use cases in healthcare. These articles were then reviewed and mapped against current orthopaedic practice to illustrate applications specific to that specialty.One hundred and forty-one papers were identified which described case studies, simulations, or detailed proposals of blockchain solutions in healthcare. Most studies described blockchain solutions at the simulated or prototype testing phase, with only 10 case studies describing blockchains in "real-world" use. The most frequently cited use cases for blockchain technology involved the storage, security and sharing of electronic medical records. Other blockchain solutions focused on the "Internet of Things", research, COVID 19, supply chains and radiology. There were no solutions focusing specifically on orthopaedics. Many of the described blockchain solutions had considerable scope for application in orthopaedic practice however, providing the potential for greater inter-institutional collaboration, cross border data exchange, enhanced patient participation, and more robust and transparent research practices.Blockchain solutions for healthcare are increasing in number and scope and have multiple applications relevant to orthopaedic practice. The orthopaedic community needs to be aware of this innovative and growing field of computer science so that surgeons can leverage the power of blockchain safely for the future of orthopaedics.
Characterizing human random-sequence generation in competitive and non-competitive environments using Lempel–Ziv complexity

The human ability for random-sequence generation (RSG) is limited but improves in a competitive game environment with feedback. However, it remains unclear how random people can be during games and whether RSG during games can improve when explicitly informing people that they must be as random as possible to win the game. Nor is it known whether any such improvement in RSG transfers outside the game environment. To investigate this, we designed a pre/post intervention paradigm around a Rock-Paper-Scissors game followed by a questionnaire. During the game, we manipulated participants' level of awareness of the computer's strategy; they were either (a) not informed of the computer's algorithm or (b) explicitly informed that the computer used patterns in their choice history against them, so they must be maximally random to win. Using a compressibility metric of randomness, our results demonstrate that human RSG can reach levels statistically indistinguishable from computer pseudo-random generators in a competitive-game setting. However, our results also suggest that human RSG cannot be further improved by explicitly informing participants that they need to be random to win. In addition, the higher RSG in the game setting does not transfer outside the game environment. Furthermore, we found that the underrepresentation of long repetitions of the same entry in the series explains up to 29% of the variability in human RSG, and we discuss what might make up the variance left unexplained.
Multi-modal Open World User Identification

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.
Noise Avoidance SMOTE in Ensemble Learning for Imbalanced Data

Class imbalance is a common problem in many real-world applications. To deal with class imbalance, several techniques, including resampling and ensemble approaches, have been proposed and resampling and ensemble methods have been proven effective for imbalanced data. Moreover, hybrid methods that combine resampling and ensemble have been verified to be highly effective in dealing with imbalance problems. this study proposes new hybrid sampling/ensemble algorithms based on a modification of SMOTE, called NASBoost and NASBagging, which avoids selecting noise samples in the minority class while maintaining diversity among training sets. The proposed sampling method introduces new measures to identify samples that may generate noisy synthetic samples during sampling in SMOTE. Experimental results on 16 imbalanced datasets show that the hybrid of the proposed sampling procedure and ensemble algorithms improves the classification performance by preventing the generation of noise while allowing samples in the minority class to be evenly chosen.
UDF to SQL translation through compositional lazy inductive synthesis

Many data processing systems allow SQL queries that call user-defined functions (UDFs) written in conventional programming languages. While such SQL extensions provide convenience and flexibility to users, queries involving UDFs are not as efficient as their pure SQL counterparts that invoke SQL’s highly-optimized built-in functions. Motivated by this problem, we propose a new technique for translating SQL queries with UDFs to pure SQL expressions. Unlike prior work in this space, our method is not based on syntactic rewrite rules and can handle a much more general class of UDFs. At a high-level, our method is based on counterexample-guided inductive synthesis (CEGIS) but employs a novel compositional strategy that decomposes the synthesis task into simpler sub-problems. However, because there is no universal decomposition strategy that works for all UDFs, we propose a novel lazy inductive synthesis approach that generates a sequence of decompositions that correspond to increasingly harder inductive synthesis problems. Because most realistic UDF-to-SQL translation tasks are amenable to a fine-grained decomposition strategy, our lazy inductive synthesis method scales significantly better than traditional CEGIS. We have implemented our proposed technique in a tool called CLIS for optimizing Spark SQL programs containing Scala UDFs. To evaluate CLIS, we manually study 100 randomly selected UDFs and find that 63 of them can be expressed in pure SQL. Our evaluation on these 63 UDFs shows that CLIS can automatically synthesize equivalent SQL expressions in 92% of the cases and that it can solve 2.4× more benchmarks compared to a baseline that does not use our compositional approach. We also show that CLIS yields an average speed-up of 3.5× for individual UDFs and 1.3× to 3.1× in terms of end-to-end application performance.
Advanced robotics as a support in healthcare organizational response: A COVID-19 pandemic case study

The use of robotics is becoming widespread in healthcare. However, little is known about how robotics can affect the relationship with patients during an emergency or how it impacts clinicians in their organization and work. As a hospital responding to the consequences of the COVID-19 pandemic "ASST dei Sette Laghi" (A7L) in Varese, Italy, had to react quickly to protect its staff from infection while coping with high budgetary pressure as prices of Personal Protection Equipment (PPE) increased rapidly. In response, it introduced six semi-autonomous robots to mediate interactions between staff and patients. Thanks to the cooperation of multiple departments, A7L implemented the solution in less than 10 weeks. It reduced risks to staff and outlay for PPE. However, the characteristics of the robots affected staff's perceptions. This case study reviews critical issues faced by A7L in introducing these devices and recommendations for the path forward.
The Future of Trust will be Dystopian or Decentralized: Escaping the Metaverse

As we spend increasingly more time in the digital sphere relative to the physical one, an emerging concern is how the so-called will look like, and who will design and control it. For digital platform companies like Facebook, the metaverse looks like a set of virtual worlds that we can access via corporate-controlled entry points, such as proprietary headsets and app stores. But for Web3 players looking to build a decentralized web, the consists of community-owned platforms where users can earn a living in cryptocurrency and data is shared transparently using interoperable blockchains (sometimes called sidechains or parachains). While Facebook's version of the metaverse risks looking like a dystopian world controlled by undisclosed private interests, the Web3's version is more akin to a paraverse wherein multiple communities coexist independently of any centralized control. The fundamental difference between these two extreme scenarios is whom to trust in the digital world and how such trust comes into existence.