Miracle-dz/UniFAR-FTUs huggingface dataset:
NUTRITIONAL CONSEQUENCES OF PARASITIC INFECTION DURING PREGNANCY

The effects of gastro-intestinal helminth infection on nutritional status of pregnant women was investigated in a semi-urban community, southeast, Nigeria. A total of 282 pregnant and 243 non-pregnant women were enlisted for this study. Stool samples were examined for intestinal helminths using formalin-ether sedimentation technique. Haemoglobin (Hb) and Packed Cell Volume (PCV) levels were evaluated in venous blood samples using Sahli’s and microhaematocrit methods respectively. Anthropometry-based nutritional assessment of subjects was done to determine their body mass index (BMI). Nutritional assessment showed that only 34.4% of the pregnant women had normal BMI. BMI was found to increase significantly (p<0.05) with trimester of pregnancy. Haematological assessment showed that the mean Hb (9.66+ 1.22) and PCV (29.68 + 3.24) of pregnant women were significantly lower than those of their non pregnant counterparts (p<0.05). The prevalence of anaemia among pregnant women was 58.9%. Anaemia was found to be highest in the second trimester with a prevalence rate of 63.6%. The percentage of infected anaemic pregnant women (82.6%) was significantly higher (p<0.05) than those of the uninfected ones (54.2%). least level of nutrition during the first trimester and the primagravidae parity and anaemia was more severe during the second trimester. Greater public health attention should be paid to the impact of gastro-intestinal infection in pregnancy.
Commissioning Community Well‐Being: Focus on Older People and Transport

In this article we consider community well‐being and new approaches to reinvigorating partnership working for older people's services. In particular, we focus on improving transport for older people. We draw on findings from a series of public consultations, group discussions and interviews with older people in 10 purposively selected localities in England. Although there was great diversity in the issues raised by older people on the subject of transport, both across and between the sites, we point to a number of core analytical themes which could assist commissioners in developing a citizens' framework designed to address this traditionally ‘wicked’ issue.
Software Architecture-Based Failure Prediction

Depending on the role of software in everyday life, the cost of a software failure can sometimes be unaffordable. During system execution, errors may occur in system components and failures may be manifested due to these errors. These errors differ with respect to their effects on system behavior and consequent failure manifestation manners. Predicting failures before their manifestation is important to assure system resilience. It helps avoid the cost of failures and enables systems to perform corrective actions prior to failure occurrences. However, effective runtime error detection and failure prediction techniques encounter a prohibitive challenge with respect to the control flow representation of large software systems with intricate control flow structures. In this thesis, we provide a technique for failure prediction from runtime errors of large software systems. Aiming to avoid the possible difficulties and inaccuracies of the existing Control Flow Graph (CFG) structures, we first propose a Connection Dependence Graph (CDG) for control flow representation of large software systems. We describe the CDG structure and explain how to derive it from program source code. Second, we utilize the proposed CDG to provide a connection-based signature approach for control flow error detection. We describe the monitor structure and present the error checking algorithm. Finally, we utilize the detected errors and erroneous state parameters to predict failure occurrences and modes during system runtime. We craft runtime signatures based on these errors and state parameters. Using system error and failure history, we determine a predictive
Effects of Computer Presentation Format on Learning in the Elderly

Younger and older adults studied two descriptions of the processes of measuring blood pressure and bees making honey under one of three experimental conditions: (a) text-only; (b) text plus narration; and (c) graphics plus narration. A control group without studying the materials was included in each age group. On verbal recall tests, the control group in both age groups performed more poorly than all the respective experimental groups, which were not different from each other. On transfer tests, the younger participants in the control group performed more poorly than all the experimental groups, which were not different from one another. For older participants, control group performed more poorly than text plus narration and graphics plus narration groups. Moreover, the text plus narration group performed better than the text-only group. These results revealed potential differences between the facilitations provided by verbal and non-verbal visual information for younger and older adults in learning novel information.
Advanced image formation and processing of partial synthetic aperture radar data

The authors propose an advanced synthetic aperture radar (SAR) image formation framework based on iterative inversion algorithms that approximately solve a regularised least squares problem. The framework provides improved image reconstructions, compared to the standard methods, in certain imaging scenarios, for example when the SAR data are under-sampled. Iterative algorithms also allow prior information to be used to solve additional problems such as the correction of unknown phase errors in the SAR data. However, for an iterative inversion framework to be feasible, fast algorithms for the generative model and its adjoint must be available. The authors demonstrate how fast, N2 log2 N complexity, (re/back)-projection algorithms can be used as accurate approximations for the generative model and its adjoint, without the limiting geometric approximations of other N2 log2 N methods, for example, the polar format algorithm. Experimental results demonstrate the effectiveness of their framework using publicly available SAR datasets.
BYOD in Hospitals-Security Issues and Mitigation Strategies

The demand for using personal devices in hospitals (BYOD) has increased rapidly over the years. However, BYOD also means that healthcare organisations are at great risk of leaking sensitive information assets like Personal Health Information (PHI) of patients, given that personal devices are outside the control of hospital IT management and may lack important security measures. Hence, the aim of this research is to develop a mitigation strategy which can cater to such security issues. A comprehensive literature review was conducted to identify BYOD security issues and mitigation solutions. This was followed by using two existing security frameworks, the BYOD security framework and People Policy Technology (PPT) model to shape a stepwise mitigation strategy. Technical, managerial and social issues were identified which include unsecure user behaviour by hospital employees, lack of security awareness, usability issues, legal requirements and lost devices. The mitigation strategy elucidates that while information and communication technologies allow better enforcement of security measures; policies and training provide the desired guidance to influence positive user behaviour among employees. The paper also discusses the need for a balance between usability and security in the success of BYOD in hospitals and hence provides systematic guidelines to curb BYOD security risks in hospitals.
Effectiveness of second Best Particle Information for Particle Swarm Optimization

Particle Swarm Optimization (PSO) represents the potential solutions of the optimization problem as the particles and then, the particles move in order to find the better solution. The particle positions are updated from the personal best and the global best particle positions which have been ever found. This research focuses on the use of the second personal best and the second global best particle positions in order to improve the search performance of the original PSO algorithm. In the present algorithm, the second global best or the second personal best particle position is randomly used for updating all particle positions. The algorithms are compared with the original PSO algorithm in five test functions. The results reveal that the use of the second global best and the second personal best particle positions can improve the search performance of the original PSO although the basic idea is simple.
An AI Race for Strategic Advantage: Rhetoric and Risks

The rhetoric of the race for strategic advantage is increasingly being used with regard to the development of artificial intelligence (AI), sometimes in a military context, but also more broadly. This rhetoric also reflects real shifts in strategy, as industry research groups compete for a limited pool of talented researchers, and nation states such as China announce ambitious goals for global leadership in AI. This paper assesses the potential risks of the AI race narrative and of an actual competitive race to develop AI, such as incentivising corner-cutting on safe-ty and governance, or increasing the risk of conflict. It explores the role of the research community in respond-ing to these risks. And it briefly explores alternative ways in which the rush to develop powerful AI could be framed so as instead to foster collaboration and respon-sible progress.
A 3D virtual Weft‐knitting Engineering learning system based on Unreal Engine 4

The traditional practice teaching mode of Weft‐knitting Engineering leads to unsatisfactory teaching outcomes due to the lack of hardware in laboratory and security issue in knitting mill. This paper presents a 3D virtual Weft‐knitting Engineering learning system based on Unreal Engine 4. The technological design of weft‐knitted fabrics including automatic design of jacquard pattern and the transformation of jacquard pattern, knitting notation, needle set‐out, and cams arrangement is realized through the establishment of mathematical model and algorithm in Unreal Engine 4. Corresponding cam configuration operation from the first person perspective is implemented in virtual practice operation part. The system provides learners a holistic cognition of weft‐knitting practice from machine structure, design of the weft‐knitted fabrics, virtual practice operation on the machine to 3D interactive Virtual Weft‐knitted fabrics. The experiment result shows that the virtual learning system developed in this study can improve the teaching effect of Weft‐knitting Engineering especially in operational skills, and the combination of traditional plus virtual learning system method is the better way to optimize the teaching effect. The learners’ feedback indicates that 3D virtual Weft‐knitting Engineering learning system is more convenient and immersive in which they feel like using real machine in real knitting mill. With the software combination of Unreal Engine 4 and Houdini, the bottom frame of the program offers great expansibility for more complex machine model and operation to the system in future. The design framework and discussions in this paper may be useful for developing virtual learning system in other areas.
Guest Editorial Special Section on Cloud Computing, Edge Computing, Internet of Things, and Big Data Analytics Applications for Healthcare Industry 4.0

INDUSTRY is the part of an economy that produces material goods and services that are highly mechanized and automatized. From the early stage of industrialization, technological advances have led to four paradigm shifts called “industrial revolutions. ” The first industrial revolution regarded mechanization based on water and steam power, the second one regarded the intensive use of electrical energy to maximize massive production based on assembly lines, the third one regarded the widespread digitalization thanks to computer-aided automation, and the fourth one (which is currently happening) regards instead the combination of future-oriented technologies including cyber-physical systems (CPS), cognitive computing, cloud computing, Internet of Things (IoT), big data analytics, and edge computing, among others [1], [2]. Industry 4.0 principles have found a great consensus and adoption in different industrial domains including healthcare. Healthcare Industry 4.0 allows increasing flexibility in production, speeding up both manufacturing and market processes, increasing both the product quality and productivity, and changing business models modifying the interaction with value chain, competitors, and clients. Healthcare Industry 4.0 requires investments and mind-set change for cross-industry collaboration, agreements on data ownership, security, legal issue solving, product registration standards, new machine-to-machine communication protocols, and employment/skills development. Furthermore, Healthcare Industry 4.0 is revolutionizing the market of health service provisioning to patients and clinical operators. In fact, all over the world, the number of investments in information and communication technology (ICT) for health and wellbeing (eHealth) is rapidly increasing. In fact, GLOBE NEWSWIRE has predicted that the global eHealth market is expected to reach 308 billion dollars by 2022 [3]. In this context, there is growing interest around the adoption of recent cutting-edge ICT technologies in Healthcare Industry 4.0, including cloud computing, IoT, big data analytics, and edge computing, that allow the delivery of various kinds of eHealth services and applications over telecommunication networks and the Internet remotely. Therefore, besides revolution-
Towards Opinions analysis method from social media for multidimensional analysis

Nowadays, social media are taking an essential part of our daily life in several domains. Especially, on opinion analysis area with decisional systems where it helps companies to improve their products from reviews posted on social media such as Facebook, Twitter, etc. In this respect, there is an interest for opinion analysis or opinion mining expressed in social media for business decision support. In this research paper, we propose a new method of opinion analysis based on machine learning that determines the polarity of users'comments shared on different social media. The latter will be integrated in the ETL (Extract, Transform and Load) process to analyze the users' opinions. The proposed method is based on the n-grams technique to construct a semi-automatic dictionary for positive and negative keywords that is used in the learning phase to establish the prediction model. In addition, we propose a new features vector specific for social media for classifying the comments as positive, negative or neutral. The evaluation results performed on the both publicly data sets Stanford Twitter Sentiment (STS) and Sanders dataset showed a high accuracy level.
Understanding Diabetes through Watch Based Interactive Play

Play based systems, such as computer games, have proven effective as tools to enhance our human understanding, skills and capabilities in a number of real world activities. The active pursuit of gamification and applied games for tasks from education to design, training and simulation continues to grow. The field of healthcare is one key area offering significant potential. Raising the question of whether play based assistive tools, as a means to create greater understanding of serious medical conditions, and the health and lifestyle impacts they cause, can be effective. This research involved both the review of play based systems that are dedicated to assisting with diabetes, as well as the development of a new watch based mobile App designed specifically to assist players (both diabetic as well as broader community) in understanding the complex nature of managing diabetes. Diabetes is listed as the sixth leading cause of death worldwide and its improved management is listed as a priority by the world health organization. The results from the randomized control trial of the new watch based tool showed that participants who used the play based watch App, demonstrated a statistically significant improvement of their understanding of the condition and its management. This highlights the potential of play based digital tools to bring greater understanding of complex medical conditions to patients, care givers and the broader population.
Arabic Person Names Recognition by using a Rule based Approach

Name Entity Recognition is very important task in many natural language processing applications such as; Machine Translation, Question Answering, Information Extraction, Text Summarization, Semantic Applications and Word Sense Disambiguation. Rule-based approach is one of the techniques that are used for named entity recognition to identify the named entities such as a person names, location names and organization names. The recent rule-based methods have been applied to recognize the person names in political domain. They ignored the recognition of other named entity types such as locations and organizations. We have used the rule based approach for recognizing the named entity type (person names) for Arabic. We have developed four rules for identifying the person names depending on the position of name. We have used an in-house Arabic corpus collected from newspaper achieves. The evaluation method that compares the results of the system with the manually annotated text has been applied in order to compute precision, recall and f-measure. In the experiment of this study, the average f-measure for recognizing person names are (92.66, 92.04 and 90.43%) in sport, economic and politic domain respectively. The experimental results showed that our rule-based method achieved the highest f-measure values in sport domain comparing with political and economic domains.
A Systematic Review of the Definition and Measurement of Engagement in Serious Games

A systematic review was designed to address the question of "What is engagement and how has the term been used, defined and measured in the context of serious games? ". The goal of the review was to collect, evaluate, and analyse literature related to the definition and measurement of engagement in serious games published between in 1970 to 2015 across a broad range of disciplines. A total of 1390 papers were initially identified from the search criteria. These were reduced to 107 papers that directly assessed engagement in a serious game. These selected papers were then analysed to examine the use of the term 'engagement' in the serious games, the genres of the games studied, the various definitions of engagement and the methods used to measure different aspects of engagement. Three distinct types of engagement, related to behaviour, affect and cognition are found in the studies, along with a broad range of evaluation methods including interviews, questionnaires, physiological approaches, in game metrics, and time and performance on task.
Comparing Pixel N-grams and Bag of Visual Word Features for the Classification of Diabetic Retinopathy

The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.
An international investigation into student concerns regarding transition into higher education computing

The experience of transitioning into and starting higher education is very much an individual one, with some applicants viewing the prospect of higher education as an unknown entity. For those who are first in their family or community to consider higher education, it can seem to be an "alien environment". This is just one of the issues that lead to applicants experiencing levels of concern when considering a transition into higher education. This international working group aims to answer the following research question: "What are the concerns that computing students have with regards to their transition into higher education? " A survey was administered and the results evaluated.
EXA2PRO programming environment: architecture and applications

The EXA2PRO programming environment will integrate a set of tools and methodologies that will allow to systematically address many exascale computing challenges, including performance, performance portability, programmability, abstraction and reusability, fault tolerance and technical debt. The EXA2PRO tool-chain will enable the efficient deployment of applications in exascale computing systems, by integrating high-level software abstractions that offer performance portability and efficient exploitation of exascale systems' heterogeneity, tools for efficient memory management, optimizations based on trade-offs between various metrics and fault-tolerance support. Hence, by addressing various aspects of productivity challenges, EXA2PRO is expected to have significant impact in the transition to exascale computing, as well as impact from the perspective of applications. The evaluation will be based on 4 applications from 4 different domains that will be deployed in JUELICH supercomputing center. The EXA2PRO will generate exploitable results in the form of a tool-chain that support diverse exascale heterogeneous supercomputing centers and concrete improvements in various exascale computing challenges.