Reviewerly/exHarmony huggingface dataset:
Blogger and YouTube services at a distant course “Database management system Microsoft Access”

The article is devoted to the coverage of the course “Database management system Microsoft Access”, an educational blog review “The development of a creative child. ІCТ”, which is used as an auxiliary tool for promoting a course and teacher in the Internet, structural analysis of this blog is made. The channel location is set on YouTube video hosting and how it is used in the course on databases. Attention is drawn to the fact that theoretical and practical material is considered on real, implemented informational and analytical systems. To prepare students for the Olympiads and provide methodological help teachers of computer science are looking at tasks from databases that were offered at the All-Ukrainian Olympiads on Information Technologies, especially II, III and IV stages (offline and online Olympiads), which are located in open access to the blog and YouTube channel. The main focus of the article is devoted to the practical side of teaching teachers of computer science, experience in using the above technologies.
Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients

Abstract Introduction Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID‐19‐hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory‐confirmed COVID‐19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non‐laboratory‐confirmed test result were excluded. Results A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased. Conclusions The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.
From compute to care: Lessons learned from deploying an early warning system into clinical practice

Deploying safe and effective machine learning models is essential to realize the promise of artificial intelligence for improved healthcare. Yet, there remains a large gap between the number of high-performing ML models trained on healthcare data and the actual deployment of these models. Here, we describe the deployment of CHARTwatch, an artificial intelligence-based early warning system designed to predict patient risk of clinical deterioration.We describe the end-to-end infrastructure that was developed to deploy CHARTwatch and outline the process from data extraction to communicating patient risk scores in real-time to physicians and nurses. We then describe the various challenges that were faced in deployment, including technical issues (e.g., unstable database connections), process-related challenges (e.g., changes in how a critical lab is measured), and challenges related to deploying a clinical system in the middle of a pandemic. We report various measures to quantify the success of the deployment: model performance, adherence to workflows, and infrastructure uptime/downtime. Ultimately, success is driven by end-user adoption and impact on relevant clinical outcomes. We assess our deployment process by evaluating how closely we followed existing guidance for good machine learning practice (GMLP) and identify gaps that are not addressed in this guidance.The model demonstrated strong and consistent performance in real-time in the first 19 months after deployment (AUC 0.76) as in the silent deployment heldout test data (AUC 0.79). The infrastructure remained online for >99% of time in the first year of deployment. Our deployment adhered to all 10 aspects of GMLP guiding principles. Several steps were crucial for deployment but are not mentioned or are missing details in the GMLP principles, including the need for a silent testing period, the creation of robust downtime protocols, and the importance of end-user engagement. Evaluation for impacts on clinical outcomes and adherence to clinical protocols is underway.We deployed an artificial intelligence-based early warning system to predict clinical deterioration in hospital. Careful attention to data infrastructure, identifying problems in a silent testing period, close monitoring during deployment, and strong engagement with end-users were critical for successful deployment.
Formal specification and model checking of Saber lattice-based key encapsulation mechanism in Maude

The security of most public-key cryptosystems currently in use today is threatened by advances in quantum computing.That is the reason why recently many researchers and industrial companies have spent lots of effort on constructing post-quantum cryptosystems, which are resistant to quantum attackers.A large number of post-quantum key encapsulation mechanisms (KEMs) have been proposed to provide secure key establishment -one of the most important building blocks in asymmetric cryptography.This paper presents a formal security analysis of Saber lattice-based KEM.We first formally specify the mechanism in Maude, a rewriting logic-based specification/programming language equipped with many functionalities, such as a reachability analyzer (or the search command) that can be used as an invariant model checker, and then conduct invariant model checking with the Maude search command, finding an attack.
The Spectacular Collapse of Cryptokitties

On 4 September 2018, someone known only as Rabono bought an angry cartoon cat named Dragon for 600 ethers - an amount of Ethereum cryptocurrency worth about US $170,000 at the time, or $745,000 at the cryptocurrency's value in July 2022. • It was by far the highest-priced transaction yet for a nonfungible token (NFT), the then-new concept of a unique digital asset. And it was a headline-grabbing opportunity for CryptoKitties, the world's first blockchain gaming hit. But the sky-high cost of the transaction obscured a more difficult truth: CryptoKitties was dying, and it had been for some time. • Dragon was never resold—a strange fate for one of the most historically relevant NFTs ever. Newer NFTs such as “The Merge,” a piece of digital art that sold for the equivalent of $92 million, left Dragon behind as the NFT market surged to record sales, totaling roughly $18 billion in 2021. Has the world simply moved on to newer blockchain projects? Or is this the fate that awaits all NFTs?
Color image encryption scheme combining cross-plane Zigzag scrambling and pseudo-random combination RGB component diffusion

At present, the frequency of image transmission in the network has increased many times compared with previous years, and the proportion of color images is the largest. This means that a powerful technology is needed to prevent color images from being stolen to ensure the security of transmission. The excellent characteristics of chaotic cryptography are favored by many scholars. Researchers have designed a series of color image encryption algorithms using this technology. Because of the large number of pixels in the three planes of the color image, some encryption algorithms are guaranteed in security, but the encryption efficiency is often not ideal. In order to avoid this defect as much as possible, this paper develops a new color image encryption scheme on the basis of ensuring the security of the image. First, a cross plane zigzag scrambling process is proposed, which solves the shortage of three planes scrambling separately. Secondly, the rules of pseudo-random combination of three plane pixel values are designed to improve the efficiency and unpredictability of encryption. In addition, the chaotic source selected in this paper is a new one-dimensional sine power parameter map (1D-SPPM). Experiments show that this scheme can resist classical attack types, and compared with some existing schemes, its efficiency and security are guaranteed.
Educational Technology Quarterly: in the beginning

The editorial that opens the 1st issue of Educational Technology Quarterly.
Lessons learnt of the COVID-19 contact tracing strategy in Islamabad Capital Territory, Pakistan using systems thinking processes

The strategy of test, trace and isolate has been promoted and seen as a crucial tool in the fight against the COVID-19 pandemic. As simple as the slogan sounds, effectively implementing it turns into a complex endeavor with multiple moving parts and the need for multisector collaboration. In this study, we apply a systems thinking lens to analyse the design and implementation of the contact tracing strategy for COVID-19 in the district of Islamabad, Pakistan. The data collection included participatory observation, reflective exercises, key informant interviews and participatory workshops with district health managers and health providers. The information gathered was structured using process and stakeholder mapping to identify the lessons learned of the COVID-19 contact tracing strategy. The results showed that the elements crucial for implementation were, good coordination during a crisis, available resources mobilized effectively and establishment of early active surveillance for contact tracing. Furthermore, the main aspects to be improved were lack of preparedness and existing surveillance systems and task shifting leading to impact on regular health services. The results of this study highlight the importance of developing information systems that are coherent with existing processes and resources, even in times of crisis.
Entanglement and Fisher information for a two-atom system interacting with deformed fields in correlated two-mode states

Based on the deformed Heisenberg algebra, we propose a nonlinear Tavis–Cummings model (TCM) which is obtained from the usual TCM by using deformed operators of a two-mode field. Such a generalization of the TCM considers the interaction of a two-atom system with a correlated two-mode field in the presence of time-dependent coupling. We show the effect of the deformation and interaction process between the two modes and atoms on the time variation of current interest quantum phenomena, such as entanglement and parameter estimation. The obtained results provide a new approach to manipulate the interaction between the atoms and fields that can be useful for considerable applications in quantum optics and information.
Direct Collocation Methods for Trajectory Optimization in Constrained Robotic Systems

Direct collocation methods are powerful tools to solve trajectory optimization problems in robotics.While their resulting trajectories tend to be dynamically accurate, they may also present large kinematic errors in the case of constrained mechanical systems, i.e., those whose state coordinates are subject to holonomic or nonholonomic constraints, like loop-closure or rolling-contact constraints.These constraints confine the robot trajectories to an implicitly-defined manifold, which complicates the computation of accurate solutions.Discretization errors inherent to the transcription of the problem easily make the trajectories drift away from this manifold, which results in physically inconsistent motions that are difficult to track with a controller.This paper reviews existing methods to deal with this problem and proposes new ones to overcome their limitations.Current approaches either disregard the kinematic constraints (which leads to drift accumulation) or modify the system dynamics to keep the trajectory close to the manifold (which adds artificial forces or energy dissipation to the system).The methods we propose, in contrast, achieve full drift elimination on the discrete trajectory, or even along the continuous one, without artificial modifications of the system dynamics.We illustrate and compare the methods using various examples of different complexity.
Intent-Aware Interactive Internet of Things for Enhanced Collaborative Ambient Intelligence

The Internet of Things (IoT) enables the connection of a broad range of artifacts with advanced sensory technologies and produces massive amounts of data to support ambient intelligence. While the potential of IoT systems is widely recognized, little work has demonstrated a system with the ability to execute autonomously in the real world. Inspired by the success of robotics in specialized IoT environments, we propose an end-to-end solution for a generic, interactive ambient intelligence system where robots can assist humans in conducting activities in IoT-enabled smart homes. We evaluate the solution using implementations of public benchmarks on open-source platforms and use several activities to demonstrate the effectiveness of the proposed solution in real life.
Decision making towards large-scale alternatives from multiple online platforms by a multivariate time-series-based method

With the increasing popularity of Internet-related techniques, decision-making problems with large-scale alternatives from multiple online platforms, such as consumer choice decisions and movie selections, have been emerging hot topics. How to make a selection from a set of alternatives in multiple online platforms is challenge for consumers. In this study, we introduce a multivariate time-series-based decision-making method to solve the problem with large-scale alternatives. Firstly, we set up a multivariate time series from multiple-platforms regarding each alternative. The weights of these platforms are determined based on the information entropy of time series and the number of received evaluations given by platform users. To calculate the information entropy regarding a large number of alternatives, we adopt a time series clustering method to classify alternatives into different clusters, and then calculate the information entropy of clusters and take it as the information entropy of all alternatives. Afterwards, the scores of alternatives are calculated based on the weighted averaging aggregation operator and the alternatives are ranked according to their scores. We demonstrate the effectiveness of the proposed method in guaranteeing the consistency between ranking results and users' consumption behaviors based on real ratings collected from three film-review websites. It is hoped that the proposed method would be helpful for users to intelligently make a selection from large-scale candidate products or services in multiple platforms.
Determination of the full-field stress and displacement using photoelasticity and sampling moiré method in a 3D-printed model

The quantitative characterization of the full-field stress and displacement is significant for analyzing the failure and instability of engineering materials. Various optical measurement techniques such as photoelasticity, moiré and digital image correlation methods have been developed to achieve this goal. However, these methods are difficult to incorporate to determine the stress and displacement fields simultaneously because the tested models must contain particles and grating for displacement measurement; however, these elements will disturb the light passing through the tested models using photoelasticity. In this study, by combining photoelasticity and the sampling moiré method, we developed a method to determine the stress and displacement fields simultaneously in a three-dimensional (3D)-printed photoelastic model with orthogonal grating. Then, the full-field stress was determined by analyzing 10 photoelastic patterns, and the displacement fields were calculated using the sampling moiré method. The results indicate that the developed method can simultaneously determine the stress and displacement fields.
Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of the Helmholtz partial differential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance distance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escape route maneuvers. This approach provides a guaranteed collision avoidance mechanism while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the effectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering difficulties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indicate real-world applicability of the proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.
A Cluster-Driven Adaptive Training Approach for Federated Learning

Federated learning (FL) is a promising collaborative learning approach in edge computing, reducing communication costs and addressing the data privacy concerns of traditional cloud-based training. Owing to this, diverse studies have been conducted to distribute FL into industry. However, there still remain the practical issues of FL to be solved (e.g., handling non-IID data and stragglers) for an actual implementation of FL. To address these issues, in this paper, we propose a cluster-driven adaptive training approach (CATA-Fed) to enhance the performance of FL training in a practical environment. CATA-Fed employs adaptive training during the local model updates to enhance the efficiency of training, reducing the waste of time and resources due to the presence of the stragglers and also provides a straggler mitigating scheme, which can reduce the workload of straggling clients. In addition to this, CATA-Fed clusters the clients considering the data size and selects the training participants within a cluster to reduce the magnitude differences of local gradients collected in the global model update under a statistical heterogeneous condition (e.g., non-IID data). During this client selection process, a proportional fair scheduling is employed for securing the data diversity as well as balancing the load of clients. We conduct extensive experiments using three benchmark datasets (MNIST, Fashion-MNIST, and CIFAR-10), and the results show that CATA-Fed outperforms the previous FL schemes (FedAVG, FedProx, and TiFL) with regard to the training speed and test accuracy under the diverse FL conditions.
IAF-LG: An Interactive Attention Fusion Network With Local and Global Perspective for Aspect-Based Sentiment Analysis

One of the interesting trending phenomena in sentiment analysis is the prediction of sentiment given by the user towards an aspect term. Till today, a considerable number of researchers have proposed varying methodologies for predicting aspect-based sentiments. But they mostly encapsulate the semantic information by manifesting themselves within a local boundary around each aspect term and overlook capturing the semantic concept that is conveyed within the entire review (global). Therefore, this study proposes a model, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAF-LG</i> , that performs semantic learning at both local and global scales to discover aspect-based sentiments. IAF-LG first encodes the local semantics by fusing contextual-semantic dependencies between tokens and computing relational semantics between inter-aspects. Next, it develops the global semantics by formulating interactions between local semantics and review-based sentiment learning. Lastly, it conjoins the local and global interactive learning to earn credible semantics for predicting the accurate sentiment of aspect terms. Extensive experiments on publicly available datasets demonstrate the significantly improved performance of IAF-LG than competitive baselines.
Single document text summarization addressed with a cat swarm optimization approach

The availability of a tremendous amount of online information bringing about a broad interest in extracting relevant information in a compact and meaningful way, prompted the need for automatic text summarization. Hence, in the proposed system, the automated text summarization has been considered as an extractive single-document summarization problem, and a Cat Swarm Optimization (CSO) algorithm-based approach is proposed to solve it, whose objective is to generate good summaries in terms of content coverage, informative, anti-redundancy, and readability. In this work, input documents are pre-processed first. Then the cat population is initialized, where each individual (cat) in a binary vector is randomly initialized in the search space, considering the constraint. The objective function is then formulated considering different sentence quality measures. The Best Cat Memory Pool (BCMP) is initialized based on the objective function score. After that, individuals are randomly distributed for position updating to perform seeking/tracing mode operations based on the mixture ratio in each iteration. BCMP is also updated accordingly. Finally, an optimal individual is chosen to generate the summary after the last iteration. DUC-2001 and DUC-2002 data sets and ROUGE measures are used for system evaluation, and the obtained results are compared with the various state-of-the-art methods. We have achieved approximately 25% and 5% improvement on ROUGE-1 and ROUGE-2 scores on the datasets over the best existing method mentioned in this paper, revealing the proposed method's superiority. The proposed system is also evaluated considering the generational distance, CPU processing time, cohesion, and readability factor, reflecting that the system-generated summaries are readable, concise, relevant, and fast. We have also conducted a two-sample t-test, and one-way ANOVA test showing the proposed approach is statistically significant.