Reviewerly/exHarmony huggingface dataset:
Examining the use of digital technology in schools with a school-wide approach to personalized learning

Abstract A growing number of schools have recently been changing their culture of teaching and learning towards personalized learning. Our study investigates how schools use digital technology to facilitate and promote personalized practices. Based on the answers of a student questionnaire from 31 lower-secondary schools with a personalized learning policy in Switzerland, we selected the three cases with the most frequent use of digital technology in the classroom. Using key categories of digital technology implementation to frame the analysis, we examined the differences and similarities regarding the contribution of digital technology to fostering personalized learning. A systematization of our analyses resulted in three different types in terms of how schools integrate digital tools into their daily practices: 1. selective use of digital technology according to individual teacher preference; 2. selective use of digital technology according to individual student preference; and 3. structural use of digital technology in accordance with a school-wide strategy. The findings provide indications for future research and practice with respect to an implementation of personalized learning that takes full advantage of digital technology.
On new approaches to the study of school informatics in the conditions of digital transformation of society

The 2022/2023 academic year marks the beginning of a new stage in the development of school informatics as one of the subjects that most fully reﬂects the social order in the context of the digital transformation of many areas of our lives. In the updated Federal State Educational Standards, for the frst time, the possibility of studying informatics at the basic and advanced levels is already recorded in the basic school. By choosing an advanced level of studying informatics in the basic school, educational organizations can go beyond the Procrustean bed of an ineﬀective one-hour subject, which until now many experts rightly considered school informatics. Approved by the Federal Educational and Methodological Association for General Education, exemplary work programs in informatics of the basic and advanced levels for 7th–9th grades, as well as a program in informatics for 5th–6th grades, allow educational organizations to oﬀer students various trajectories for mastering the subject.
Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

Abstract Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.
Model-Agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition

In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of a backbone network. Unlike a multi-head network that switches classification heads only, our model switches not only the heads, but also the adapters for facilitating to learn feature representations universal to multiple domains. Unlike prior works, the proposed method is model-agnostic and doesn't assume model structures unlike prior works. Experimental results on three popular action recognition datasets (HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more effective than a multi-head architecture and more efficient than separately training models for each domain.
Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network

In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system's overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries-PyTorch and TensorFlow-and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented.
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine learning based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.
Improved One-Stage Detectors with Neck Attention Block for Object Detection in Remote Sensing

Object detection in remote sensing is becoming a conspicuous challenge with the rapidly increasing quantity and quality of remote sensing images. Although the application of Deep Learning has obtained remarkable performance in Computer Vision, detecting multi-scale targets in remote sensing images is still an unsolved problem, especially for small instances which possess limited features and intricate backgrounds. In this work, we managed to cope with this problem by designing a neck attention block (NAB), a simple and flexible module which combines the convolutional bottleneck structure and the attention mechanism, different from traditional attention mechanisms that focus on designing complicated attention branches. In addition, Vehicle in High-Resolution Aerial Imagery (VHRAI), a diverse, dense, and challenging dataset, was proposed for studying small object detection. To validate the effectiveness and generalization of NAB, we conducted experiments on a variety of datasets with the improved YOLOv3, YOLOv4-Tiny, and SSD. On VHRAI, the improved YOLOv3 and YOLOv4-Tiny surpassed the original models by 1.98% and 1.89% mAP, respectively. Similarly, they exceeded the original models by 1.12% and 3.72% mAP on TGRS-HRRSD, a large multi-scale dataset. Including SSD, these three models also showed excellent generalizability on PASCAL VOC.
Development Methodology for Immersive Home Laboratories in Virtual Reality

Although fluid mechanics should be an easy area of physics to learn due to the ubiquity of flows, many students already struggle to understand the basics. Unlike hands-on labs, this is where virtual labs can provide additional interactive visualizations to better facilitate and promote understanding of the fundamental phenomena. However, how can such laboratories be developed for engineering education relatively easily? This contribution presents a simple technical methodology to convert laboratory experiments into a virtual home laboratory. The basis is the representation of arbitrary data in virtual reality. The authors show on the example of a fluid mechanics laboratory experiment a simple numerical method to calculate the flow by using the so-called fictitious domain approach, a procedure to import CFD-calculated fluid-mechanical data into the Unreal engine 4 originally made for development of computer games. As a game engine, it offers high performance so that the virtual environment runs smoothly on ordinary computers without need of special hardware, so also VR-goggles are optionally. In the home laboratory, the flow can be influenced by setting the pressure gradient and we show how to display vector fields, particle motion, residence time, streamlines, information panels, and scalar fields in 3d representation and in arbitrarily set cut planes. Since many of the features cannot be displayed in a real lab, this contribution reflects some possibilities of the design principles of industry 4.0 in a virtual lab.
A Study on Object Detection Performance of YOLOv4 for Autonomous Driving of Tram

Recently, autonomous driving technology has been in the spotlight. However, autonomous driving is still in its infancy in the railway industry. In the case of railways, there are fewer control elements than autonomous driving of cars due to the characteristics of running on railways, but there is a disadvantage in that evasive maneuvers cannot be made in the event of a dangerous situation. In addition, when braking, it cannot be decelerated quickly for the weight of the body and the safety of the passengers. In the case of a tram, one of the railway systems, research has already been conducted on how to generate a profile that plans braking and acceleration as a base technology for autonomous driving, and to find the location coordinates of surrounding objects through object recognition. In pilot research about the tram's automated driving, YOLOv3 was used for object detection to find object coordinates. YOLOv3 is an artificial intelligence model that finds coordinates, sizes, and classes of objects in an image. YOLOv3 is the third upgrade of YOLO, which is one of the most famous object detection technologies based on CNN. YOLO's object detection performance is characterized by ordinary accuracy and fast speed. For this paper, we conducted a study to find out whether the object detection performance required for autonomous trams can be sufficiently implemented with the already developed object detection model. For this experiment, we used the YOLOv4 which is the fourth upgrade of YOLO.
Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling

Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.
Adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) modelling of Cu, Cd, and Pb adsorption onto tropical soils

Soils interact in many ways with metal ions thereby modifying their mobility, phase distribution, plant availability, speciation, and so on. The most prominent of such interactions is sorption. In this study, we investigated the sorption of Pb, Cd, and Cu in five natural soils of Nigerian origin. A relatively sparsely used method of modelling soil-metal ion adsorption, i.e. adaptive neuro-fuzzy inference system (ANFIS), was applied comparatively with multiple linear regression (MLR) models. The isotherms were well described by Freundlich and Langmuir equations (R2 ≥ 0.95) and the kinetics by nonlinear two-stage kinetic model, TSKM (R2 ≥ 0.81). Based on the values delivered by the Langmuir equation, the maximum adsorption capacities (Qm*) were found to be in the ranges 10,000-20,000, 12,500-50,000, and 4929-35,037 µmol kg-1 for Cd, Cu, and Pb, respectively. The study revealed significant correlations between Qm* and routinely determined soil parameters such as soil organic carbon (Corg), cation exchange capacity (CEC), amorphous Fe and Mn oxides, and percentage clay content. These soil parameters, combined with operational variables (i.e. solution/soil pH, initial metal concentration (Co), and temperature), were used as input vectors in ANFIS and MLR models to predict the adsorption capacities (Qe) of the soil-metal ion systems. A total of 255 different ANFIS and 255 different MLR architectures/models were developed and compared based on three performance metrics: MAE (mean absolute error), RMSE (root mean square errors), and R2 (coefficient of determination). The best ANFIS returned MAEtest 0.134, RMSEtest 0.164, and R2test 0.76, while the best MLR returned MAEtest 0.158, RMSEtest 0.199, and R2test 0.66, indicating the predictive advantage of ANFIS over MLR. Thus, ANFIS can fairly accurately predict the adsorption capacity and/or distribution coefficient of a soil-metal ion system a priori. Nevertheless, more investigation is required to further confirm the robustness/generalisation of the proposed ANFIS.
Making drug supply chain secure traceable and efficient: a Blockchain and smart contract based implementation

The healthcare supply chain involves obtaining resources, managing supplies, and delivering goods and services to patients across multiple teams, stakeholders, and geographical boundaries. With such a complex structure, the healthcare supply chain is vulnerable to fraud, inaccurate data, and lack of transparency. These misdeeds cost businesses money and harm health. To address these issues, the health care supply chain needs an end-to-end decentralized track-and-trace system. Most centralized systems risk drug and data safety. This paper presents an Ethereum blockchain-based solution for a health care supply chain track-and-trace mechanism that uses smart contracts and data immutability. Hash functions store data in a public distributed ledger. This protects and discloses data. Smart contracts automate agreement execution so all parties know the outcome instantly, without an intermediary or time loss. It also outlined decentralized healthcare supply chain application architecture and algorithms. This paper proposes a system to address the lack of transparency and tracking in traditional supply chains. The blockchain-based method proposed in this paper runs on Solidity smart contracts. The system's algorithms and methods are tested against a variety of inputs, and the results are presented as an average gas cost for specific functionality. The proposed system tracks goods' histories (medicine). The average gas cost for all accounts is 18,027.2. Overall, log gas costs 48,118.6 to buy medicine, gas costs 229,607.5, and to log out 14,275.The results of the proposed system are compared to state-of-the-art methods. Thus, the presented work allows a seamless flow of medicines via blockchain and smart contracts without intermediaries. Finally, it addresses building a secure pharma supply chain application for blockchain 4.0.
How education level influences internet security knowledge, behaviour, and attitude: a comparison among undergraduates, postgraduates and working graduates

During the pandemic, the prevailing online learning has brought tremendous benefits to the education field. However, it has also become a target for cybercriminals. Cybersecurity awareness (CSA) or Internet security awareness in the education sector turns out to be critical to mitigating cybersecurity risks. However, previous research indicated that using education level alone to judge CSA level received inconsistent results. This study postulated Social Educational Level (SEL) as a moderator with an extended Knowledge-Attitude-Behaviour model, used students’ year level as a proxy for the impact of education level, and used work exposure for the influence of social education level, to compare CSA among undergraduates, postgraduates and working graduates. The participants in the study were divided into six groups, namely year 1 university students, year 2-3university students, final-year students, postgraduate students, young working graduates, and experienced working graduates. The Human Aspects of Information Security Questionnaire was used to conduct a large-scale survey. The multivariate regression model analysis showed significant differences among the knowledge, attitude and behaviour dimensions across groups with different conditions of year-level and work exposure. However, it was found that SEL played a more significant role than an individual’s education level. The study suggested that a greater endeavour be committed to educating the public at large together with individuals, institutes, corporate and governments to improve the national CSA level.
ATTransUNet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation

Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the deficiencies of convolutional neural networks (CNNs) in global context modeling, and thus improves segmentation performance. However, in Transformer, due to the heavy computational burden of paired attention modeling between redundant visual tokens, the efficiency of Transformer needs to be further improved. Therefore, in this paper, we propose ATTransUNet, a Transformer enhanced hybrid architecture based on the adaptive token for ultrasound and histopathology image segmentation. In the encoding stage of the ATTransUNet, we introduced an Adaptive Token Extraction Module (ATEM), which can mine a few important visual tokens in the image for self-attention modeling, thus reducing the complexity of the model and improving the segmentation accuracy. In addition, in the decoding stage, we introduce a Selective Feature Reinforcement Module (SFRM) to reinforce the representation of and attention to key tissues or pathological features. The proposed ATTransUNet is evaluated on the basis of three medical image segmentation datasets. The results show that ATTransUNet achieves the best segmentation performance compared with the previous state-of-the-art models, and the proposed method is also competitive in terms of the network parameters and computation.
Insight into breast cancer detection: new hybrid feature selection method

Abstract Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F -measure. In contrast, it has the lowest error rate value of 0.03.
Privacy and Utility of Private Synthetic Data for Medical Data Analyses

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.