Miracle-dz/UniFAR-FTUs huggingface dataset:
Systems Design Framework for a Practice-Based Evidence Approached Clinical Decision Support Systems

The widespread adoption of Information and Communication Technologies (ICT) in the healthcare industries have given rise to the increase in the generation of valuable Electronic Health Records (EHRs). This brings potential in the secondary uses of EHRs for research, analysis and decision making support. Clinical Decision Support System (CDSS) has become an integral tool in assisting healthcare professionals, make well-informed decisions. However, CDSSs are plagued with issues of effectiveness in delivering recommendations and support. With ongoing concerns regarding the limitations of evidence generated from systematic studies and effectiveness of CDSS to offer recommendations, a Practice-based evidence (PBE) approach to decision making is proposed. In this study, we present a system design framework that models and implements a PBE-approached CDSS. Such a CDSS design leverages on practical evidence in the form of EHRs where health records of patients are inferred and potentially used to support decision making for other similar patient types. Our design approach includes identifying suitable data sources and integration tool that packages various sources of health records as evidence for decision support systems. We present a prototype of our PBE-approached CDSS that provides patient-centric statistical information and prediction modelling.
Invariant detection of orthogonal frequency division multiplexing signals with unknown parameters for cognitive radio applications

The authors propose a computationally efficient spectrum sensing solution for an orthogonal frequency division multiplexing (OFDM) signal in a frequency-selective fading channel with additive white Gaussian noise (AWGN). The authors' assumption is that the data symbols, channel coefficients and the noise variance are all unknown. The nature of the problem leads them to find an invariant detector. The optimum one is uniformly most powerful invariant (UMPI); their effort shows that this test does not exist, as the final decision statistic depends on some unknown parameters; although, they derive the most powerful invariant (MPI) detector, implanting these parameters, to provide an upper bound for invariant detectors. Instead, the authors develop the generalised likelihood ratio test (GLRT), substituting the unknown parameters by their maximum likelihood (ML) estimates in the Neyman'Pearson likelihood ratio. Furthermore, they propose a computationally efficient implementation of the resulting detector. Simulation results show a slight decrease in efficiency against gaining so much computational complexity (CC) improvement. For more comparisons they also derive the conventional energy detector, namely, generalised energy detector (GED) for a binary phase shift keying (BPSK) signal, to show the adequacy of the GLR detector performance which would be demonstrated by simulation results.
Mobile Technology Contributing to Omni-Channel Retail

Technological integration and convergence enables customers to experience conventional, online and mobile shopping. Companies tailor multiple retail channels in respond to their customers' needs. The so-called omni-channel retail strategy suggests an integrated shopping approach, combining the convenience of various digital channels with the customers' personal experience, typical for brick and mortar settings. Due to the co-existence of multiple sales channels complementing each other, retail companies may better engage with their customers. Moreover, the easy and seamless switching between various retail channels allows customers to experience the brand holistically, and thus be more satisfied with the shopping experience and the product per se, and be more brandloyal. This paper aims to investigate and analyse -- based on recent literature -- the advantages and challenges of omni-channel retailing on a broad, systematic basis from a business perspective. As a result, we identify those aspects of omni-channel retailing, where mobile technologies may contribute either to further improvements or to eliminate drawbacks.
Analysing Emerging Topics across Multiple Social Media Platforms

The ability to compose emerging topics from the data collected from multiple social media platforms can help individuals and organisations meet their business goals and improve decision-making, as such information can provide more complete and accurate information. However, existing research has mainly focused on analysing emerging topics from the posts and related data collected from a single social media platform. In this paper, we propose a framework referred to as Multi-source Social Topic Media Analysis (xSMA) framework to model, rank and semantically analyse emerging topics across various social media platforms. The implementation and evaluation of the xSMA framework using real-world datasets obtained from Twitter and Reddit are also described.
Real-time learning analytics system for improvement of on-site lectures

Purpose The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc. Design/methodology/approach The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation. Findings Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences. Originality/value Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.
Construction et gestion du terrain commun chez des enfants de sept à quatorze ans et des adultes

Cette recherche porte sur la facon dont des enfants de sept a quatorze ans, compares aux adultes, construisent et gerent le «terrain commun» en vue de la comprehension mutuelle. L'experience reprend celle realisee sur des adultes par Clark et Wilkes-Gibbs (1986): il s'agit d'un jeu d'identification de formes (le jeu de Tangram), ou l'un des sujets doit donner des instructions verbales au partenaire pour que ce dernier reconstitue l'ordre d'un ensemble de cartes representant des formes abstraites. A tous les âges, on retrouve des resultats analogues a ceux qu'avaient observes Clark et Wilkes-Gibbs chez les adultes: diminution reguliere du nombre de mots et de tours de parole au cours des essais, augmentation progressive des references definies ; on note aussi une evolution significative dans l'utilisation du vocabulaire. Le recours au terrain commun est deja nettement detectable a sept ans; il fait ensuite l'objet d'une utilisation de plus en plus efficace avec l'âge Mais en outre, on observe certaines differences qualitatives entre les groupes d'âge dans le mode de construction et de gestion de ce terrain commun, qui suggerent le passage d'un mode de fonctionnement implicite et spontane, a un mode controle et explicite.
Multi-agent-based System to Model and Simulate the Emergency Response in Metropolis

Emergency incidents happen frequently and cause huge loss of both lives and properties. However, in the traditional emergency response cycle, the involvement of human operators and resource dispatchers make bad decisions in some cases and the communications between different departments are in low efficiencies. Thus, Effective and rapid emergency response is very necessary. This paper discusses some existing approaches which can assist the emergency responses then propose a multi-agent-based system to model and simulate the emergency response cycle in real life in order to improve the efficiency of the emergency response.
Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach

Chronic diseases and associated conditions are the leading causes of death in most of the countries worldwide. Due to this, governments all over the world are concerned about the burden of chronic diseases. These diseases often pose severe health risks to the patients when they suffer from more than one chronic disease at the same time (also named as comorbidity of chronic disease). Several prediction approaches utilizing routinely collected administrative healthcare data have been developed for the prevention and better management of comorbidity. Most studies to date have focused on understanding the progression of single chronic disease rather than multiple chronic diseases. In this study, a research framework is proposed using social network analysis and graph theory using administrative healthcare data to understand the comorbidity of two chronic diseases (i.e., type 2 diabetes (T2D) leading to the development of cardiovascular disease). The results show that diseases related to blood (e.g., high blood pressure or high cholesterol) and kidney disease occurred frequently during the progression of cardiovascular disease for the T2D patients. The proposed framework could be useful for stakeholders including governments and health insurers to adopt appropriate prevention or management program for the patients at high risk of developing multiple chronic diseases.
Quantum era challenges for classical computers

Quantum computers are expected as a major revolution in computer science. The availability of quantum computational power will enable the exponential speedup of several algorithms, allowing to perform in a much faster way computation related, for instance, with biology, chemistry, and physics. Quantum computers however are not intended as replacement for classical computers. In contrast, the realization of quantum computers will pose several challenges which have necessary to be tackled using the classical computation power. Among them, the two most important ones are certainly the study and the deployment of a public key infrastructure which is robust against quantum computational power, and the realization and implementation of robust quantum error correction codes, a fundamental building block for quantum computers. In this paper we discuss these two challenges summarizing the state of the art and reporting potential research directions.
Papertian Mathetics with Concept Map Stories

This paper describes the design and implementation of an assessment inspired by Papert's approach to learning. The assessment is part of a course unit called Digital Making and Learning on a masters programme in Education aimed at students with a predominantly social sciences background. The design of the assessment as part of this course is fundamentally based on Papert's work, and maintains a 'mathetic' approach, focusing on the process of learning rather than a transfer of knowledge and competencies. In order to do this, an online collaborative concept-mapping tool with history-tracking functionality is adopted. The paper describes the social theory lens that informs the entire course and associated assessment design. The practical implementation of the course and how the assessment task functions as part of this learning design is explained, and finally some early evidence for the efficacy of the assessment is discussed.
Electroencephalogram signals classification for sleep-state decision - a Riemannian geometry approach

In this work, the authors study the classification of electroencephalogram (EEG) signals for the determination of the state of sleep of a patient. They employ the power spectral density (PSD) matrices as the feature for the distinction between different classes of EEG signals. This not only allows us to examine the power spectrum contents of each signal as well as the correlation between the multi-channel signals, but also complies with what clinical experts use in their visual judgement of EEG signals. To establish a metric facilitating the classification, the authors exploit the specific geometric properties, and develop, with the aid of fibre bundle theory, an appropriate metric in the Riemannian manifold described by the PSD matrices. To use this new metric effectively for the EEG signal classification, the authors further need to find a weighting for the PSD matrices so that the distances of similar features are minimised whereas those for dissimilar features are maximised. A closed form of this weighting matrix is obtained by solving an equivalent convex optimisation problem. The effectiveness of using these new metrics is examined by applying them to a collection of recorded EEG signals for sleep pattern classification based on the k-nearest neighbour decision algorithm with excellent outcome.
Cardiovascular Risk Prediction Models: A Scoping Review

Background: The prevention of cardiovascular disease is a public health priority as it is associated with increasing morbidity and mortality worldwide. Objective: A scoping review of the existing cardiovascular risk prediction models, to provide a basis for suggesting future research directions. Methods: PubMed and Scopus were searched from 2008 to 2018 for review papers investigating the formulation and effectiveness of risk prediction models for cardiovascular disease. Results: 229 references were screened of which 4 articles were included in the review, describing development of 436 prediction models. Most of the work reported was from USA and Europe. Conclusions: Availability of larger datasets from Electronic Health Records for more comprehensive and targeted risk prediction, and advancement in data analysis and modeling methods like machine learning to enable cohort directed approaches, has prompted researchers and clinicians to rethink risk modeling.
SafeBand: A Wearable Device for the Safety of Women in Bangladesh

Even in the twenty-first century, while people are debating over various reasons of assault, women from all walks of life are continuously being harassed and violated in the streets, public transportation and public spaces. The safety of women has become a progressively emphasized concern in Bangladesh also. Though a number of initiatives have been introduced by the government to keep the women safe, there is no or little Information and Communication Technology (ICT) based solution exist to ensure the woman's safety in the context of Bangladesh. Therefore, the objective of this paper is to design and develop a safety device called 'SafeBand' for women to fight against physical harassment. The 'SafeBand' system is comprised of a wearable band to be used by the victim, and two mobile applications to be used by the victim and by the police. Women can wear the device as a wristband or locket which will comprise of a button and a light. When the user (victim) presses the button, it identifies the location of the user through Global Positioning System (GPS) and sends a message incorporating the location to the nearest police station and previously saved contacts (number of relatives). An empirical study was carried out to evaluate the 'SafeBand' system, which was replicated with 15 participants. The study results show that the developed system is effective, efficient, useful and acceptable to all users.
Last Island: Exploring Transitions to Sustainable Futures through Play

A serious game was designed and developed with the goal of exploring potential sustainable futures and the transitions towards them. This computer-assisted board game, Last Island, which incorporates a system dynamics model into a board game's core mechanics, attempts to impart knowledge and understanding on sustainability and how an isolated society may transition to various futures to a non-expert community of players. To this end, this collaborative-competitive game utilizes the Miniworld model which simulates three variables important for the sustainability of a society: human population, economic production and the state of the environment. The resulting player interaction offers possibilities to collectively discover and validate potential scenarios for transitioning to a sustainable future, encouraging players to work together to balance the model output while also competing on individual objectives to be the individual winner of the game.
Characterization of HPC workloads on an ARMv8 based server under relaxed DRAM refresh and thermal stress

Improving energy efficiency of the memory subsystem becomes increasingly important for all digital systems due to the rapid growth of data. Many recent schemes have attempted to reduce the DRAM power by relaxing the refresh rate, which may negatively affect the DRAM reliability. To optimize the trade-offs between power and reliability, existing studies rely on experimental setups based on FPGAs and the use of few known data-patterns for exciting rare worst-case circuit reliability effects. However, by doing so, existing studies may be missing to capture the real DRAM behavior within a commodity server with a fully fledged OS. In this paper, we develop an experimental framework based on a state-of-the-art 64-bit ARM based server with Linux OS, in which we enabled the characterization of 72 DRAM chips under relaxed refresh period and various temperatures controlled by a unique thermal testbed. We evaluate the DRAM reliability running single-and multi-threaded HPC workloads on such a commodity server with a fully-fledged Linux OS and a typical multilevel memory hierarchy. In fact, our results show that the manifested Word-Error-Rate under relaxed refresh period varies among the workloads and can be different from the one estimated by the few known fixed data-patterns that were conventionally used in all existing studies. We also discover that the error rates incurred by the execution of the HPC workloads may vary within a program run. Finally, our study shows that the refresh period can be relaxed by 35X leading to 11.2 % power savings on average, while avoiding any system disruption, since the available error-correcting-codes were able to correct all incurred errors up to 60 °C.
Predicting academic performance: a systematic literature review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.
Impact of Network Fairness on the Performance of Parallel Systems

Execution time of parallel applications depends on the balanced execution and synchronization of all its processes. However, most interconnects exhibit significant throughput unfairness, introducing load unbalance. At high loads, such unfairness significantly degrades the performance of some nodes, and eventually the whole system. Different strategies have been developed to guarantee network fairness, fundamentally based on two different approaches: traffic prioritization in the network switches or injection throttling at the source. This work qualitatively and quantitatively compares two fairness mechanisms which are representative of these two types of explicit fairness strategies: Age arbitration, which prioritizes old packets in transit, and the SAT injection protocol, which throttles traffic to provide even access to all nodes. Multiple synthetic traffic patterns as well as parallel application loads at different transmission rates are considered in the evaluation. Our research identifies key limitations of each fairness mechanism: a realistic implementation of Age arbitration fails to achieve perfect fairness due to a delayed assignment of packet timestamps, while SAT may restrict maximum throughput when it is not necessary to do so. Both mechanisms do not hinder performance at low loads, and improve throughtput fairness at congested loads. With real applications, performance improvements up to 14.2% are observed, with SAT being particularly efficient at high loads.
Generating Log Requirements for Checking Conformance against Healthcare Standards using Workflow Modelling

The healthcare industry establishes policies and standards to improve the quality of health processes and their delivery. Auditing and monitoring can be used to measure the quality of healthcare and check compliance with the health policies. Appropriate logging mechanisms can be considered as a key component to manage compliance initiatives because they can be used to monitor and audit low performance, malfunctions and unauthorised user activities. However, existing Health Information Systems (HISs) inadequately implement logging mechanisms, making it crucial to be improved for policy compliance. Identification of sufficient logging requirements is one of the major challenges faced by HIS developers. We present a step-wise workflow modelling approach to help identify logging requirements that can facilitate proper auditing against established typical patient journeys and documented healthcare policies and standards. As a case study we develop a healthcare event log file containing sufficient log details which has been merged from different hosts in a HIS in order to gather necessary log details to audit for policy compliance.
Low Power analog Multiplier using MIFGMOS

A novel 4-quadrant analog multiplier using Floating Gate MOS (FGMOS) transistors operating in saturation region are implemented. Floating gate MOSFETs are being utilized in a number of new and existing analog applications. These devices are not only useful for designing memory elements but also we can implement circuit elements. The main advantage in FGMOS is that the drain current is proportional to square of the weighted sum of input signals. By using conventional transistors we obtain only few hundred mill volts range of the supply voltage and when we go for square law devices we obtain up to 50%. So in order to get 100% range of the supply voltage we go for FGMOS. This can be obtained by the control voltage applied at the gate of the FGMOS. This simulation is done with the SPICE tools.