Miracle-dz/UniFAR-FTUs huggingface dataset:
The perceived impact of Technology Enhanced Learning (TEL) across a new pre-registration nursing curriculum

The construction and labelling of a relatively disparate set of university information technology systems as the “Nurse Navigator System” were routed in the principles of broader design research where methodologies of teaching, learning, and assessment were used to drive educational innovation within and between clinical and academic teaching. In terms of pragmatic design and appearance, this was straightforward; however, the theoretical basis of the design was more complex and rooted in core pedagogic design principles. Responding to the outcome of the initial evaluation of the system was therefore critical in the iterative developmental design of the Nurse Navigator System. Evaluation necessitated the collation of data which could tangibly and qualitatively examine whether expectations of such a conglomerate set of information technology criteria were realistic in practice. This pilot period of adjustment was recognized as a time to allow for configuring, fine-tuning, and assessment of purposefulness to the student cohort using it and in keeping with the need to co-construct learning and resource needs of students in practice. Evaluating the effectiveness of the preliminary pedagogic design of the Nurse Navigator System necessitated reliable indicators of engagement and learning. This research methods case study provides an overview of the qualitative evaluation of the impact of the new Nurse Navigator System using anecdote circles as an alternative to Focus Groups.
Health-Aware Multiobjective Optimal Charging Strategy With Coupled Electrochemical-Thermal-Aging Model for Lithium-Ion Battery

Battery fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, in this article, we propose a novel health-aware multiobjective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multiobjective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multiobjective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery.
Why Matrix Factorization Works Well in Recommender Systems: A Systems-Based Explanation

Many computer-based services use recommender systems that predict our preferences based on our degree of satisfaction with the past selections. One of the most efficient techniques making recommender systems successful is matrix factorization. While this technique works well, until now, there was no general explanation of why it works. In this paper, we provide such an explanation. 1 Formulation of the Problem Recommender systems. Many computer-based services aim at making the customers happier. For example, platforms like amazon.com that help us buy things not only allow us to buy what we want, they also advise us about we may be interested in looking at. The system comes up with this advice based on our previous pattern of purchases and on how satisfied we were with these purchases. For example, platforms like Netflix not only allow you to watch movies, they also use our previous selections to help the customer by providing advice on what other movies this particular customer will want to see. To make such recommendations, for each customer i, the system uses the ratings rij that different customers made for different objects j. Based on the
Distributed Processing of Location-Based Aggregate Queries Using MapReduce

The location-based aggregate queries, consisting of the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ) are new types of location-based queries. Such queries can be used to provide the user with useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of location-based aggregate queries that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper, we focus on developing the distributed processing technique to answer multiple location-based aggregate queries, based on the MapReduce platform. We first design a grid structure to manage information of objects by taking into account the storage balance, and then develop a distributed processing algorithm, namely the MapReduce-based aggregate query algorithm (MRAggQ algorithm), to efficiently process the location-based aggregate queries in a distributed manner. Extensive experiments using synthetic and real datasets are conducted to demonstrate the scalability and the efficiency of the proposed processing algorithm.
Modified sparse functional principal component analysis for fMRI data process

Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's disease and the control group. By applying proper classification methods for sparse features, we get better result than classic singular value decomposition, support vector machine and logistic regression.
Structured Bayesian Latent Factor Models with Meta-data

In the era of big data, huge amounts of data are being generated from the internet, social networks, phone apps, and so on, which creates high demand for powerful and efficient data analysis techniques. In areas such as collaborative filtering, text analysis, graph analysis, and bioinformatics, a large proportion of such data can be formulated into discrete matrices. This research focuses on developing structured Bayesian latent factor models with meta-data for analysing discrete data in the above areas. Compared with state-of-the-art methods, the proposed approaches have achieved not only better modelling performance and efficiency, but also preferable interpretability for intuitively understanding those data.
Modeling of Holographic Imaging of Volume Object using Modified Covariance Method

A high resolution modified covariance method is applied to model an ultrasonic holographic imaging (detection) of volume objects. The results demonstrate the enhanced performance capability of the combination of modified covariance and holographic techniques. The results are compared with the traditional, Fourier transform, method. The performance is investigated for different values of some parameters of the object under imaging process.
Slice Localization for Three-Dimensional Breast Ultrasound Volume Using Deep Learning

In routine ultrasonography, slice images of the internal organs of the human body are usually generated through an 1D array probe. The position and orientation of the probe is adjusted manually to obtain slice planes with pathological features. This is quite dependent on the experience and technique of a sonographer. This paper aims to locate 2D slice planes in a 3D breast ultrasound volume, which has significant application value in clinical ultrasound examinations. We propose a deep learning approach mapping all possible 2D image slices to their 3D coordinates parameters using a fully connected neural network implemented on MATLAB. We emphasize that this training must be done separately for each patient since the mammary tissue structure varies greatly from one person to another. The trained network can be interpreted as an image-slice location database for each patient. Our study is validated on GE ABUS (Automated Breast Ultrasound System) volume data. Each 2D image slice has four spatial parameters. The method achieves a prediction error of 0.14mm/0.25mm and 0.5 degree/0.3 degree for translation (x/y) and rotation (yaw/roll) parameters respectively, averaged over all practically scannable slices. It takes less than 0.1ms to predict the location of one 64×64 slice image. Thus, slice locations may be displayed with high accuracy in real-time when scanning with a conventional 1D probe, potentially allowing physicians to manipulate the probe to any scan planes of interest.
Learning-Based Energy-Efficient Resource Management by Heterogeneous RF/VLC for Ultra-Reliable Low-Latency Industrial IoT Networks

Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.
Computational Rumor Detection Without Non-Rumor: A One-Class Classification Approach

Rumor spreading in online social networks can inflict serious damages on individual, organizational, and societal levels. This problem has been addressed via computational approach in recent years. The dominant computational technique for the identification of rumors is the binary classification that uses rumor and non-rumor for the training. In this method, the way of annotating training data points determines how each class is defined for the classifier. Unlike rumor samples that often are annotated similarly, non-rumors get their labels arbitrarily based on annotators’ volition. Such an approach leads to unreliable classifiers that cannot distinguish rumor from non-rumor consistently. In this paper, we tackle this problem via a novel classification approach called one-class classification (OCC). In this approach, the classifier is trained with only rumors, which means that we do not need the non-rumor data points at all. For this study, we use two primary Twitter data sets in this field and extract 86 features from each tweet. We then apply seven one-class classifiers from three different paradigms and compare their performance. Our results show that this approach can recognize rumors with a high level of F1-score. This approach may influence the predominant mentality of scholars about computational rumor detection and puts forward a new research path toward dealing with this problem.
Reducing the Energy Consumption of Electric Buses With Design Choices and Predictive Driving

Transportation electrification is increasing and recently more focus has been directed on heavy vehicles and especially on city buses. Battery electric buses are inherently more energy efficient than diesel buses and the efficiency can be further increased by different methods. This paper evaluates the energy consumption reductions that are achievable with an aluminum chassis, low-drag body, low-rolling-resistance class C tires, heat pump, and predictive driving. A simulation model of a generic electric bus was developed in the Simulink software. Simulations were carried out on various types of driving cycles in cold (−10 °C) and warm conditions (20 °C). A novel nonlinear model predictive control problem formulation was created for minimizing the energy consumption of an electric bus. Using a heat pump instead of an electric heater provided the highest energy savings in the cold conditions with an average consumption reduction of 12.7%. The results indicated that a heat pump is particularly effective on low-speed bus routes. However, the class C tires and aluminum chassis provided higher energy savings than the heat pump in the warm conditions. The low-rolling-resistance tires achieved the most robust energy savings. The aluminum chassis reduced the energy consumption more than the class C tires, but the benefit of the lighter chassis was shown to also correlate strongly with the aggressiveness of the driving. The results showed that a low-drag body is a potential method for consumption reduction on high-speed bus routes. Predictive driving was found to reduce the average consumption by 9.5% at −10 °C when using 10-second prediction and control horizons.
Discrete Viewpoint Control to Reduce Cybersickness in Virtual Environments

Cybersickness in virtual reality is an on-going problem. We propose a method for reducing the onset of cybersickness caused by illusions of self-motion (vection), when using stationary VR setups. Our approaches rely on reducing optic flow and the inconsistent displacement approach. We propose two different technique including translational and rotational viewpoint movements. We run two different user studies and measure participant cybersickness levels via Simulator Sickness Questionnaire, as well as user reported levels of nausea, presence, and error rates. Overall, our results indicate that both viewpoints snapping and translation snapping significantly reduced SSQ-reported cybersickness levels by 40% for rotational viewpoint movement, and 50% for translational viewpoint movement. Both techniques resulted in a reduction in participant nausea levels, especially with longer VR exposure. Presence levels, error rate, and performance were not significantly different when using viewpoint snapping, or translation snapping as compared to a control condition with continuous viewpoint motion.
A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest

Container-based virtualization has gradually become a main solution in today‘s cloud computing environments. Detecting and analyzing anomaly in containers present a major challenge for cloud vendors and users. This paper proposes an online container anomaly detection system by monitoring and analyzing multidimensional resource metrics of the containers based on the optimized isolation forest algorithm. To improve the detection accuracy, it assigns each resource metric a weight and changes the random feature selection in the isolation forest algorithm to the weighted feature selection according to the resource bias of the container. In addition, it can identify abnormal resource metrics and automatically adjust the monitoring period to reduce the monitoring delay and system overhead. Moreover, it can locate the cause of the anomalies via analyzing and exploring the container log. The experimental results demonstrate the performance and efficiency of the system on detecting the typical anomalies in containers in both simulated and real cloud environments.
Exploring the effects of automated tracking of student responses to teacher feedback in draft revision: evidence from an undergraduate EFL writing course

ABSTRACT This study aimed to investigate the impact of using an automated tracking system on the writing performance of English as Foreign Language (EFL) students in a 13-week academic writing course. Sixty-eight first year university students participated in the study. They received the same instruction on academic writing and were allocated to one of two conditions: experimental (N = 36) or control (N = 32). Participants in the experimental condition could use the automated tracking system to generate analysis of teacher feedback on their draft essays and of their subsequent revisions in response to the feedback received, while those in the control condition could not. The results of this study show that the system could not only support students to reflect on the quality of their revisions but also likely result in improvements in their revised texts. The findings of this study would contribute to the body of literature on effects of using technology to facilitate student reflection on multiple-draft writing.
Indirect Data Representation Via Offset Vectoring: A Code-integrity-driven In-memory Data Regeneration Scheme

A common problem in software development is how to handle sensitive information required for appropriate process execution, especially when requesting user input like passwords or -phrases for proper encryption is not applicable due to I/O, UI or UX limitations. This often leads to such information being either stored directly in the source code of the application, or as plaintext in a separate file. We therefore propose an experimental scheme for dynamically recovering arbitrary chunks of information based on the integrity of the text-segment of a running process, without the information being easily extractible from either an ondisk binary, memory dump or the memory map of a running process. Implementing an algorithm we call offset vectoring, this method can help dealing with sensitive information and enhancing the resistance against attacks which aim at extracting such data as well as attempts towards modifying an application, e.g. for the purposes of cracking software.
Cryptanalysis of A Mutual Authentication Scheme for Smart Healthcare Systems under Global Mobility Networks Notion

Authentication and key agreement scheme is an important mechanism for legal users to access the services of smart healthcare systems under global mobility networks notion. However, the design of authentication and key agreement schemes is still quite a challenging problem. In this paper, we analyse a novel mutual authentication scheme for smart healthcare systems under global mobility networks notion proposed by Wu et al. in 2018, and point out the scheme is vulnerable to malicious user impersonation attack, offline guessing attack and suffers from low efficiency.
Foresight of Promising Technologies for Healthcare-IoT Convergence Service by Patent Analysis

The transplantation of modern innovations of the information and communication technology (ICT) field is contributing to the birth of beneficial services and effective production of them. Healthcare is no exception - new paradigms such as the ubiquitous healthcare (u-health) have been suggested as it is undergoing a process of convergence with smart device technologies and the Internet of Things (IoT) concept. This study aims to offer a foresight of promising technologies for the ICT-based healthcare by employing two technology forecasting methods. A network analysis is carried out for the keywords extracted from the abstract text of U.S. patent data. Then an expert panel interpreted each of the clusters in the network into a promising technology area by combining the meaning of keywords in it with professional knowledge and experience. As the result, five promising technology fields are proposed.
Observation-enhanced verification of operational processes

Operational processes are at the core of many organisations. The failure and misuse of these processes can cause significant economic losses to businesses or, in the worst cases, endanger human life. As a result, there has been significant research effort focused on the development of techniques and tools for the model-based analysis and verification of reliability, performance and quality-of-service properties of processes. Constructing models which accurately represent the behaviour of real-world systems is very challenging. The complexity and stochastic nature of real-world phenomena requires the use of modelling assumptions which introduce errors that can significantly impact the results of model-based analysis. Where inaccurate analyses are used as the basis of engineering or business decisions, the consequences can be catastrophic. Many operational processes are now routinely instrumented and capture information about component interactions and the behaviour of human operators. This thesis introduces a set of tool-supported techniques which exploit these logs in conjunction with tried and tested probabilistic model checking. This produces Markov models and formal analysis techniques which more accurately capture process behaviours and improve the quality of model-based analysis for operational processes. We show how observation data can be used to improve the modelling and analysis of continuous time systems by refining continuous-time Markov models (CTMCs) to more accurately reflect real-world behaviours. We apply the tools and techniques developed to real-world processes and demonstrate how we may avoid the invalid decisions which arise from traditional CTMC modelling and analysis techniques. We also show how observation-enhanced discrete time Markov models may be used to characterise the behaviour of users within an operational process. The self-adaptive role based access control approach we develop uses a formal definition of adaptation policies to identify potential threats in a real-world IT support system and mitigates risks to the system.
An Overview of the Endless Battle between Virus Writers and Detectors: How Compilers Can Be Used as an Evasion Technique

The increasing complexity of new malware and the constant refinement of detection mechanisms are driving malware writers to rethink the malware development process. In this respect, compilers play a key role and can be used to implement evasion techniques able to defeat even the new generation of detection algorithms. In this paper we provide an overview of the endless battle between malware writers and detectors and we discuss some considerations on the benefits of using high level languages and even exotic compilers (e.g. single instruction compilers) in the process of writing malicious code.